From a22e475a7b98492a1e653f440587ad52bf0b8701 Mon Sep 17 00:00:00 2001 From: theproteinbot Date: Sat, 28 Feb 2026 14:03:51 -0500 Subject: [PATCH 01/34] Fix prediction pipeline robustness and checkpoint compatibility --- catpred/data/cache_utils.py | 9 ++- catpred/data/esm_utils.py | 7 +- catpred/data/utils.py | 76 ++++++++++++++++++---- catpred/models/model.py | 18 ++++-- catpred/utils.py | 20 ++++-- demo_run.py | 122 +++++++++++++++++++++++++++-------- predict.sh | 12 ++-- scripts/create_pdbrecords.py | 90 ++++++++++++++++++-------- 8 files changed, 271 insertions(+), 83 deletions(-) diff --git a/catpred/data/cache_utils.py b/catpred/data/cache_utils.py index 7ee9ede..90cac3a 100644 --- a/catpred/data/cache_utils.py +++ b/catpred/data/cache_utils.py @@ -27,6 +27,13 @@ def md5_hash_fn(s): encoded = s.encode('utf-8') return hashlib.md5(encoded).hexdigest() + +def _torch_load_compat(path): + try: + return torch.load(path, weights_only=False) + except TypeError: + return torch.load(path) + # run once function GLOBAL_RUN_RECORDS = dict() @@ -92,7 +99,7 @@ def inner(t, *args, __cache_key = None, **kwargs): if entry_path.exists(): log(f'cache hit: fetching {t} from {str(entry_path)}') - return torch.load(str(entry_path)) + return _torch_load_compat(str(entry_path)) out = fn(t, *args, **kwargs) diff --git a/catpred/data/esm_utils.py b/catpred/data/esm_utils.py index f45bd65..690c106 100644 --- a/catpred/data/esm_utils.py +++ b/catpred/data/esm_utils.py @@ -157,8 +157,11 @@ def get_single_esm_repr(protein_str): def get_esm_repr(proteins, name, device): if isinstance(proteins, torch.Tensor): proteins = tensor_to_aa_str(proteins) - - get_protein_repr_fn = cache_fn(get_single_esm_repr, path = 'esm/proteins', name = name) + + # Cache by sequence content to avoid collisions when different proteins + # are accidentally given the same pdb/name identifier. + _ = name + get_protein_repr_fn = cache_fn(get_single_esm_repr, path = 'esm/proteins') return calc_protein_representations_with_subunits([proteins], get_protein_repr_fn, device = device) diff --git a/catpred/data/utils.py b/catpred/data/utils.py index a2baaee..341791a 100644 --- a/catpred/data/utils.py +++ b/catpred/data/utils.py @@ -27,6 +27,33 @@ # Increase maximum size of field in the csv processing for the current architecture csv.field_size_limit(int(ctypes.c_ulong(-1).value // 2)) + +def _load_protein_records(protein_records_path: str): + """ + Load protein records JSON from gzip, plain JSON, or accidentally double-gzipped files. + """ + try: + with gzip.open(protein_records_path, "rt", encoding="utf-8") as handle: + return json.load(handle) + except Exception: + try: + with gzip.open(protein_records_path, "rb") as handle: + payload = handle.read() + if payload[:2] == b"\x1f\x8b": + payload = gzip.decompress(payload) + return json.loads(payload.decode("utf-8")) + except Exception: + pass + + try: + with open(protein_records_path, "rt", encoding="utf-8") as handle: + return json.load(handle) + except Exception as plain_err: + raise ValueError( + f'Failed to load protein records from "{protein_records_path}". ' + "Expected a JSON mapping in .json or .json.gz format." + ) from plain_err + def get_header(path: str) -> List[str]: """ Returns the header of a data CSV file. @@ -400,8 +427,7 @@ def get_data(path: str, if protein_records_path is None: protein_records = None else: - with gzip.open(protein_records_path, "rt", encoding="utf-8") as f: - protein_records = json.load(f) + protein_records = _load_protein_records(protein_records_path) if args is not None: # Prefer explicit function arguments but default to args if not provided @@ -498,16 +524,42 @@ def get_data(path: str, if args.smoke_test: if smoke_test_counter>100: break smiles = [row[c] for c in smiles_columns] - pdbpath = row['pdbpath'] - pdbname = pdbpath.split("/")[-1] - protein_record = protein_records[pdbname] - if not protein_records is None: - sequence_features, _ = sequence_feat_getter(protein_record['seq'], - name = pdbname, - device = 'cpu') - protein_record['esm2_feats'] = sequence_features[0] #batch dim - else: - protein_record = None + protein_record = None + + if protein_records is not None: + if 'pdbpath' not in row: + raise ValueError( + f'Missing required "pdbpath" column in {path} at row {i + 2}.' + ) + + pdbpath = (row['pdbpath'] or '').strip() + if pdbpath == '': + raise ValueError( + f'Empty pdbpath found in {path} at row {i + 2}.' + ) + + pdbname = os.path.basename(pdbpath) + protein_record = protein_records.get(pdbname) or protein_records.get(pdbpath) + if protein_record is None: + raise KeyError( + f'No protein record found for pdbpath "{pdbpath}" (basename "{pdbname}") ' + f'in row {i + 2}. Ensure create_pdbrecords.py was run on the same input file.' + ) + + row_sequence = row.get('sequence') + if row_sequence is not None and protein_record.get('seq') != row_sequence: + raise ValueError( + f'Sequence mismatch for pdbpath "{pdbpath}" in row {i + 2}. ' + 'This usually means multiple sequences share the same pdbpath identifier.' + ) + + if 'esm2_feats' not in protein_record: + sequence_features, _ = sequence_feat_getter( + protein_record['seq'], + name=pdbname, + device='cpu' + ) + protein_record['esm2_feats'] = sequence_features[0] # batch dim targets, atom_targets, bond_targets = [], [], [] for column in target_columns: diff --git a/catpred/models/model.py b/catpred/models/model.py index 58dabe6..d9e8a88 100644 --- a/catpred/models/model.py +++ b/catpred/models/model.py @@ -24,6 +24,14 @@ from torch import nn from torch import einsum + +def _torch_load_compat(path): + try: + return torch.load(path, weights_only=False) + except TypeError: + return torch.load(path) + + def exists(val): return val is not None @@ -131,7 +139,7 @@ def create_protein_model(self, args: TrainArgs) -> None: self.seq_embedder = nn.Embedding(21, args.seq_embed_dim, padding_idx=20) #last index is for padding if self.args.add_pretrained_egnn_feats: - self.pretrained_egnn_feats_dict = torch.load(self.args.pretrained_egnn_feats_path) + self.pretrained_egnn_feats_dict = _torch_load_compat(self.args.pretrained_egnn_feats_path) x = list(self.pretrained_egnn_feats_dict.values()) self.pretrained_egnn_feats_avg = torch.stack(x).mean(dim=0) @@ -417,8 +425,10 @@ def seq_to_tensor(seq): esm_feature_arr = [each['esm2_feats'] for each in protein_records] esm_feature_arr = pad_sequence(esm_feature_arr, batch_first=True).to(self.device) - if seq_arr.shape[1]!=esm_feature_arr.shape[1]: - seq_arr = seq_arr[:,:esm_feature_arr.shape[1]:] + if seq_arr.shape[1] != esm_feature_arr.shape[1]: + common_len = min(seq_arr.shape[1], esm_feature_arr.shape[1]) + seq_arr = seq_arr[:, :common_len] + esm_feature_arr = esm_feature_arr[:, :common_len] # project sequence to embed dim seq_outs = self.seq_embedder(seq_arr) @@ -522,4 +532,4 @@ def seq_to_tensor(seq): else: output = nn.functional.softplus(output) + 1 - return output \ No newline at end of file + return output diff --git a/catpred/utils.py b/catpred/utils.py index 7aaa9cf..ecde744 100644 --- a/catpred/utils.py +++ b/catpred/utils.py @@ -25,6 +25,18 @@ from catpred.models.ffn import MultiReadout +def _torch_load_compat(path, map_location=None): + """ + Explicitly disable weights-only loading for backward compatibility with + CatPred checkpoints that store non-tensor objects (e.g., argparse.Namespace). + """ + try: + return torch.load(path, map_location=map_location, weights_only=False) + except TypeError: + # For older torch versions that do not support weights_only. + return torch.load(path, map_location=map_location) + + def makedirs(path: str, isfile: bool = False) -> None: """ Creates a directory given a path to either a directory or file. @@ -110,7 +122,7 @@ def load_checkpoint( debug = info = print # Load model and args - state = torch.load(path, map_location=lambda storage, loc: storage) + state = _torch_load_compat(path, map_location=lambda storage, loc: storage) args = TrainArgs() args.from_dict(vars(state["args"]), skip_unsettable=True) if not pretrained_egnn_feats_path is None: @@ -221,7 +233,7 @@ def load_frzn_model( """ debug = logger.debug if logger is not None else print - loaded_mpnn_model = torch.load(path, map_location=lambda storage, loc: storage) + loaded_mpnn_model = _torch_load_compat(path, map_location=lambda storage, loc: storage) loaded_state_dict = loaded_mpnn_model["state_dict"] loaded_args = loaded_mpnn_model["args"] @@ -443,7 +455,7 @@ def load_scalers( :return: A tuple with the data :class:`~catpred.data.scaler.StandardScaler` and features :class:`~catpred.data.scaler.StandardScaler`. """ - state = torch.load(path, map_location=lambda storage, loc: storage) + state = _torch_load_compat(path, map_location=lambda storage, loc: storage) if state["data_scaler"] is not None: scaler = StandardScaler(state["data_scaler"]["means"], state["data_scaler"]["stds"]) @@ -498,7 +510,7 @@ def load_args(path: str) -> TrainArgs: """ args = TrainArgs() args.from_dict( - vars(torch.load(path, map_location=lambda storage, loc: storage)["args"]), + vars(_torch_load_compat(path, map_location=lambda storage, loc: storage)["args"]), skip_unsettable=True, ) diff --git a/demo_run.py b/demo_run.py index 8a78739..5b77e38 100644 --- a/demo_run.py +++ b/demo_run.py @@ -13,6 +13,7 @@ import time import os +import subprocess import pandas as pd import numpy as np from IPython.display import Image, display @@ -20,8 +21,30 @@ from IPython.display import display, Latex, Math import argparse -def create_csv_sh(parameter, input_file_path, checkpoint_dir): + +def prepare_prediction_inputs(parameter, input_file_path): df = pd.read_csv(input_file_path) + required_columns = {"SMILES", "sequence", "pdbpath"} + missing_columns = required_columns.difference(df.columns) + if missing_columns: + print( + f'Missing required column(s) in input file: {", ".join(sorted(missing_columns))}.' + ) + return None + + conflicting_pdbpaths = ( + df.groupby("pdbpath")["sequence"] + .nunique(dropna=False) + .loc[lambda value: value > 1] + ) + if len(conflicting_pdbpaths) > 0: + preview = ", ".join(conflicting_pdbpaths.index.astype(str).tolist()[:5]) + print( + "Found pdbpath values mapped to multiple sequences. " + f"Each unique sequence must have a unique pdbpath. Examples: {preview}" + ) + return None + smiles_list = df.SMILES seq_list = df.sequence smiles_list_new = [] @@ -45,21 +68,17 @@ def create_csv_sh(parameter, input_file_path, checkpoint_dir): print('Correct your input! Exiting..') return None - input_file_new_path = f'{input_file_path[:-4]}_input.csv' + input_file_base, _ = os.path.splitext(input_file_path) + input_file_new_path = f'{input_file_base}_input.csv' df['SMILES'] = smiles_list_new - df.to_csv(input_file_new_path) - - with open('predict.sh', 'w') as f: - f.write(f''' - TEST_FILE_PREFIX={input_file_new_path[:-4]} - RECORDS_FILE=${{TEST_FILE_PREFIX}}.json - CHECKPOINT_DIR={checkpoint_dir} - - python ./scripts/create_pdbrecords.py --data_file ${{TEST_FILE_PREFIX}}.csv --out_file ${{RECORDS_FILE}} - python predict.py --test_path ${{TEST_FILE_PREFIX}}.csv --preds_path ${{TEST_FILE_PREFIX}}_output.csv --checkpoint_dir $CHECKPOINT_DIR --uncertainty_method mve --smiles_column SMILES --individual_ensemble_predictions --protein_records_path $RECORDS_FILE - ''') + df.to_csv(input_file_new_path, index=False) - return input_file_new_path[:-4]+'_output.csv' + test_file_prefix = input_file_new_path[:-4] + return { + "input_csv": input_file_new_path, + "records_file": f"{test_file_prefix}.json.gz", + "output_csv": f"{test_file_prefix}_output.csv", + } def get_predictions(parameter, outfile): """ @@ -86,6 +105,12 @@ def get_predictions(parameter, outfile): unc_col = f'{target_col}_mve_uncal_var' + missing_cols = [col for col in [target_col, unc_col] if col not in df.columns] + if missing_cols: + raise ValueError( + f'Prediction output is missing required column(s): {", ".join(missing_cols)}' + ) + for _, row in df.iterrows(): model_cols = [col for col in row.index if col.startswith(target_col) and 'model_' in col] @@ -93,12 +118,15 @@ def get_predictions(parameter, outfile): prediction = row[target_col] prediction_linear = np.power(10, prediction) - model_outs = np.array([row[col] for col in model_cols]) - epi_unc = np.var(model_outs) - alea_unc = unc - epi_unc + if model_cols: + model_outs = np.array([row[col] for col in model_cols]) + epi_unc = np.var(model_outs) + else: + epi_unc = 0.0 + alea_unc = max(unc - epi_unc, 0.0) epi_unc = np.sqrt(epi_unc) alea_unc = np.sqrt(alea_unc) - unc = np.sqrt(unc) + unc = np.sqrt(max(unc, 0.0)) pred_col.append(prediction_linear) pred_logcol.append(prediction) @@ -115,22 +143,62 @@ def get_predictions(parameter, outfile): return df def main(args): - print(os.getcwd()) - - outfile = create_csv_sh(args.parameter, args.input_file, args.checkpoint_dir) - if outfile is None: + run_paths = prepare_prediction_inputs(args.parameter, args.input_file) + if run_paths is None: return + outfile = run_paths["output_csv"] print('Predicting.. This will take a while..') - if args.use_gpu: - os.system("export PROTEIN_EMBED_USE_CPU=0;./predict.sh") - else: - os.system("export PROTEIN_EMBED_USE_CPU=1;./predict.sh") + env = os.environ.copy() + env["PROTEIN_EMBED_USE_CPU"] = "0" if args.use_gpu else "1" + + create_records_cmd = [ + "python", + "./scripts/create_pdbrecords.py", + "--data_file", + run_paths["input_csv"], + "--out_file", + run_paths["records_file"], + ] + predict_cmd = [ + "python", + "predict.py", + "--test_path", + run_paths["input_csv"], + "--preds_path", + outfile, + "--checkpoint_dir", + args.checkpoint_dir, + "--uncertainty_method", + "mve", + "--smiles_column", + "SMILES", + "--individual_ensemble_predictions", + "--protein_records_path", + run_paths["records_file"], + ] + + create_records_result = subprocess.run(create_records_cmd, env=env) + if create_records_result.returncode != 0: + print( + f"Protein record generation failed with exit code {create_records_result.returncode}." + ) + return + + predict_result = subprocess.run(predict_cmd, env=env) + if predict_result.returncode != 0: + print(f"Prediction command failed with exit code {predict_result.returncode}.") + return + + if not os.path.exists(outfile): + print(f'Prediction output file was not generated: {outfile}') + return output_final = get_predictions(args.parameter, outfile) filename = outfile.split('/')[-1] - output_final.to_csv(f'../results/{filename}') + os.makedirs('../results', exist_ok=True) + output_final.to_csv(f'../results/{filename}', index=False) print('Output saved to results/', filename) if __name__ == "__main__": diff --git a/predict.sh b/predict.sh index 2caad26..93d8036 100644 --- a/predict.sh +++ b/predict.sh @@ -1,7 +1,9 @@ -TEST_FILE_PREFIX=./demo/batch_kcat_input -RECORDS_FILE=${TEST_FILE_PREFIX}.json -CHECKPOINT_DIR=../data/pretrained/production/kcat/ +#!/usr/bin/env bash +set -euo pipefail -python ./scripts/create_pdbrecords.py --data_file ${TEST_FILE_PREFIX}.csv --out_file ${RECORDS_FILE}.gz +TEST_FILE_PREFIX="${TEST_FILE_PREFIX:-./demo/batch_kcat_input}" +RECORDS_FILE="${RECORDS_FILE:-${TEST_FILE_PREFIX}.json.gz}" +CHECKPOINT_DIR="${CHECKPOINT_DIR:-../data/pretrained/production/kcat}" -python predict.py --test_path ${TEST_FILE_PREFIX}.csv --preds_path ${TEST_FILE_PREFIX}_output.csv --checkpoint_dir $CHECKPOINT_DIR --uncertainty_method mve --smiles_column SMILES --individual_ensemble_predictions --protein_records_path ${RECORDS_FILE}.gz +python ./scripts/create_pdbrecords.py --data_file "${TEST_FILE_PREFIX}.csv" --out_file "${RECORDS_FILE}" +python predict.py --test_path "${TEST_FILE_PREFIX}.csv" --preds_path "${TEST_FILE_PREFIX}_output.csv" --checkpoint_dir "${CHECKPOINT_DIR}" --uncertainty_method mve --smiles_column SMILES --individual_ensemble_predictions --protein_records_path "${RECORDS_FILE}" diff --git a/scripts/create_pdbrecords.py b/scripts/create_pdbrecords.py index 0360b81..54c2736 100644 --- a/scripts/create_pdbrecords.py +++ b/scripts/create_pdbrecords.py @@ -1,42 +1,76 @@ -import pandas as pd import argparse +import gzip import json -def parse_args(): - """Prepare argument parser. +import os - Args: +import pandas as pd - Return: - """ +def parse_args(): parser = argparse.ArgumentParser( - description="Generate json records for test file with only sequences" + description="Generate gzipped JSON protein records from an input CSV file." ) + parser.add_argument("--data_file", required=True, help="Path to CSV file") parser.add_argument( - "--data_file", - help="Path to csv file", + "--out_file", required=True, + help="Output path for gzipped JSON protein records", ) - - parser.add_argument("--out_file", help="output file for json records") + return parser.parse_args() - args = parser.parse_args() - return args -args = parse_args() -df = pd.read_csv(args.data_file) -assert('pdbpath' in df.columns) -assert('sequence' in df.columns) +def _as_clean_str(value): + return value.strip() if isinstance(value, str) else value -import json -dic_full = {} -for ind, row in df.iterrows(): - dic = {} - dic['name'] = row.pdbpath - dic['seq'] = row.sequence - dic_full[row.pdbpath] = dic -import gzip -# Writing the dictionary to a gzipped file -with gzip.open(args.out_file, 'wb') as f: - f.write(json.dumps(dic_full).encode('utf-8')) +def build_records(df: pd.DataFrame, data_file: str) -> dict: + required = {"pdbpath", "sequence"} + missing = required.difference(df.columns) + if missing: + raise ValueError( + f'Missing required column(s) in "{data_file}": {", ".join(sorted(missing))}' + ) + + records = {} + conflicts = [] + + for index, row in df.iterrows(): + row_num = index + 2 # account for header row + pdbpath = _as_clean_str(row["pdbpath"]) + sequence = _as_clean_str(row["sequence"]) + + if not pdbpath: + raise ValueError(f'Empty "pdbpath" in row {row_num} of "{data_file}".') + if not sequence: + raise ValueError(f'Empty "sequence" in row {row_num} of "{data_file}".') + + key = os.path.basename(pdbpath) + existing = records.get(key) + if existing is not None and existing["seq"] != sequence: + conflicts.append((row_num, key)) + continue + + records[key] = {"name": key, "seq": sequence} + + if conflicts: + preview = ", ".join( + [f'{key} (row {row_num})' for row_num, key in conflicts[:5]] + ) + raise ValueError( + "Found pdbpath basenames reused for different sequences. " + f"Each unique sequence must have a unique pdbpath. Examples: {preview}" + ) + + return records + + +def main(): + args = parse_args() + df = pd.read_csv(args.data_file) + records = build_records(df, args.data_file) + with gzip.open(args.out_file, "wt", encoding="utf-8") as handle: + json.dump(records, handle) + + +if __name__ == "__main__": + main() From 00416bb80c0e1f23ef8968ce1b0eaa8d7efbeb25 Mon Sep 17 00:00:00 2001 From: theproteinbot Date: Sat, 28 Feb 2026 14:04:06 -0500 Subject: [PATCH 02/34] Update setup and usage docs for resilient local workflows --- README.md | 58 ++++++++++++++++++++++++++++++++++++++++++++++++- environment.yml | 1 - 2 files changed, 57 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index 1933ecd..2a92ed8 100644 --- a/README.md +++ b/README.md @@ -65,7 +65,8 @@ Then proceed to either option below to complete the installation. If installing ```bash mkdir catpred_pipeline catpred_pipeline/results cd catpred_pipeline -wget https://catpred.s3.us-east-1.amazonaws.com/capsule_data_update.tar.gz +wget -c --tries=5 --timeout=30 https://catpred.s3.us-east-1.amazonaws.com/capsule_data_update.tar.gz || \ +wget -c --tries=5 --timeout=30 https://catpred.s3.amazonaws.com/capsule_data_update.tar.gz tar -xzf capsule_data_update.tar.gz git clone https://github.com/maranasgroup/catpred.git cd catpred @@ -74,10 +75,65 @@ conda activate catpred pip install -e . ```` +`stride` is Linux-only and optional for the default demos. If needed for your workflow, install it separately on Linux: + +```bash +conda install -c kimlab stride +``` + ### ๐Ÿ”ฎ Prediction The Jupyter Notebook `batch_demo.ipynb` and the Python script `demo_run.py` show the usage of pre-trained models for prediction. +Input CSV requirements for `demo_run.py` and batch prediction: +- Required columns: `SMILES`, `sequence`, `pdbpath`. +- `pdbpath` must be unique per unique sequence. Reusing the same `pdbpath` for different sequences can produce incorrect cached embeddings. +- Reusing the same `pdbpath` for repeated measurements of the same sequence is supported. + +The helper script used to build protein records is: + +```bash +python ./scripts/create_pdbrecords.py --data_file --out_file +``` + +CatPred currently expects one sequence per row. Multi-protein complexes (e.g., heteromers/homodimers) are not explicitly modeled as separate chains in the default prediction workflow. + +For released benchmark datasets, the number of entries with 3D structure can be smaller than the total sequence/substrate pairs; 3D-derived artifacts are available only for the subset with valid structure mapping. + +### ๐Ÿงช Fine-Tuning On Custom Data + +You can fine-tune CatPred on your own regression targets using `train.py`. + +1. Prepare train/val/test CSVs with at least: +- `SMILES` +- `sequence` +- `pdbpath` (unique per unique sequence) +- one numeric target column (for example: `log10kcat_max`) + +2. Build a protein-records file that covers all `pdbpath` values in your splits: + +```bash +python ./scripts/create_pdbrecords.py --data_file --out_file +``` + +3. Train: + +```bash +python train.py \ + --protein_records_path \ + --data_path \ + --separate_val_path \ + --separate_test_path \ + --dataset_type regression \ + --smiles_columns SMILES \ + --target_columns \ + --add_esm_feats \ + --loss_function mve \ + --save_dir +``` + +For working end-to-end examples, see the training commands in scripts such as `scripts/reproduce_figS10_catpred.sh`. + ### ๐Ÿ”„ Reproducing Publication Results We provide three separate ways for reproducing the results of the publication. diff --git a/environment.yml b/environment.yml index 082e55c..890460c 100644 --- a/environment.yml +++ b/environment.yml @@ -20,7 +20,6 @@ dependencies: - faiss-cpu - pytorch-scatter - pyg - - kimlab::stride - pip: - ipdb - fair-esm From 56c15d44f781ec40584fd00d13deed1dc9f1ff2c Mon Sep 17 00:00:00 2001 From: theproteinbot Date: Sat, 28 Feb 2026 14:20:00 -0500 Subject: [PATCH 03/34] Phase 0: harden inference runtime and packaging entrypoints --- .gitignore | 12 ++ catpred/data/esm_utils.py | 57 +++++---- catpred/data/utils.py | 1 - catpred/features/__init__.py | 6 +- catpred/features/featurization.py | 28 +++++ catpred/models/model.py | 26 ++-- catpred/train/make_predictions.py | 196 +++++++++++++++--------------- catpred/train/run_training.py | 2 +- catpred/utils.py | 5 +- demo_run.py | 8 +- setup.cfg | 5 - setup.py | 1 + 12 files changed, 194 insertions(+), 153 deletions(-) diff --git a/.gitignore b/.gitignore index b155533..a3b307a 100644 --- a/.gitignore +++ b/.gitignore @@ -32,3 +32,15 @@ catpred/data/__pycache__/esm_utils.cpython-312.pyc catpred/data/__pycache__/data.cpython-312.pyc catpred/data/__pycache__/cache_utils.cpython-312.pyc catpred/data/__pycache__/__init__.cpython-312.pyc + +# Generic Python/OS artifacts +__pycache__/ +*.pyo +*.pyd +.DS_Store +*.egg-info/ +.ipynb_checkpoints/ + +# Local validation artifacts +.e2e-assets/ +.e2e-tests/ diff --git a/catpred/data/esm_utils.py b/catpred/data/esm_utils.py index 690c106..359613a 100644 --- a/catpred/data/esm_utils.py +++ b/catpred/data/esm_utils.py @@ -1,12 +1,9 @@ import torch import os -import re -from pathlib import Path from functools import partial import esm from torch.nn.utils.rnn import pad_sequence -from .cache_utils import cache_fn, run_once, md5_hash_fn -# import esm.inverse_folding as esm_if +from .cache_utils import cache_fn, run_once def exists(val): return val is not None @@ -20,7 +17,12 @@ def to_device(t, *, device): def cast_tuple(t): return (t,) if not isinstance(t, tuple) else t -PROTEIN_EMBED_USE_CPU = os.getenv('PROTEIN_EMBED_USE_CPU', None) is not None +def _env_flag(name: str, default: str = "0") -> bool: + raw = os.getenv(name, default) + return str(raw).strip().lower() in {"1", "true", "yes", "on"} + + +PROTEIN_EMBED_USE_CPU = _env_flag("PROTEIN_EMBED_USE_CPU", "0") if PROTEIN_EMBED_USE_CPU: print('calculating protein embed only on cpu') @@ -32,9 +34,6 @@ def cast_tuple(t): 'tokenizer': None } -# general helper functions -import ipdb - def calc_protein_representations_with_subunits(proteins, get_repr_fn, *, device): representations = [] for subunits in proteins: @@ -102,8 +101,6 @@ def calc_protein_representations_with_subunits(proteins, get_repr_fn, *, device) } AA_STR_TO_INT_MAP = {v:k for k,v in INT_TO_AA_STR_MAP.items()} -import ipdb - def tensor_to_aa_str(t): str_seqs = [] #ipdb.set_trace() @@ -116,6 +113,7 @@ def tensor_to_aa_str(t): def init_esm(): model, alphabet = esm.pretrained.esm2_t33_650M_UR50D() batch_converter = alphabet.get_batch_converter() + model.eval() if not PROTEIN_EMBED_USE_CPU: model = model.cuda() @@ -124,6 +122,8 @@ def init_esm(): @run_once('init_esm_if') def init_esm_if(): + import esm.inverse_folding as esm_if + model, alphabet = esm.pretrained.esm_if1_gvp4_t16_142M_UR50() batch_converter = esm_if.util.CoordBatchConverter(alphabet, 2048) @@ -145,7 +145,7 @@ def get_single_esm_repr(protein_str): batch_tokens = batch_tokens[:, :ESM_MAX_LENGTH] if not PROTEIN_EMBED_USE_CPU: - batch_tokens = batch_tokens.cuda() + batch_tokens = batch_tokens.to(next(model.parameters()).device) with torch.no_grad(): results = model(batch_tokens, repr_layers=[33]) @@ -161,20 +161,30 @@ def get_esm_repr(proteins, name, device): # Cache by sequence content to avoid collisions when different proteins # are accidentally given the same pdb/name identifier. _ = name - get_protein_repr_fn = cache_fn(get_single_esm_repr, path = 'esm/proteins') + get_protein_repr_fn = cache_fn(get_single_esm_repr, path='esm/proteins') + + return calc_protein_representations_with_subunits([proteins], get_protein_repr_fn, device=device) - return calc_protein_representations_with_subunits([proteins], get_protein_repr_fn, device = device) +def get_coords(pdbpath: str, chain_id: str = "A"): + try: + import esm.inverse_folding as esm_if + except ImportError as exc: + raise ImportError( + "ESM inverse folding is not installed. Install optional esm inverse-folding dependencies " + "to use get_coords()." + ) from exc -def get_coords(pdbpath): - #init_esm_if() - #model, batch_converter = GLOBAL_VARIABLES['esmif_model'] - addpath = '/home/ubuntu/CatPred-DB/CatPred-DB/' - coords = esm_if.util.load_coords(addpath+pdbpath, 'A') - return coords + if not os.path.exists(pdbpath): + raise FileNotFoundError(f'PDB file not found: "{pdbpath}"') + + return esm_if.util.load_coords(pdbpath, chain_id) def get_esm_tokens(protein_str, device): if isinstance(protein_str, torch.Tensor): - proteins = tensor_to_aa_str(proteins) + protein_str = tensor_to_aa_str(protein_str) + if len(protein_str) != 1: + raise ValueError("get_esm_tokens expects a single protein sequence.") + protein_str = protein_str[0] init_esm() model, batch_converter = GLOBAL_VARIABLES['model'] @@ -187,8 +197,8 @@ def get_esm_tokens(protein_str, device): batch_tokens = batch_tokens[:, :ESM_MAX_LENGTH] - if device!='cpu': - batch_tokens = batch_tokens.cuda() + if device != 'cpu': + batch_tokens = batch_tokens.to(device) return batch_tokens @@ -204,7 +214,8 @@ def get_esm_tokens(protein_str, device): def get_protein_embedder(name): allowed_protein_embedders = list(PROTEIN_REPR_CONFIG.keys()) - assert name in allowed_protein_embedders, f"must be one of {', '.join(allowed_protein_embedders)}" + if name not in allowed_protein_embedders: + raise ValueError(f"Unsupported protein embedder '{name}'. Must be one of {', '.join(allowed_protein_embedders)}") config = PROTEIN_REPR_CONFIG[name] return config diff --git a/catpred/data/utils.py b/catpred/data/utils.py index 341791a..34f03be 100644 --- a/catpred/data/utils.py +++ b/catpred/data/utils.py @@ -15,7 +15,6 @@ import numpy as np import pandas as pd from tqdm import tqdm -import ipdb from .esm_utils import get_protein_embedder, get_coords from .data import MoleculeDatapoint, MoleculeDataset, make_mols diff --git a/catpred/features/__init__.py b/catpred/features/__init__.py index 596e02f..94220bf 100644 --- a/catpred/features/__init__.py +++ b/catpred/features/__init__.py @@ -4,7 +4,8 @@ from .featurization import atom_features, bond_features, BatchMolGraph, get_atom_fdim, get_bond_fdim, mol2graph, \ MolGraph, onek_encoding_unk, set_extra_atom_fdim, set_extra_bond_fdim, set_reaction, set_explicit_h, \ - set_adding_hs, set_keeping_atom_map, is_reaction, is_explicit_h, is_adding_hs, is_keeping_atom_map, is_mol, reset_featurization_parameters + set_adding_hs, set_keeping_atom_map, is_reaction, is_explicit_h, is_adding_hs, is_keeping_atom_map, is_mol, \ + reset_featurization_parameters, featurization_session from .utils import load_features, save_features, load_valid_atom_or_bond_features __all__ = [ @@ -37,5 +38,6 @@ 'load_features', 'save_features', 'load_valid_atom_or_bond_features', - 'reset_featurization_parameters' + 'reset_featurization_parameters', + 'featurization_session' ] diff --git a/catpred/features/featurization.py b/catpred/features/featurization.py index 7a9b5d2..b272475 100644 --- a/catpred/features/featurization.py +++ b/catpred/features/featurization.py @@ -1,6 +1,9 @@ from typing import List, Tuple, Union from itertools import zip_longest import logging +from contextlib import contextmanager +from copy import deepcopy +import threading from rdkit import Chem import torch @@ -50,6 +53,31 @@ def __init__(self) -> None: # Create a global parameter object for reference throughout this module PARAMS = Featurization_parameters() +_FEATURIZATION_LOCK = threading.RLock() + + +def _clone_featurization_parameters(params: Featurization_parameters) -> Featurization_parameters: + cloned = Featurization_parameters() + cloned.__dict__.update(deepcopy(params.__dict__)) + return cloned + + +@contextmanager +def featurization_session(): + """ + Protects global featurization state for one train/predict transaction. + + This is an interim safety mechanism for multi-request serving environments + where concurrent requests can otherwise mutate shared featurization globals. + """ + global PARAMS + + with _FEATURIZATION_LOCK: + snapshot = _clone_featurization_parameters(PARAMS) + try: + yield + finally: + PARAMS = snapshot def reset_featurization_parameters(logger: logging.Logger = None) -> None: diff --git a/catpred/models/model.py b/catpred/models/model.py index d9e8a88..5b50099 100644 --- a/catpred/models/model.py +++ b/catpred/models/model.py @@ -1,28 +1,18 @@ from typing import List, Union, Tuple -from rotary_embedding_torch import RotaryEmbedding +import os import numpy as np from rdkit import Chem import torch import torch.nn as nn +from rotary_embedding_torch import RotaryEmbedding +from torch.nn.utils.rnn import pad_sequence + from .mpn import MPN from .ffn import build_ffn, MultiReadout from catpred.args import TrainArgs from catpred.features import BatchMolGraph from catpred.nn_utils import initialize_weights -from torch.nn.utils.rnn import pad_sequence - -from collections import OrderedDict -import ipdb -import os - -import torch -import torch.nn as nn - -import ipdb -import torch -from torch import nn -from torch import einsum def _torch_load_compat(path): @@ -34,9 +24,6 @@ def _torch_load_compat(path): def exists(val): return val is not None - -def default(val, d): - return val if exists(val) else d class AttentivePooling(nn.Module): def __init__(self, input_size=1280, hidden_size=1280): @@ -238,7 +225,10 @@ def create_ffn(self, args: TrainArgs) -> None: first_linear_dim_now += 1280 if args.add_pretrained_egnn_feats: first_linear_dim_now+=128 - assert(os.path.exists(args.pretrained_egnn_feats_path)) + if not os.path.exists(args.pretrained_egnn_feats_path): + raise FileNotFoundError( + f'Pretrained EGNN features file not found: "{args.pretrained_egnn_feats_path}"' + ) self.readout = build_ffn( first_linear_dim=first_linear_dim_now, diff --git a/catpred/train/make_predictions.py b/catpred/train/make_predictions.py index 9e285ad..419507c 100644 --- a/catpred/train/make_predictions.py +++ b/catpred/train/make_predictions.py @@ -7,7 +7,7 @@ from catpred.args import PredictArgs, TrainArgs from catpred.data import get_data, get_data_from_smiles, MoleculeDataLoader, MoleculeDataset, StandardScaler, AtomBondScaler from catpred.utils import load_args, load_checkpoint, load_scalers, makedirs, timeit, update_prediction_args -from catpred.features import set_extra_atom_fdim, set_extra_bond_fdim, set_reaction, set_explicit_h, set_adding_hs, set_keeping_atom_map, reset_featurization_parameters +from catpred.features import set_extra_atom_fdim, set_extra_bond_fdim, set_reaction, set_explicit_h, set_adding_hs, set_keeping_atom_map, reset_featurization_parameters, featurization_session from catpred.models import MoleculeModel from catpred.uncertainty import UncertaintyCalibrator, build_uncertainty_calibrator, UncertaintyEstimator, build_uncertainty_evaluator from catpred.multitask_utils import reshape_values @@ -240,8 +240,14 @@ def predict_and_save( # Save results if save_results: print(f"Saving predictions to {args.preds_path}") - assert len(test_data) == len(preds) - assert len(test_data) == len(unc) + if len(test_data) != len(preds): + raise RuntimeError( + f"Prediction count mismatch: expected {len(test_data)} rows, got {len(preds)} predictions." + ) + if len(test_data) != len(unc): + raise RuntimeError( + f"Uncertainty count mismatch: expected {len(test_data)} rows, got {len(unc)} rows." + ) makedirs(args.preds_path, isfile=True) @@ -401,110 +407,108 @@ def make_predictions( num_models = len(args.checkpoint_paths) - set_features(args, train_args) + with featurization_session(): + set_features(args, train_args) - # Note: to get the invalid SMILES for your data, use the get_invalid_smiles_from_file or get_invalid_smiles_from_list functions from data/utils.py - full_data, test_data, test_data_loader, full_to_valid_indices = load_data( - args, smiles - ) - - if args.uncertainty_method is None and (args.calibration_method is not None or args.evaluation_methods is not None): - if args.dataset_type in ['classification', 'multiclass']: - args.uncertainty_method = 'classification' - else: - raise ValueError('Cannot calibrate or evaluate uncertainty without selection of an uncertainty method.') + # Note: to get the invalid SMILES for your data, use get_invalid_smiles_from_file/get_invalid_smiles_from_list. + full_data, test_data, test_data_loader, full_to_valid_indices = load_data( + args, smiles + ) + if args.uncertainty_method is None and (args.calibration_method is not None or args.evaluation_methods is not None): + if args.dataset_type in ['classification', 'multiclass']: + args.uncertainty_method = 'classification' + else: + raise ValueError('Cannot calibrate or evaluate uncertainty without selection of an uncertainty method.') + + if calibrator is None and args.calibration_path is not None: + + calibration_data = get_data( + protein_records_path=args.protein_records_path, + path=args.calibration_path, + smiles_columns=args.smiles_columns, + target_columns=task_names, + args=args, + features_path=args.calibration_features_path, + features_generator=args.features_generator, + phase_features_path=args.calibration_phase_features_path, + atom_descriptors_path=args.calibration_atom_descriptors_path, + bond_descriptors_path=args.calibration_bond_descriptors_path, + max_data_size=args.max_data_size, + loss_function=args.loss_function, + ) - if calibrator is None and args.calibration_path is not None: + calibration_data_loader = MoleculeDataLoader( + dataset=calibration_data, + batch_size=args.batch_size, + num_workers=args.num_workers, + ) - calibration_data = get_data( - protein_records_path=args.protein_records_path, - path=args.calibration_path, - smiles_columns=args.smiles_columns, - target_columns=task_names, - args=args, - features_path=args.calibration_features_path, - features_generator=args.features_generator, - phase_features_path=args.calibration_phase_features_path, - atom_descriptors_path=args.calibration_atom_descriptors_path, - bond_descriptors_path=args.calibration_bond_descriptors_path, - max_data_size=args.max_data_size, - loss_function=args.loss_function, - ) + if isinstance(models, list) and isinstance(scalers, list): + calibration_models = models + calibration_scalers = scalers + else: + calibration_model_objects = load_model(args, generator=True) + calibration_models = calibration_model_objects[2] + calibration_scalers = calibration_model_objects[3] - calibration_data_loader = MoleculeDataLoader( - dataset=calibration_data, - batch_size=args.batch_size, - num_workers=args.num_workers, - ) + calibrator = build_uncertainty_calibrator( + calibration_method=args.calibration_method, + uncertainty_method=args.uncertainty_method, + interval_percentile=args.calibration_interval_percentile, + regression_calibrator_metric=args.regression_calibrator_metric, + calibration_data=calibration_data, + calibration_data_loader=calibration_data_loader, + models=calibration_models, + scalers=calibration_scalers, + num_models=num_models, + dataset_type=args.dataset_type, + loss_function=args.loss_function, + uncertainty_dropout_p=args.uncertainty_dropout_p, + dropout_sampling_size=args.dropout_sampling_size, + spectra_phase_mask=getattr(train_args, "spectra_phase_mask", None), + ) - if isinstance(models, List) and isinstance(scalers, List): - calibration_models = models - calibration_scalers = scalers + # Edge case if empty list of smiles is provided + if len(test_data) == 0: + preds = [None] * len(full_data) + unc = [None] * len(full_data) else: - calibration_model_objects = load_model(args, generator=True) - calibration_models = calibration_model_objects[2] - calibration_scalers = calibration_model_objects[3] - - calibrator = build_uncertainty_calibrator( - calibration_method=args.calibration_method, - uncertainty_method=args.uncertainty_method, - interval_percentile=args.calibration_interval_percentile, - regression_calibrator_metric=args.regression_calibrator_metric, - calibration_data=calibration_data, - calibration_data_loader=calibration_data_loader, - models=calibration_models, - scalers=calibration_scalers, - num_models=num_models, - dataset_type=args.dataset_type, - loss_function=args.loss_function, - uncertainty_dropout_p=args.uncertainty_dropout_p, - dropout_sampling_size=args.dropout_sampling_size, - spectra_phase_mask=getattr(train_args, "spectra_phase_mask", None), - ) - - # Edge case if empty list of smiles is provided - if len(test_data) == 0: - preds = [None] * len(full_data) - unc = [None] * len(full_data) - else: - preds, unc = predict_and_save( - args=args, - train_args=train_args, - test_data=test_data, - task_names=task_names, - num_tasks=num_tasks, - test_data_loader=test_data_loader, - full_data=full_data, - full_to_valid_indices=full_to_valid_indices, - models=models, - scalers=scalers, - num_models=num_models, - calibrator=calibrator, - return_invalid_smiles=return_invalid_smiles, - ) + preds, unc = predict_and_save( + args=args, + train_args=train_args, + test_data=test_data, + task_names=task_names, + num_tasks=num_tasks, + test_data_loader=test_data_loader, + full_data=full_data, + full_to_valid_indices=full_to_valid_indices, + models=models, + scalers=scalers, + num_models=num_models, + calibrator=calibrator, + return_invalid_smiles=return_invalid_smiles, + ) - if return_index_dict: - preds_dict = {} - unc_dict = {} - for i in range(len(full_data)): - if return_invalid_smiles: - preds_dict[i] = preds[i] - unc_dict[i] = unc[i] - else: - valid_index = full_to_valid_indices.get(i, None) - if valid_index is not None: - preds_dict[i] = preds[valid_index] - unc_dict[i] = unc[valid_index] - if return_uncertainty: - return preds_dict, unc_dict - else: + if return_index_dict: + preds_dict = {} + unc_dict = {} + for i in range(len(full_data)): + if return_invalid_smiles: + preds_dict[i] = preds[i] + unc_dict[i] = unc[i] + else: + valid_index = full_to_valid_indices.get(i, None) + if valid_index is not None: + preds_dict[i] = preds[valid_index] + unc_dict[i] = unc[valid_index] + if return_uncertainty: + return preds_dict, unc_dict return preds_dict - else: + if return_uncertainty: return preds, unc - else: - return preds + return preds def catpred_predict() -> None: diff --git a/catpred/train/run_training.py b/catpred/train/run_training.py index 1e52394..aa0b762 100644 --- a/catpred/train/run_training.py +++ b/catpred/train/run_training.py @@ -237,7 +237,7 @@ def run_training(args: TrainArgs, makedirs(save_dir) try: writer = SummaryWriter(log_dir=save_dir) - except: + except TypeError: writer = SummaryWriter(logdir=save_dir) # Load/build model diff --git a/catpred/utils.py b/catpred/utils.py index ecde744..3f70e62 100644 --- a/catpred/utils.py +++ b/catpred/utils.py @@ -134,7 +134,7 @@ def load_checkpoint( # Build model model = MoleculeModel(args) - print(model) + debug(model) model_state_dict = model.state_dict() # Skip missing parameters and parameters of mismatched size @@ -172,9 +172,6 @@ def load_checkpoint( model = model.to(args.device) return model - -import ipdb - def overwrite_state_dict( loaded_param_name: str, model_param_name: str, diff --git a/demo_run.py b/demo_run.py index 5b77e38..4bb9b57 100644 --- a/demo_run.py +++ b/demo_run.py @@ -52,18 +52,20 @@ def prepare_prediction_inputs(parameter, input_file_path): for i, smi in enumerate(smiles_list): try: mol = Chem.MolFromSmiles(smi) + if mol is None: + raise ValueError("RDKit could not parse SMILES") smi = Chem.MolToSmiles(mol) if parameter == 'kcat' and '.' in smi: smi = '.'.join(sorted(smi.split('.'))) smiles_list_new.append(smi) - except: - print(f'Invalid SMILES input in input row {i}') + except Exception as exc: + print(f'Invalid SMILES input in input row {i}: {exc}') print('Correct your input! Exiting..') return None valid_aas = set('ACDEFGHIKLMNPQRSTVWY') for i, seq in enumerate(seq_list): - if not set(seq).issubset(valid_aas): + if not isinstance(seq, str) or not set(seq).issubset(valid_aas): print(f'Invalid Enzyme sequence input in row {i}!') print('Correct your input! Exiting..') return None diff --git a/setup.cfg b/setup.cfg index 168dee3..d75df80 100644 --- a/setup.cfg +++ b/setup.cfg @@ -51,11 +51,6 @@ console_scripts = catpred_train=catpred.train:catpred_train catpred_predict=catpred.train:catpred_predict catpred_fingerprint=catpred.train:catpred_fingerprint - catpred_hyperopt=catpred.hyperparameter_optimization:catpred_hyperopt - catpred_interpret=catpred.interpret:catpred_interpret - catpred_web=catpred.web.run:catpred_web - sklearn_train=catpred.sklearn_train:sklearn_train - sklearn_predict=catpred.sklearn_predict:sklearn_predict [options.extras_require] test = pytest>=6.2.2; parameterized>=0.8.1 diff --git a/setup.py b/setup.py index 5de8c5d..e7d62bf 100644 --- a/setup.py +++ b/setup.py @@ -28,6 +28,7 @@ "console_scripts": [ "catpred_train=catpred.train:catpred_train", "catpred_predict=catpred.train:catpred_predict", + "catpred_fingerprint=catpred.train:catpred_fingerprint", ] }, install_requires=[ From 89d686662f331ac6aee15c2300ac8bbf46b11101 Mon Sep 17 00:00:00 2001 From: theproteinbot Date: Sat, 28 Feb 2026 14:29:42 -0500 Subject: [PATCH 04/34] Phase 1: extract reusable inference service core --- catpred/__init__.py | 1 + catpred/data/esm_utils.py | 2 +- catpred/inference/__init__.py | 17 +++ catpred/inference/service.py | 240 ++++++++++++++++++++++++++++++++ demo_run.py | 250 +++++++--------------------------- 5 files changed, 306 insertions(+), 204 deletions(-) create mode 100644 catpred/inference/__init__.py create mode 100644 catpred/inference/service.py diff --git a/catpred/__init__.py b/catpred/__init__.py index e27ba71..c4b3702 100644 --- a/catpred/__init__.py +++ b/catpred/__init__.py @@ -3,6 +3,7 @@ import catpred.models import catpred.train import catpred.uncertainty +import catpred.inference import catpred.args import catpred.constants diff --git a/catpred/data/esm_utils.py b/catpred/data/esm_utils.py index 359613a..ff527e9 100644 --- a/catpred/data/esm_utils.py +++ b/catpred/data/esm_utils.py @@ -22,7 +22,7 @@ def _env_flag(name: str, default: str = "0") -> bool: return str(raw).strip().lower() in {"1", "true", "yes", "on"} -PROTEIN_EMBED_USE_CPU = _env_flag("PROTEIN_EMBED_USE_CPU", "0") +PROTEIN_EMBED_USE_CPU = _env_flag("PROTEIN_EMBED_USE_CPU", "0") or not torch.cuda.is_available() if PROTEIN_EMBED_USE_CPU: print('calculating protein embed only on cpu') diff --git a/catpred/inference/__init__.py b/catpred/inference/__init__.py new file mode 100644 index 0000000..c4b814e --- /dev/null +++ b/catpred/inference/__init__.py @@ -0,0 +1,17 @@ +from .service import ( + PredictionRequest, + PreparedInputPaths, + prepare_prediction_inputs, + run_raw_prediction, + postprocess_predictions, + run_prediction_pipeline, +) + +__all__ = [ + "PredictionRequest", + "PreparedInputPaths", + "prepare_prediction_inputs", + "run_raw_prediction", + "postprocess_predictions", + "run_prediction_pipeline", +] diff --git a/catpred/inference/service.py b/catpred/inference/service.py new file mode 100644 index 0000000..99f768d --- /dev/null +++ b/catpred/inference/service.py @@ -0,0 +1,240 @@ +from __future__ import annotations + +from dataclasses import dataclass +from pathlib import Path +import os +import subprocess +from typing import Tuple + +import numpy as np +import pandas as pd +from rdkit import Chem + + +_VALID_PARAMETERS = {"kcat", "km", "ki"} +_TARGET_COLUMNS = { + "kcat": ("log10kcat_max", "s^(-1)"), + "km": ("log10km_mean", "mM"), + "ki": ("log10ki_mean", "mM"), +} +_VALID_AAS = set("ACDEFGHIKLMNPQRSTVWY") + + +@dataclass(frozen=True) +class PredictionRequest: + parameter: str + input_file: str + checkpoint_dir: str + use_gpu: bool = False + repo_root: str | None = None + python_executable: str = "python" + + +@dataclass(frozen=True) +class PreparedInputPaths: + input_csv: str + records_file: str + output_csv: str + + +def _validate_parameter(parameter: str) -> str: + parameter = parameter.lower() + if parameter not in _VALID_PARAMETERS: + raise ValueError(f"Unsupported parameter '{parameter}'. Must be one of: kcat, km, ki.") + return parameter + + +def _resolve_repo_root(repo_root: str | None) -> Path: + root = Path(repo_root) if repo_root else Path.cwd() + root = root.resolve() + if not root.exists(): + raise FileNotFoundError(f'Repository root does not exist: "{root}"') + return root + + +def _resolve_input_path(input_file: str, repo_root: Path) -> Path: + path = Path(input_file) + if not path.is_absolute(): + path = (repo_root / path).resolve() + if not path.exists(): + raise FileNotFoundError(f'Input CSV not found: "{path}"') + return path + + +def _validate_and_prepare_dataframe(parameter: str, df: pd.DataFrame, input_csv: Path) -> pd.DataFrame: + required_columns = {"SMILES", "sequence", "pdbpath"} + missing = required_columns.difference(df.columns) + if missing: + raise ValueError( + f'Missing required column(s) in "{input_csv}": {", ".join(sorted(missing))}.' + ) + + conflicting_pdbpaths = ( + df.groupby("pdbpath")["sequence"] + .nunique(dropna=False) + .loc[lambda value: value > 1] + ) + if len(conflicting_pdbpaths) > 0: + preview = ", ".join(conflicting_pdbpaths.index.astype(str).tolist()[:5]) + raise ValueError( + "Found pdbpath values mapped to multiple sequences. " + f"Each unique sequence must have a unique pdbpath. Examples: {preview}" + ) + + canonical_smiles = [] + for i, raw_smiles in enumerate(df["SMILES"]): + mol = Chem.MolFromSmiles(raw_smiles) + if mol is None: + raise ValueError(f'Invalid SMILES input in row {i + 2}: "{raw_smiles}"') + smiles = Chem.MolToSmiles(mol) + if parameter == "kcat" and "." in smiles: + smiles = ".".join(sorted(smiles.split("."))) + canonical_smiles.append(smiles) + + for i, sequence in enumerate(df["sequence"]): + if not isinstance(sequence, str) or not set(sequence).issubset(_VALID_AAS): + raise ValueError(f'Invalid enzyme sequence in row {i + 2}: "{sequence}"') + + prepared = df.copy() + prepared["SMILES"] = canonical_smiles + return prepared + + +def prepare_prediction_inputs(parameter: str, input_file: str, repo_root: str | None = None) -> PreparedInputPaths: + parameter = _validate_parameter(parameter) + root = _resolve_repo_root(repo_root) + input_csv = _resolve_input_path(input_file, root) + + df = pd.read_csv(input_csv) + prepared_df = _validate_and_prepare_dataframe(parameter, df, input_csv) + + input_base = input_csv.with_suffix("") + prepared_input_csv = Path(f"{input_base}_input.csv") + prepared_df.to_csv(prepared_input_csv, index=False) + + test_prefix = prepared_input_csv.with_suffix("") + records_file = Path(f"{test_prefix}.json.gz") + output_csv = Path(f"{test_prefix}_output.csv") + + return PreparedInputPaths( + input_csv=str(prepared_input_csv), + records_file=str(records_file), + output_csv=str(output_csv), + ) + + +def _build_prediction_commands( + python_executable: str, + repo_root: Path, + paths: PreparedInputPaths, + checkpoint_dir: str, +) -> Tuple[list[str], list[str]]: + create_records_cmd = [ + python_executable, + str(repo_root / "scripts" / "create_pdbrecords.py"), + "--data_file", + paths.input_csv, + "--out_file", + paths.records_file, + ] + predict_cmd = [ + python_executable, + str(repo_root / "predict.py"), + "--test_path", + paths.input_csv, + "--preds_path", + paths.output_csv, + "--checkpoint_dir", + checkpoint_dir, + "--uncertainty_method", + "mve", + "--smiles_column", + "SMILES", + "--individual_ensemble_predictions", + "--protein_records_path", + paths.records_file, + ] + return create_records_cmd, predict_cmd + + +def run_raw_prediction(request: PredictionRequest, paths: PreparedInputPaths) -> None: + root = _resolve_repo_root(request.repo_root) + create_records_cmd, predict_cmd = _build_prediction_commands( + python_executable=request.python_executable, + repo_root=root, + paths=paths, + checkpoint_dir=request.checkpoint_dir, + ) + + env = os.environ.copy() + env["PROTEIN_EMBED_USE_CPU"] = "0" if request.use_gpu else "1" + + subprocess.run(create_records_cmd, cwd=str(root), env=env, check=True) + subprocess.run(predict_cmd, cwd=str(root), env=env, check=True) + + if not os.path.exists(paths.output_csv): + raise FileNotFoundError(f'Prediction output file was not generated: "{paths.output_csv}"') + + +def postprocess_predictions(parameter: str, output_csv: str) -> pd.DataFrame: + parameter = _validate_parameter(parameter) + target_col, unit = _TARGET_COLUMNS[parameter] + unc_col = f"{target_col}_mve_uncal_var" + + df = pd.read_csv(output_csv) + missing_cols = [col for col in [target_col, unc_col] if col not in df.columns] + if missing_cols: + raise ValueError( + f'Prediction output is missing required column(s): {", ".join(missing_cols)}' + ) + + pred_col, pred_logcol, pred_sd_tot, pred_sd_alea, pred_sd_epi = [], [], [], [], [] + + for _, row in df.iterrows(): + model_cols = [col for col in row.index if col.startswith(target_col) and "model_" in col] + + unc = row[unc_col] + prediction_log = row[target_col] + prediction_linear = np.power(10, prediction_log) + + if model_cols: + model_outs = np.array([row[col] for col in model_cols]) + epi_unc_var = np.var(model_outs) + else: + epi_unc_var = 0.0 + + alea_unc_var = max(unc - epi_unc_var, 0.0) + epi_unc = np.sqrt(epi_unc_var) + alea_unc = np.sqrt(alea_unc_var) + total_unc = np.sqrt(max(unc, 0.0)) + + pred_col.append(prediction_linear) + pred_logcol.append(prediction_log) + pred_sd_tot.append(total_unc) + pred_sd_alea.append(alea_unc) + pred_sd_epi.append(epi_unc) + + df[f"Prediction_({unit})"] = pred_col + df["Prediction_log10"] = pred_logcol + df["SD_total"] = pred_sd_tot + df["SD_aleatoric"] = pred_sd_alea + df["SD_epistemic"] = pred_sd_epi + return df + + +def run_prediction_pipeline(request: PredictionRequest, results_dir: str = "../results") -> str: + parameter = _validate_parameter(request.parameter) + paths = prepare_prediction_inputs(parameter, request.input_file, request.repo_root) + run_raw_prediction(request, paths) + + output_final = postprocess_predictions(parameter, paths.output_csv) + + results_path = Path(results_dir) + if not results_path.is_absolute(): + results_path = (_resolve_repo_root(request.repo_root) / results_path).resolve() + results_path.mkdir(parents=True, exist_ok=True) + + out_name = Path(paths.output_csv).name + final_output = results_path / out_name + output_final.to_csv(final_output, index=False) + return str(final_output) diff --git a/demo_run.py b/demo_run.py index 4bb9b57..e6c3d08 100644 --- a/demo_run.py +++ b/demo_run.py @@ -1,220 +1,64 @@ """ -Enzyme Kinetics Parameter Prediction Script - -This script predicts enzyme kinetics parameters (kcat, Km, or Ki) using a pre-trained model. -It processes input data, generates predictions, and saves the results. +Enzyme kinetics parameter prediction CLI for local/demo usage. Usage: - python demo_run.py --parameter --input_file --checkpoint_dir [--use_gpu] - -Dependencies: - pandas, numpy, rdkit, IPython, argparse + python demo_run.py --parameter --input_file --checkpoint_dir [--use_gpu] """ -import time -import os -import subprocess -import pandas as pd -import numpy as np -from IPython.display import Image, display -from rdkit import Chem -from IPython.display import display, Latex, Math import argparse +import subprocess +from catpred.inference import PredictionRequest, run_prediction_pipeline -def prepare_prediction_inputs(parameter, input_file_path): - df = pd.read_csv(input_file_path) - required_columns = {"SMILES", "sequence", "pdbpath"} - missing_columns = required_columns.difference(df.columns) - if missing_columns: - print( - f'Missing required column(s) in input file: {", ".join(sorted(missing_columns))}.' - ) - return None - conflicting_pdbpaths = ( - df.groupby("pdbpath")["sequence"] - .nunique(dropna=False) - .loc[lambda value: value > 1] +def main(args: argparse.Namespace) -> int: + request = PredictionRequest( + parameter=args.parameter.lower(), + input_file=args.input_file, + checkpoint_dir=args.checkpoint_dir, + use_gpu=args.use_gpu, + repo_root=".", ) - if len(conflicting_pdbpaths) > 0: - preview = ", ".join(conflicting_pdbpaths.index.astype(str).tolist()[:5]) - print( - "Found pdbpath values mapped to multiple sequences. " - f"Each unique sequence must have a unique pdbpath. Examples: {preview}" - ) - return None - - smiles_list = df.SMILES - seq_list = df.sequence - smiles_list_new = [] - - for i, smi in enumerate(smiles_list): - try: - mol = Chem.MolFromSmiles(smi) - if mol is None: - raise ValueError("RDKit could not parse SMILES") - smi = Chem.MolToSmiles(mol) - if parameter == 'kcat' and '.' in smi: - smi = '.'.join(sorted(smi.split('.'))) - smiles_list_new.append(smi) - except Exception as exc: - print(f'Invalid SMILES input in input row {i}: {exc}') - print('Correct your input! Exiting..') - return None - - valid_aas = set('ACDEFGHIKLMNPQRSTVWY') - for i, seq in enumerate(seq_list): - if not isinstance(seq, str) or not set(seq).issubset(valid_aas): - print(f'Invalid Enzyme sequence input in row {i}!') - print('Correct your input! Exiting..') - return None - - input_file_base, _ = os.path.splitext(input_file_path) - input_file_new_path = f'{input_file_base}_input.csv' - df['SMILES'] = smiles_list_new - df.to_csv(input_file_new_path, index=False) - - test_file_prefix = input_file_new_path[:-4] - return { - "input_csv": input_file_new_path, - "records_file": f"{test_file_prefix}.json.gz", - "output_csv": f"{test_file_prefix}_output.csv", - } - -def get_predictions(parameter, outfile): - """ - Process prediction results and add additional metrics. - - Args: - parameter (str): The kinetics parameter that was predicted. - outfile (str): Path to the output CSV file from the prediction. - - Returns: - pandas.DataFrame: Processed predictions with additional metrics. - """ - df = pd.read_csv(outfile) - pred_col, pred_logcol, pred_sd_totcol, pred_sd_aleacol, pred_sd_epicol = [], [], [], [], [] - - unit = 'mM' - if parameter == 'kcat': - target_col = 'log10kcat_max' - unit = 's^(-1)' - elif parameter == 'km': - target_col = 'log10km_mean' - else: - target_col = 'log10ki_mean' - - unc_col = f'{target_col}_mve_uncal_var' - - missing_cols = [col for col in [target_col, unc_col] if col not in df.columns] - if missing_cols: - raise ValueError( - f'Prediction output is missing required column(s): {", ".join(missing_cols)}' - ) - - for _, row in df.iterrows(): - model_cols = [col for col in row.index if col.startswith(target_col) and 'model_' in col] - - unc = row[unc_col] - prediction = row[target_col] - prediction_linear = np.power(10, prediction) - if model_cols: - model_outs = np.array([row[col] for col in model_cols]) - epi_unc = np.var(model_outs) - else: - epi_unc = 0.0 - alea_unc = max(unc - epi_unc, 0.0) - epi_unc = np.sqrt(epi_unc) - alea_unc = np.sqrt(alea_unc) - unc = np.sqrt(max(unc, 0.0)) + print("Predicting.. This will take a while..") + try: + final_output = run_prediction_pipeline(request=request, results_dir="../results") + except (ValueError, FileNotFoundError) as exc: + print(str(exc)) + return 1 + except subprocess.CalledProcessError as exc: + print(f"Prediction command failed with exit code {exc.returncode}.") + return exc.returncode if exc.returncode is not None else 1 - pred_col.append(prediction_linear) - pred_logcol.append(prediction) - pred_sd_totcol.append(unc) - pred_sd_aleacol.append(alea_unc) - pred_sd_epicol.append(epi_unc) + print(f"Output saved to {final_output}") + return 0 - df[f'Prediction_({unit})'] = pred_col - df['Prediction_log10'] = pred_logcol - df['SD_total'] = pred_sd_totcol - df['SD_aleatoric'] = pred_sd_aleacol - df['SD_epistemic'] = pred_sd_epicol - - return df - -def main(args): - run_paths = prepare_prediction_inputs(args.parameter, args.input_file) - if run_paths is None: - return - - outfile = run_paths["output_csv"] - print('Predicting.. This will take a while..') - - env = os.environ.copy() - env["PROTEIN_EMBED_USE_CPU"] = "0" if args.use_gpu else "1" - - create_records_cmd = [ - "python", - "./scripts/create_pdbrecords.py", - "--data_file", - run_paths["input_csv"], - "--out_file", - run_paths["records_file"], - ] - predict_cmd = [ - "python", - "predict.py", - "--test_path", - run_paths["input_csv"], - "--preds_path", - outfile, - "--checkpoint_dir", - args.checkpoint_dir, - "--uncertainty_method", - "mve", - "--smiles_column", - "SMILES", - "--individual_ensemble_predictions", - "--protein_records_path", - run_paths["records_file"], - ] - - create_records_result = subprocess.run(create_records_cmd, env=env) - if create_records_result.returncode != 0: - print( - f"Protein record generation failed with exit code {create_records_result.returncode}." - ) - return - - predict_result = subprocess.run(predict_cmd, env=env) - if predict_result.returncode != 0: - print(f"Prediction command failed with exit code {predict_result.returncode}.") - return - - if not os.path.exists(outfile): - print(f'Prediction output file was not generated: {outfile}') - return - - output_final = get_predictions(args.parameter, outfile) - filename = outfile.split('/')[-1] - os.makedirs('../results', exist_ok=True) - output_final.to_csv(f'../results/{filename}', index=False) - print('Output saved to results/', filename) if __name__ == "__main__": parser = argparse.ArgumentParser(description="Predict enzyme kinetics parameters.") - parser.add_argument("--parameter", type=str, choices=["kcat", "km", "ki"], required=True, - help="Kinetics parameter to predict (kcat, km, or ki)") - parser.add_argument("--input_file", type=str, required=True, - help="Path to the input CSV file") - parser.add_argument("--use_gpu", action="store_true", - help="Use GPU for prediction (default is CPU)") - parser.add_argument("--checkpoint_dir", type=str, required=True, - help="Path to the model checkpoint directory") - - args = parser.parse_args() - args.parameter = args.parameter.lower() + parser.add_argument( + "--parameter", + type=str, + choices=["kcat", "km", "ki"], + required=True, + help="Kinetics parameter to predict (kcat, km, or ki)", + ) + parser.add_argument( + "--input_file", + type=str, + required=True, + help="Path to the input CSV file", + ) + parser.add_argument( + "--use_gpu", + action="store_true", + help="Use GPU for prediction (default is CPU)", + ) + parser.add_argument( + "--checkpoint_dir", + type=str, + required=True, + help="Path to the model checkpoint directory", + ) - main(args) + raise SystemExit(main(parser.parse_args())) From 968b54918a0265c1acba079f17a7ccd45570c461 Mon Sep 17 00:00:00 2001 From: theproteinbot Date: Sat, 28 Feb 2026 16:04:12 -0500 Subject: [PATCH 05/34] Phase 2: add web API and optional modal backend routing --- .gitignore | 1 + README.md | 46 +++++ catpred/__init__.py | 39 ++-- catpred/inference/__init__.py | 40 ++++- catpred/inference/backends.py | 325 ++++++++++++++++++++++++++++++++++ catpred/inference/service.py | 19 +- catpred/inference/types.py | 20 +++ catpred/web/__init__.py | 3 + catpred/web/app.py | 160 +++++++++++++++++ catpred/web/run.py | 28 +++ setup.cfg | 4 + setup.py | 7 + 12 files changed, 655 insertions(+), 37 deletions(-) create mode 100644 catpred/inference/backends.py create mode 100644 catpred/inference/types.py create mode 100644 catpred/web/__init__.py create mode 100644 catpred/web/app.py create mode 100644 catpred/web/run.py diff --git a/.gitignore b/.gitignore index a3b307a..4edc983 100644 --- a/.gitignore +++ b/.gitignore @@ -39,6 +39,7 @@ __pycache__/ *.pyd .DS_Store *.egg-info/ +.venv/ .ipynb_checkpoints/ # Local validation artifacts diff --git a/README.md b/README.md index 2a92ed8..0e9d6d9 100644 --- a/README.md +++ b/README.md @@ -23,6 +23,7 @@ - [System Requirements](#requirements) - [Installation](#installing) - [Prediction](#predict) + - [Web API (Optional)](#web-api-optional) - [Reproducibility](#reproduce) - [Acknowledgements](#acknw) - [License](#license) @@ -100,6 +101,51 @@ CatPred currently expects one sequence per row. Multi-protein complexes (e.g., h For released benchmark datasets, the number of entries with 3D structure can be smaller than the total sequence/substrate pairs; 3D-derived artifacts are available only for the subset with valid structure mapping. +### ๐ŸŒ Web API (Optional) + +CatPred also provides an optional FastAPI service for prediction workflows. + +Install web dependencies: + +```bash +pip install -e ".[web]" +``` + +Run the API: + +```bash +catpred_web --host 0.0.0.0 --port 8000 +``` + +Endpoints: +- `GET /health` โ€” liveness check. +- `GET /ready` โ€” backend configuration/readiness. +- `POST /predict` โ€” run inference. + +Minimal `POST /predict` example for local inference: + +```bash +curl -X POST http://127.0.0.1:8000/predict \ + -H "Content-Type: application/json" \ + -d '{ + "parameter": "kcat", + "checkpoint_dir": "../data/pretrained/reproduce_checkpoints/kcat", + "input_file": "./demo/batch_kcat_pred.csv", + "backend": "local" + }' +``` + +You can keep local inference as default and optionally enable Modal as another backend: + +```bash +export CATPRED_DEFAULT_BACKEND=local +export CATPRED_MODAL_ENDPOINT="https://" +export CATPRED_MODAL_TOKEN="" +export CATPRED_MODAL_FALLBACK_TO_LOCAL=1 +``` + +Use `"backend": "modal"` in `/predict` requests to route through Modal. If fallback is enabled (env var above or request field `fallback_to_local`), failed modal requests can automatically reroute to local inference. + ### ๐Ÿงช Fine-Tuning On Custom Data You can fine-tune CatPred on your own regression targets using `train.py`. diff --git a/catpred/__init__.py b/catpred/__init__.py index c4b3702..92cfc40 100644 --- a/catpred/__init__.py +++ b/catpred/__init__.py @@ -1,14 +1,29 @@ -import catpred.data -import catpred.features -import catpred.models -import catpred.train -import catpred.uncertainty -import catpred.inference - -import catpred.args -import catpred.constants -import catpred.nn_utils -import catpred.utils -import catpred.rdkit +from __future__ import annotations + +import importlib __version__ = "0.0.1" + +_LAZY_SUBMODULES = { + "args", + "constants", + "data", + "features", + "inference", + "models", + "nn_utils", + "rdkit", + "train", + "uncertainty", + "utils", +} + +__all__ = sorted(_LAZY_SUBMODULES) + ["__version__"] + + +def __getattr__(name: str): + if name in _LAZY_SUBMODULES: + module = importlib.import_module(f"catpred.{name}") + globals()[name] = module + return module + raise AttributeError(f"module 'catpred' has no attribute '{name}'") diff --git a/catpred/inference/__init__.py b/catpred/inference/__init__.py index c4b814e..103d8ed 100644 --- a/catpred/inference/__init__.py +++ b/catpred/inference/__init__.py @@ -1,13 +1,24 @@ -from .service import ( - PredictionRequest, - PreparedInputPaths, - prepare_prediction_inputs, - run_raw_prediction, - postprocess_predictions, - run_prediction_pipeline, +from __future__ import annotations + +from .backends import ( + BackendPredictionResult, + BackendRouterSettings, + InferenceBackend, + InferenceBackendError, + InferenceBackendRouter, + LocalInferenceBackend, + ModalHTTPInferenceBackend, ) +from .types import PreparedInputPaths, PredictionRequest __all__ = [ + "BackendPredictionResult", + "BackendRouterSettings", + "InferenceBackend", + "InferenceBackendError", + "InferenceBackendRouter", + "LocalInferenceBackend", + "ModalHTTPInferenceBackend", "PredictionRequest", "PreparedInputPaths", "prepare_prediction_inputs", @@ -15,3 +26,18 @@ "postprocess_predictions", "run_prediction_pipeline", ] + + +def __getattr__(name: str): + if name in { + "prepare_prediction_inputs", + "run_raw_prediction", + "postprocess_predictions", + "run_prediction_pipeline", + }: + from . import service + + value = getattr(service, name) + globals()[name] = value + return value + raise AttributeError(f"module 'catpred.inference' has no attribute '{name}'") diff --git a/catpred/inference/backends.py b/catpred/inference/backends.py new file mode 100644 index 0000000..916dfe9 --- /dev/null +++ b/catpred/inference/backends.py @@ -0,0 +1,325 @@ +from __future__ import annotations + +from dataclasses import dataclass, field, replace +from pathlib import Path +import csv +import json +import os +from typing import Any +from urllib import error, request as urllib_request + +import pandas as pd + +from .types import PredictionRequest + + +class InferenceBackendError(RuntimeError): + """Raised when a backend cannot satisfy an inference request.""" + + +@dataclass(frozen=True) +class BackendPredictionResult: + backend_name: str + output_file: str + metadata: dict[str, Any] = field(default_factory=dict) + + +class InferenceBackend: + name = "base" + + def readiness(self) -> dict[str, Any]: + raise NotImplementedError + + def predict(self, request_obj: PredictionRequest, results_dir: str) -> BackendPredictionResult: + raise NotImplementedError + + +class LocalInferenceBackend(InferenceBackend): + name = "local" + + def __init__(self, repo_root: str | None = None) -> None: + self._repo_root = repo_root + + def readiness(self) -> dict[str, Any]: + root = Path(self._repo_root) if self._repo_root else Path.cwd() + root = root.resolve() + required = [ + root / "predict.py", + root / "scripts" / "create_pdbrecords.py", + ] + missing = [str(path) for path in required if not path.exists()] + return { + "configured": True, + "ready": len(missing) == 0, + "missing_files": missing, + "repo_root": str(root), + } + + def predict(self, request_obj: PredictionRequest, results_dir: str) -> BackendPredictionResult: + from .service import run_prediction_pipeline + + effective_request = request_obj + if not request_obj.repo_root and self._repo_root: + effective_request = replace(request_obj, repo_root=self._repo_root) + + output_file = run_prediction_pipeline(effective_request, results_dir=results_dir) + return BackendPredictionResult(backend_name=self.name, output_file=output_file) + + +class ModalHTTPInferenceBackend(InferenceBackend): + name = "modal" + + def __init__( + self, + endpoint: str | None, + token: str | None = None, + timeout_seconds: int = 900, + repo_root: str | None = None, + ) -> None: + self._endpoint = endpoint + self._token = token + self._timeout_seconds = timeout_seconds + self._repo_root = repo_root + + def readiness(self) -> dict[str, Any]: + configured = bool(self._endpoint) + return { + "configured": configured, + "ready": configured, + "endpoint": self._endpoint, + "timeout_seconds": self._timeout_seconds, + } + + def _resolve_input_file(self, request_obj: PredictionRequest) -> Path: + input_path = Path(request_obj.input_file) + if not input_path.is_absolute(): + root = Path(request_obj.repo_root or self._repo_root or Path.cwd()).resolve() + input_path = (root / input_path).resolve() + if not input_path.exists(): + raise FileNotFoundError(f'Input CSV not found for modal backend: "{input_path}"') + return input_path + + def _resolve_results_dir(self, results_dir: str, request_obj: PredictionRequest) -> Path: + out_dir = Path(results_dir) + if not out_dir.is_absolute(): + root = Path(request_obj.repo_root or self._repo_root or Path.cwd()).resolve() + out_dir = (root / out_dir).resolve() + out_dir.mkdir(parents=True, exist_ok=True) + return out_dir + + @staticmethod + def _load_rows(csv_path: Path) -> list[dict[str, Any]]: + with csv_path.open(newline="", encoding="utf-8") as handle: + return list(csv.DictReader(handle)) + + def _post_json(self, payload: dict[str, Any]) -> dict[str, Any]: + if not self._endpoint: + raise InferenceBackendError( + "Modal backend is not configured. Set CATPRED_MODAL_ENDPOINT." + ) + + encoded_payload = json.dumps(payload).encode("utf-8") + headers = {"Content-Type": "application/json"} + if self._token: + headers["Authorization"] = f"Bearer {self._token}" + + req = urllib_request.Request( + url=self._endpoint, + method="POST", + data=encoded_payload, + headers=headers, + ) + try: + with urllib_request.urlopen(req, timeout=self._timeout_seconds) as resp: + raw = resp.read() + except error.HTTPError as exc: + body = exc.read().decode("utf-8", errors="replace") + raise InferenceBackendError( + f"Modal backend request failed with HTTP {exc.code}: {body}" + ) from exc + except error.URLError as exc: + raise InferenceBackendError( + f"Modal backend request failed: {exc.reason}" + ) from exc + + try: + decoded = json.loads(raw.decode("utf-8")) + except json.JSONDecodeError as exc: + raise InferenceBackendError("Modal backend returned non-JSON output.") from exc + + if not isinstance(decoded, dict): + raise InferenceBackendError( + "Modal backend response must be a JSON object." + ) + return decoded + + def _materialize_output( + self, + response: dict[str, Any], + input_path: Path, + results_dir: str, + request_obj: PredictionRequest, + ) -> BackendPredictionResult: + output_file = response.get("output_file") + if isinstance(output_file, str) and output_file: + resolved = Path(output_file).resolve() + if resolved.exists(): + return BackendPredictionResult( + backend_name=self.name, + output_file=str(resolved), + metadata={"endpoint": self._endpoint, "mode": "output_file"}, + ) + + output_rows = response.get("output_rows") + if isinstance(output_rows, list): + out_dir = self._resolve_results_dir(results_dir, request_obj) + out_name = response.get("output_filename") + if not isinstance(out_name, str) or not out_name: + out_name = f"{input_path.stem}_modal_output.csv" + if not out_name.endswith(".csv"): + out_name = f"{out_name}.csv" + + final_output = out_dir / out_name + pd.DataFrame(output_rows).to_csv(final_output, index=False) + return BackendPredictionResult( + backend_name=self.name, + output_file=str(final_output), + metadata={"endpoint": self._endpoint, "mode": "output_rows"}, + ) + + output_csv_text = response.get("output_csv_text") + if isinstance(output_csv_text, str) and output_csv_text: + out_dir = self._resolve_results_dir(results_dir, request_obj) + out_name = response.get("output_filename") + if not isinstance(out_name, str) or not out_name: + out_name = f"{input_path.stem}_modal_output.csv" + if not out_name.endswith(".csv"): + out_name = f"{out_name}.csv" + final_output = out_dir / out_name + final_output.write_text(output_csv_text, encoding="utf-8") + return BackendPredictionResult( + backend_name=self.name, + output_file=str(final_output), + metadata={"endpoint": self._endpoint, "mode": "output_csv_text"}, + ) + + raise InferenceBackendError( + "Modal backend response must include one of: output_file, output_rows, output_csv_text." + ) + + def predict(self, request_obj: PredictionRequest, results_dir: str) -> BackendPredictionResult: + input_path = self._resolve_input_file(request_obj) + payload = { + "parameter": request_obj.parameter, + "checkpoint_dir": request_obj.checkpoint_dir, + "use_gpu": request_obj.use_gpu, + "input_rows": self._load_rows(input_path), + "input_filename": input_path.name, + } + response = self._post_json(payload) + return self._materialize_output( + response=response, + input_path=input_path, + results_dir=results_dir, + request_obj=request_obj, + ) + + +def _env_flag(name: str, default: bool = False) -> bool: + value = os.environ.get(name) + if value is None: + return default + return value.strip().lower() in {"1", "true", "yes", "y", "on"} + + +@dataclass(frozen=True) +class BackendRouterSettings: + default_backend: str = "local" + modal_endpoint: str | None = None + modal_token: str | None = None + modal_timeout_seconds: int = 900 + repo_root: str | None = None + + @classmethod + def from_env(cls) -> "BackendRouterSettings": + timeout = int(os.environ.get("CATPRED_MODAL_TIMEOUT_SECONDS", "900")) + return cls( + default_backend=os.environ.get("CATPRED_DEFAULT_BACKEND", "local").lower(), + modal_endpoint=os.environ.get("CATPRED_MODAL_ENDPOINT"), + modal_token=os.environ.get("CATPRED_MODAL_TOKEN"), + modal_timeout_seconds=timeout, + repo_root=os.environ.get("CATPRED_REPO_ROOT"), + ) + + +class InferenceBackendRouter: + def __init__(self, settings: BackendRouterSettings | None = None) -> None: + self.settings = settings or BackendRouterSettings.from_env() + self._backends: dict[str, InferenceBackend] = { + "local": LocalInferenceBackend(repo_root=self.settings.repo_root), + "modal": ModalHTTPInferenceBackend( + endpoint=self.settings.modal_endpoint, + token=self.settings.modal_token, + timeout_seconds=self.settings.modal_timeout_seconds, + repo_root=self.settings.repo_root, + ), + } + if self.settings.default_backend not in self._backends: + raise ValueError( + f"Unsupported CATPRED_DEFAULT_BACKEND '{self.settings.default_backend}'. " + "Use one of: local, modal." + ) + + def available_backends(self) -> list[str]: + return sorted(self._backends.keys()) + + def resolve_backend(self, backend_name: str | None = None) -> InferenceBackend: + selected = (backend_name or self.settings.default_backend).lower() + if selected not in self._backends: + raise ValueError( + f"Unsupported backend '{selected}'. Use one of: {', '.join(self.available_backends())}." + ) + + backend = self._backends[selected] + state = backend.readiness() + if not state.get("configured", False): + raise InferenceBackendError( + f"Backend '{selected}' is not configured. Readiness: {state}" + ) + return backend + + def predict( + self, + request_obj: PredictionRequest, + results_dir: str, + backend_name: str | None = None, + fallback_to_local: bool = False, + ) -> BackendPredictionResult: + selected_name = (backend_name or self.settings.default_backend).lower() + backend: InferenceBackend | None = None + try: + backend = self.resolve_backend(selected_name) + return backend.predict(request_obj=request_obj, results_dir=results_dir) + except Exception as exc: + if fallback_to_local and selected_name != "local": + local_backend = self._backends["local"] + local_result = local_backend.predict(request_obj=request_obj, results_dir=results_dir) + metadata = dict(local_result.metadata) + metadata["fallback_from"] = selected_name + metadata["fallback_reason"] = str(exc) + return BackendPredictionResult( + backend_name=local_result.backend_name, + output_file=local_result.output_file, + metadata=metadata, + ) + raise + + def readiness(self) -> dict[str, Any]: + backends = {name: backend.readiness() for name, backend in self._backends.items()} + default_state = backends[self.settings.default_backend] + return { + "default_backend": self.settings.default_backend, + "ready": bool(default_state.get("ready", False)), + "backends": backends, + "fallback_to_local_enabled": _env_flag("CATPRED_MODAL_FALLBACK_TO_LOCAL", default=False), + } diff --git a/catpred/inference/service.py b/catpred/inference/service.py index 99f768d..ae8a875 100644 --- a/catpred/inference/service.py +++ b/catpred/inference/service.py @@ -1,6 +1,5 @@ from __future__ import annotations -from dataclasses import dataclass from pathlib import Path import os import subprocess @@ -10,6 +9,7 @@ import pandas as pd from rdkit import Chem +from .types import PreparedInputPaths, PredictionRequest _VALID_PARAMETERS = {"kcat", "km", "ki"} _TARGET_COLUMNS = { @@ -20,23 +20,6 @@ _VALID_AAS = set("ACDEFGHIKLMNPQRSTVWY") -@dataclass(frozen=True) -class PredictionRequest: - parameter: str - input_file: str - checkpoint_dir: str - use_gpu: bool = False - repo_root: str | None = None - python_executable: str = "python" - - -@dataclass(frozen=True) -class PreparedInputPaths: - input_csv: str - records_file: str - output_csv: str - - def _validate_parameter(parameter: str) -> str: parameter = parameter.lower() if parameter not in _VALID_PARAMETERS: diff --git a/catpred/inference/types.py b/catpred/inference/types.py new file mode 100644 index 0000000..51c4857 --- /dev/null +++ b/catpred/inference/types.py @@ -0,0 +1,20 @@ +from __future__ import annotations + +from dataclasses import dataclass + + +@dataclass(frozen=True) +class PredictionRequest: + parameter: str + input_file: str + checkpoint_dir: str + use_gpu: bool = False + repo_root: str | None = None + python_executable: str = "python" + + +@dataclass(frozen=True) +class PreparedInputPaths: + input_csv: str + records_file: str + output_csv: str diff --git a/catpred/web/__init__.py b/catpred/web/__init__.py new file mode 100644 index 0000000..e4dd907 --- /dev/null +++ b/catpred/web/__init__.py @@ -0,0 +1,3 @@ +"""Web API interface for CatPred inference.""" + +__all__ = ["app", "run"] diff --git a/catpred/web/app.py b/catpred/web/app.py new file mode 100644 index 0000000..01cbc0a --- /dev/null +++ b/catpred/web/app.py @@ -0,0 +1,160 @@ +from __future__ import annotations + +from pathlib import Path +import os +import subprocess +import tempfile +from typing import Any, Optional + +import pandas as pd + +try: + from fastapi import FastAPI, HTTPException + from pydantic import BaseModel, Field, root_validator +except ImportError as exc: # pragma: no cover - import guard for optional dependency + raise ImportError( + "catpred.web requires optional dependencies. Install with `pip install .[web]`." + ) from exc + +from catpred.inference import ( + InferenceBackendError, + InferenceBackendRouter, + PredictionRequest, +) + + +def _env_flag(name: str, default: bool = False) -> bool: + value = os.environ.get(name) + if value is None: + return default + return value.strip().lower() in {"1", "true", "yes", "y", "on"} + + +class PredictRequest(BaseModel): + parameter: str = Field(..., description="One of: kcat, km, ki") + checkpoint_dir: str = Field(..., description="Path to the checkpoint directory") + input_file: Optional[str] = Field(default=None, description="Path to input CSV") + input_rows: Optional[list[dict[str, Any]]] = Field( + default=None, + description="Optional in-request CSV rows. Use this for remote clients.", + ) + use_gpu: bool = False + backend: Optional[str] = Field(default=None, description="Override backend (local|modal)") + fallback_to_local: Optional[bool] = Field(default=None, description="Fallback if modal fails") + results_dir: str = Field(default="../results", description="Directory for final predictions") + repo_root: Optional[str] = Field(default=None, description="Repository root path") + python_executable: str = Field(default="python", description="Python executable to run scripts") + + @root_validator + def _validate_input_source(cls, values: dict[str, Any]) -> dict[str, Any]: + has_file = bool(values.get("input_file")) + has_rows = bool(values.get("input_rows")) + if has_file == has_rows: + raise ValueError("Provide exactly one of `input_file` or `input_rows`.") + return values + + +class PredictResponse(BaseModel): + backend: str + output_file: str + row_count: int + preview_rows: list[dict[str, Any]] + metadata: dict[str, Any] = Field(default_factory=dict) + + +def _resolve_repo_root(repo_root: Optional[str]) -> Path: + if repo_root: + return Path(repo_root).resolve() + env_repo_root = os.environ.get("CATPRED_REPO_ROOT") + if env_repo_root: + return Path(env_repo_root).resolve() + return Path.cwd().resolve() + + +def _write_rows_to_temp_csv(rows: list[dict[str, Any]], repo_root: Path) -> tuple[str, str]: + if len(rows) == 0: + raise ValueError("`input_rows` cannot be empty.") + + tmp_dir = repo_root / ".e2e-tests" / "api_tmp" + tmp_dir.mkdir(parents=True, exist_ok=True) + fd, tmp_path = tempfile.mkstemp(prefix="api_input_", suffix=".csv", dir=str(tmp_dir)) + os.close(fd) + pd.DataFrame(rows).to_csv(tmp_path, index=False) + return tmp_path, tmp_path + + +def _resolve_input_file(payload: PredictRequest) -> tuple[str, Optional[str]]: + if payload.input_file: + return payload.input_file, None + + repo_root = _resolve_repo_root(payload.repo_root) + return _write_rows_to_temp_csv(payload.input_rows or [], repo_root) + + +def _preview_output(output_file: str, preview_limit: int = 5) -> tuple[int, list[dict[str, Any]]]: + df = pd.read_csv(output_file) + return len(df), df.head(preview_limit).to_dict(orient="records") + + +def create_app(router: Optional[InferenceBackendRouter] = None) -> FastAPI: + app = FastAPI(title="CatPred API", version="0.1.0") + backend_router = router or InferenceBackendRouter() + default_fallback = _env_flag("CATPRED_MODAL_FALLBACK_TO_LOCAL", default=False) + + @app.get("/health") + def health() -> dict[str, str]: + return {"status": "ok"} + + @app.get("/ready") + def ready() -> dict[str, Any]: + return backend_router.readiness() + + @app.post("/predict", response_model=PredictResponse) + def predict(payload: PredictRequest) -> PredictResponse: + input_file, temp_file = _resolve_input_file(payload) + try: + request_obj = PredictionRequest( + parameter=payload.parameter.lower(), + input_file=input_file, + checkpoint_dir=payload.checkpoint_dir, + use_gpu=payload.use_gpu, + repo_root=payload.repo_root, + python_executable=payload.python_executable, + ) + + fallback = payload.fallback_to_local + if fallback is None: + fallback = default_fallback + + result = backend_router.predict( + request_obj=request_obj, + results_dir=payload.results_dir, + backend_name=payload.backend, + fallback_to_local=fallback, + ) + + row_count, preview_rows = _preview_output(result.output_file) + return PredictResponse( + backend=result.backend_name, + output_file=result.output_file, + row_count=row_count, + preview_rows=preview_rows, + metadata=result.metadata, + ) + except ValueError as exc: + raise HTTPException(status_code=400, detail=str(exc)) from exc + except (FileNotFoundError, InferenceBackendError) as exc: + raise HTTPException(status_code=400, detail=str(exc)) from exc + except subprocess.CalledProcessError as exc: + raise HTTPException( + status_code=500, + detail=f"Prediction command failed with exit code {exc.returncode}.", + ) from exc + finally: + if temp_file and Path(temp_file).exists(): + Path(temp_file).unlink() + + return app + + +app = create_app() diff --git a/catpred/web/run.py b/catpred/web/run.py new file mode 100644 index 0000000..41fc261 --- /dev/null +++ b/catpred/web/run.py @@ -0,0 +1,28 @@ +from __future__ import annotations + +import argparse + + +def main(argv: list[str] | None = None) -> int: + parser = argparse.ArgumentParser(description="Run CatPred web API server.") + parser.add_argument("--host", default="0.0.0.0", help="Host interface to bind") + parser.add_argument("--port", type=int, default=8000, help="Port to bind") + parser.add_argument("--reload", action="store_true", help="Enable auto-reload for development") + parser.add_argument("--workers", type=int, default=1, help="Number of worker processes") + args = parser.parse_args(argv) + + try: + import uvicorn + except ImportError: + print("uvicorn is not installed. Install optional web dependencies with `pip install .[web]`.") + return 1 + + from .app import create_app + + app = create_app() + uvicorn.run(app, host=args.host, port=args.port, reload=args.reload, workers=args.workers) + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/setup.cfg b/setup.cfg index d75df80..4557b02 100644 --- a/setup.cfg +++ b/setup.cfg @@ -51,9 +51,13 @@ console_scripts = catpred_train=catpred.train:catpred_train catpred_predict=catpred.train:catpred_predict catpred_fingerprint=catpred.train:catpred_fingerprint + catpred_web=catpred.web.run:main [options.extras_require] test = pytest>=6.2.2; parameterized>=0.8.1 +web = + fastapi>=0.95,<1.0 + uvicorn>=0.22,<1.0 [options.package_data] catpred = py.typed diff --git a/setup.py b/setup.py index e7d62bf..5502b99 100644 --- a/setup.py +++ b/setup.py @@ -29,6 +29,7 @@ "catpred_train=catpred.train:catpred_train", "catpred_predict=catpred.train:catpred_predict", "catpred_fingerprint=catpred.train:catpred_fingerprint", + "catpred_web=catpred.web.run:main", ] }, install_requires=[ @@ -46,6 +47,12 @@ "scipy<1.11 ; python_version=='3.7'", "scipy>=1.9 ; python_version=='3.8'", ], + extras_require={ + "web": [ + "fastapi>=0.95,<1.0", + "uvicorn>=0.22,<1.0", + ] + }, python_requires=">=3.7", classifiers=[ "Programming Language :: Python :: 3", From 7d1167289485a3688e67603ade082a44a27116cc Mon Sep 17 00:00:00 2001 From: theproteinbot Date: Sat, 28 Feb 2026 16:12:41 -0500 Subject: [PATCH 06/34] Phase 3.1: harden API request surface and path guardrails --- README.md | 29 ++++++- catpred/web/app.py | 192 +++++++++++++++++++++++++++++++++++++++------ 2 files changed, 195 insertions(+), 26 deletions(-) diff --git a/README.md b/README.md index 0e9d6d9..c185b4b 100644 --- a/README.md +++ b/README.md @@ -122,7 +122,12 @@ Endpoints: - `GET /ready` โ€” backend configuration/readiness. - `POST /predict` โ€” run inference. -Minimal `POST /predict` example for local inference: +By default, the API is hardened for service use: +- `input_file` requests are disabled (use `input_rows` instead). +- request-time overrides of `repo_root` / `python_executable` are disabled. +- `results_dir` is constrained under `CATPRED_API_RESULTS_ROOT`. + +Minimal `POST /predict` example for local inference using `input_rows`: ```bash curl -X POST http://127.0.0.1:8000/predict \ @@ -130,7 +135,11 @@ curl -X POST http://127.0.0.1:8000/predict \ -d '{ "parameter": "kcat", "checkpoint_dir": "../data/pretrained/reproduce_checkpoints/kcat", - "input_file": "./demo/batch_kcat_pred.csv", + "input_rows": [ + {"SMILES": "CCO", "sequence": "ACDEFGHIK", "pdbpath": "seq_a"}, + {"SMILES": "CCN", "sequence": "LMNPQRSTV", "pdbpath": "seq_b"} + ], + "results_dir": "batch1", "backend": "local" }' ``` @@ -146,6 +155,22 @@ export CATPRED_MODAL_FALLBACK_TO_LOCAL=1 Use `"backend": "modal"` in `/predict` requests to route through Modal. If fallback is enabled (env var above or request field `fallback_to_local`), failed modal requests can automatically reroute to local inference. +Optional API environment variables: + +```bash +# Root directories used by API path constraints +export CATPRED_API_INPUT_ROOT="/absolute/path/for/input-csvs" +export CATPRED_API_RESULTS_ROOT="/absolute/path/for/results" + +# Enable only for trusted local workflows (not recommended for public deployments) +export CATPRED_API_ALLOW_INPUT_FILE=1 +export CATPRED_API_ALLOW_UNSAFE_OVERRIDES=1 + +# Request limits +export CATPRED_API_MAX_INPUT_ROWS=1000 +export CATPRED_API_MAX_INPUT_FILE_BYTES=5000000 +``` + ### ๐Ÿงช Fine-Tuning On Custom Data You can fine-tune CatPred on your own regression targets using `train.py`. diff --git a/catpred/web/app.py b/catpred/web/app.py index 01cbc0a..ed1081f 100644 --- a/catpred/web/app.py +++ b/catpred/web/app.py @@ -1,8 +1,10 @@ from __future__ import annotations +from dataclasses import dataclass from pathlib import Path import os import subprocess +import sys import tempfile from typing import Any, Optional @@ -30,6 +32,38 @@ def _env_flag(name: str, default: bool = False) -> bool: return value.strip().lower() in {"1", "true", "yes", "y", "on"} +@dataclass(frozen=True) +class APISettings: + repo_root: str + python_executable: str + input_root: str + results_root: str + allow_input_file: bool = False + allow_unsafe_request_overrides: bool = False + max_input_rows: int = 1000 + max_input_file_bytes: int = 5_000_000 + preview_rows: int = 5 + + @classmethod + def from_env(cls) -> "APISettings": + env_repo_root = os.environ.get("CATPRED_REPO_ROOT") + repo_root = str(Path(env_repo_root).resolve()) if env_repo_root else str(Path.cwd().resolve()) + input_root = os.environ.get("CATPRED_API_INPUT_ROOT") + results_root = os.environ.get("CATPRED_API_RESULTS_ROOT") + + return cls( + repo_root=repo_root, + python_executable=os.environ.get("CATPRED_PYTHON_EXECUTABLE", sys.executable or "python"), + input_root=str(Path(input_root).resolve()) if input_root else str((Path(repo_root) / "inputs").resolve()), + results_root=str(Path(results_root).resolve()) if results_root else str((Path(repo_root) / "results").resolve()), + allow_input_file=_env_flag("CATPRED_API_ALLOW_INPUT_FILE", default=False), + allow_unsafe_request_overrides=_env_flag("CATPRED_API_ALLOW_UNSAFE_OVERRIDES", default=False), + max_input_rows=max(int(os.environ.get("CATPRED_API_MAX_INPUT_ROWS", "1000")), 1), + max_input_file_bytes=max(int(os.environ.get("CATPRED_API_MAX_INPUT_FILE_BYTES", "5000000")), 1024), + preview_rows=max(int(os.environ.get("CATPRED_API_PREVIEW_ROWS", "5")), 1), + ) + + class PredictRequest(BaseModel): parameter: str = Field(..., description="One of: kcat, km, ki") checkpoint_dir: str = Field(..., description="Path to the checkpoint directory") @@ -41,9 +75,18 @@ class PredictRequest(BaseModel): use_gpu: bool = False backend: Optional[str] = Field(default=None, description="Override backend (local|modal)") fallback_to_local: Optional[bool] = Field(default=None, description="Fallback if modal fails") - results_dir: str = Field(default="../results", description="Directory for final predictions") - repo_root: Optional[str] = Field(default=None, description="Repository root path") - python_executable: str = Field(default="python", description="Python executable to run scripts") + results_dir: str = Field( + default="results", + description="Results subdirectory under CATPRED_API_RESULTS_ROOT.", + ) + repo_root: Optional[str] = Field( + default=None, + description="Unsafe override. Disabled by default; only for trusted local workflows.", + ) + python_executable: Optional[str] = Field( + default=None, + description="Unsafe override. Disabled by default; only for trusted local workflows.", + ) @root_validator def _validate_input_source(cls, values: dict[str, Any]) -> dict[str, Any]: @@ -62,20 +105,96 @@ class PredictResponse(BaseModel): metadata: dict[str, Any] = Field(default_factory=dict) -def _resolve_repo_root(repo_root: Optional[str]) -> Path: - if repo_root: +def _is_subpath(path: Path, root: Path) -> bool: + try: + path.relative_to(root) + return True + except ValueError: + return False + + +def _resolve_repo_root(repo_root: Optional[str], settings: APISettings) -> Path: + if repo_root is not None: + if not settings.allow_unsafe_request_overrides: + raise ValueError( + "Request field `repo_root` is disabled. " + "Set CATPRED_API_ALLOW_UNSAFE_OVERRIDES=1 for trusted local use." + ) return Path(repo_root).resolve() - env_repo_root = os.environ.get("CATPRED_REPO_ROOT") - if env_repo_root: - return Path(env_repo_root).resolve() - return Path.cwd().resolve() + return Path(settings.repo_root).resolve() + +def _resolve_python_executable(python_executable: Optional[str], settings: APISettings) -> str: + if python_executable is not None: + if not settings.allow_unsafe_request_overrides: + raise ValueError( + "Request field `python_executable` is disabled. " + "Set CATPRED_API_ALLOW_UNSAFE_OVERRIDES=1 for trusted local use." + ) + return python_executable + return settings.python_executable -def _write_rows_to_temp_csv(rows: list[dict[str, Any]], repo_root: Path) -> tuple[str, str]: + +def _resolve_and_validate_path_under_root(raw_path: str, root: Path, purpose: str) -> Path: + candidate = Path(raw_path) + resolved = candidate.resolve() if candidate.is_absolute() else (root / candidate).resolve() + if not _is_subpath(resolved, root): + raise ValueError(f"{purpose} must stay under configured root: {root}") + return resolved + + +def _resolve_results_dir(raw_results_dir: str, settings: APISettings) -> str: + results_root = Path(settings.results_root).resolve() + results_root.mkdir(parents=True, exist_ok=True) + resolved = _resolve_and_validate_path_under_root( + raw_path=raw_results_dir, + root=results_root, + purpose="results_dir", + ) + resolved.mkdir(parents=True, exist_ok=True) + return str(resolved) + + +def _resolve_input_file_path(input_file: str, settings: APISettings) -> Path: + if not settings.allow_input_file: + raise ValueError( + "Request field `input_file` is disabled. Submit `input_rows` instead, " + "or set CATPRED_API_ALLOW_INPUT_FILE=1 for trusted local use." + ) + + input_root = Path(settings.input_root).resolve() + input_root.mkdir(parents=True, exist_ok=True) + resolved = _resolve_and_validate_path_under_root( + raw_path=input_file, + root=input_root, + purpose="input_file", + ) + + if not resolved.exists(): + raise FileNotFoundError(f'Input CSV not found: "{resolved}"') + if not resolved.is_file(): + raise ValueError(f'Input CSV path is not a file: "{resolved}"') + if resolved.stat().st_size > settings.max_input_file_bytes: + raise ValueError( + f'Input file exceeds CATPRED_API_MAX_INPUT_FILE_BYTES ({settings.max_input_file_bytes}).' + ) + + return resolved + + +def _write_rows_to_temp_csv( + rows: list[dict[str, Any]], + settings: APISettings, + repo_root: Path, +) -> tuple[str, str]: if len(rows) == 0: raise ValueError("`input_rows` cannot be empty.") + if len(rows) > settings.max_input_rows: + raise ValueError( + f"`input_rows` exceeds CATPRED_API_MAX_INPUT_ROWS ({settings.max_input_rows})." + ) - tmp_dir = repo_root / ".e2e-tests" / "api_tmp" + tmp_dir = (repo_root.resolve() / ".e2e-tests" / "api_tmp").resolve() tmp_dir.mkdir(parents=True, exist_ok=True) fd, tmp_path = tempfile.mkstemp(prefix="api_input_", suffix=".csv", dir=str(tmp_dir)) os.close(fd) @@ -83,21 +202,29 @@ def _write_rows_to_temp_csv(rows: list[dict[str, Any]], repo_root: Path) -> tupl return tmp_path, tmp_path -def _resolve_input_file(payload: PredictRequest) -> tuple[str, Optional[str]]: +def _resolve_input_file( + payload: PredictRequest, + settings: APISettings, + repo_root: Path, +) -> tuple[str, Optional[str]]: if payload.input_file: - return payload.input_file, None + resolved = _resolve_input_file_path(payload.input_file, settings) + return str(resolved), None - repo_root = _resolve_repo_root(payload.repo_root) - return _write_rows_to_temp_csv(payload.input_rows or [], repo_root) + return _write_rows_to_temp_csv(payload.input_rows or [], settings=settings, repo_root=repo_root) -def _preview_output(output_file: str, preview_limit: int = 5) -> tuple[int, list[dict[str, Any]]]: +def _preview_output(output_file: str, preview_limit: int) -> tuple[int, list[dict[str, Any]]]: df = pd.read_csv(output_file) return len(df), df.head(preview_limit).to_dict(orient="records") -def create_app(router: Optional[InferenceBackendRouter] = None) -> FastAPI: - app = FastAPI(title="CatPred API", version="0.1.0") +def create_app( + router: Optional[InferenceBackendRouter] = None, + settings: Optional[APISettings] = None, +) -> FastAPI: + api_settings = settings or APISettings.from_env() + app = FastAPI(title="CatPred API", version="0.2.0") backend_router = router or InferenceBackendRouter() default_fallback = _env_flag("CATPRED_MODAL_FALLBACK_TO_LOCAL", default=False) @@ -107,19 +234,33 @@ def health() -> dict[str, str]: @app.get("/ready") def ready() -> dict[str, Any]: - return backend_router.readiness() + readiness = backend_router.readiness() + readiness["api"] = { + "allow_input_file": api_settings.allow_input_file, + "allow_unsafe_request_overrides": api_settings.allow_unsafe_request_overrides, + "max_input_rows": api_settings.max_input_rows, + "max_input_file_bytes": api_settings.max_input_file_bytes, + "input_root": str(Path(api_settings.input_root).resolve()), + "results_root": str(Path(api_settings.results_root).resolve()), + } + return readiness @app.post("/predict", response_model=PredictResponse) def predict(payload: PredictRequest) -> PredictResponse: - input_file, temp_file = _resolve_input_file(payload) + temp_file: Optional[str] = None try: + repo_root = _resolve_repo_root(payload.repo_root, api_settings) + python_executable = _resolve_python_executable(payload.python_executable, api_settings) + input_file, temp_file = _resolve_input_file(payload, settings=api_settings, repo_root=repo_root) + safe_results_dir = _resolve_results_dir(payload.results_dir, api_settings) + request_obj = PredictionRequest( parameter=payload.parameter.lower(), input_file=input_file, checkpoint_dir=payload.checkpoint_dir, use_gpu=payload.use_gpu, - repo_root=payload.repo_root, - python_executable=payload.python_executable, + repo_root=str(repo_root), + python_executable=python_executable, ) fallback = payload.fallback_to_local @@ -128,12 +269,15 @@ def predict(payload: PredictRequest) -> PredictResponse: result = backend_router.predict( request_obj=request_obj, - results_dir=payload.results_dir, + results_dir=safe_results_dir, backend_name=payload.backend, fallback_to_local=fallback, ) - row_count, preview_rows = _preview_output(result.output_file) + row_count, preview_rows = _preview_output( + result.output_file, + preview_limit=api_settings.preview_rows, + ) return PredictResponse( backend=result.backend_name, output_file=result.output_file, From cd6f22a0f413626916ed58c55ce8ed275cdc28b4 Mon Sep 17 00:00:00 2001 From: theproteinbot Date: Sat, 28 Feb 2026 16:13:41 -0500 Subject: [PATCH 07/34] Phase 3.2: unify packaging metadata via setup.cfg --- setup.cfg | 8 +++---- setup.py | 72 ++++--------------------------------------------------- 2 files changed, 8 insertions(+), 72 deletions(-) diff --git a/setup.cfg b/setup.cfg index 4557b02..3db702f 100644 --- a/setup.cfg +++ b/setup.cfg @@ -1,10 +1,10 @@ [metadata] name = catpred version = 0.0.1 -author = -author_email = +author = Veda Sheersh Boorla, Costas D. Maranas +author_email = mailforveda@gmail.com license = MIT -description = +description = A comprehensive framework for deep learning in vitro enzyme kinetic parameters kcat, Km and Ki keywords = protein language model machine learning @@ -21,7 +21,7 @@ classifiers = License :: OSI Approved :: MIT License Operating System :: OS Independent project_urls = - Documentation = + Documentation = https://github.com/maranasgroup/catpred/ Source = https://github.com/maranasgroup/catpred PyPi = Demo = http://tiny.cc/catpred diff --git a/setup.py b/setup.py index 5502b99..b357122 100644 --- a/setup.py +++ b/setup.py @@ -1,70 +1,6 @@ -from setuptools import find_packages, setup +from setuptools import setup -__version__ = "0.0.1" -# Load README -with open("README.md", encoding="utf-8") as f: - long_description = f.read() - -setup( - name="catpred", - author="Veda Sheersh Boorla, Costas D. Maranas", - author_email="mailforveda@gmail.com", - description="A comprehensive framework for deep learning in vitro enzyme kinetic parameters kcat, Km and Ki", - long_description=long_description, - long_description_content_type="text/markdown", - url="https://github.com/maranasgroup/catpred", - download_url=f"https://github.com/maranasgroup/catpred/v_{__version__}.tar.gz", - project_urls={ - "Documentation": "https://github.com/maranasgroup/catpred/", - "Source": "https://github.com/maranasgroup/catpred", - "PyPi": "", - "Demo": "https://tiny.cc/catpred", - }, - license="MIT", - packages=find_packages(), - package_data={"catpred": ["py.typed"]}, - entry_points={ - "console_scripts": [ - "catpred_train=catpred.train:catpred_train", - "catpred_predict=catpred.train:catpred_predict", - "catpred_fingerprint=catpred.train:catpred_fingerprint", - "catpred_web=catpred.web.run:main", - ] - }, - install_requires=[ - "matplotlib>=3.1.3", - "numpy>=1.18.1", - "pandas>=1.0.3", - "pandas-flavor>=0.2.0", - "scikit-learn>=0.22.2.post1", - "tensorboardX>=2.0", - "sphinx>=3.1.2", - "torch>=1.4.0", - "tqdm>=4.45.0", - "typed-argument-parser>=1.6.1", - "rdkit>=2020.03.1.0", - "scipy<1.11 ; python_version=='3.7'", - "scipy>=1.9 ; python_version=='3.8'", - ], - extras_require={ - "web": [ - "fastapi>=0.95,<1.0", - "uvicorn>=0.22,<1.0", - ] - }, - python_requires=">=3.7", - classifiers=[ - "Programming Language :: Python :: 3", - "Programming Language :: Python :: 3.7", - "Programming Language :: Python :: 3.8", - "License :: OSI Approved :: MIT License", - "Operating System :: OS Independent", - ], - keywords=[ - "bioinformatics", - "machine learning", - "enzyme function prediction", - "message passing neural network", - ], -) +if __name__ == "__main__": + # Keep setup.py as a thin shim so setup.cfg is the single source of packaging metadata. + setup() From 84f3e424ea9aa2414ce73b9cd1d708ce330b4bdd Mon Sep 17 00:00:00 2001 From: theproteinbot Date: Sat, 28 Feb 2026 17:18:53 -0500 Subject: [PATCH 08/34] Phase 3.3: centralize deserialization policy and enforce trusted roots --- README.md | 12 ++ catpred/__init__.py | 1 + catpred/args.py | 8 +- catpred/data/cache_utils.py | 14 +-- catpred/data/utils.py | 20 ++-- catpred/features/utils.py | 6 +- catpred/models/model.py | 13 +-- catpred/security/__init__.py | 17 +++ catpred/security/deserialization.py | 168 ++++++++++++++++++++++++++++ catpred/utils.py | 39 ++++--- catpred/web/app.py | 37 +++++- scripts/baseline_analysis.py | 10 +- 12 files changed, 288 insertions(+), 57 deletions(-) create mode 100644 catpred/security/__init__.py create mode 100644 catpred/security/deserialization.py diff --git a/README.md b/README.md index c185b4b..c0ac109 100644 --- a/README.md +++ b/README.md @@ -161,6 +161,7 @@ Optional API environment variables: # Root directories used by API path constraints export CATPRED_API_INPUT_ROOT="/absolute/path/for/input-csvs" export CATPRED_API_RESULTS_ROOT="/absolute/path/for/results" +export CATPRED_API_CHECKPOINT_ROOT="/absolute/path/for/checkpoints" # Enable only for trusted local workflows (not recommended for public deployments) export CATPRED_API_ALLOW_INPUT_FILE=1 @@ -171,6 +172,17 @@ export CATPRED_API_MAX_INPUT_ROWS=1000 export CATPRED_API_MAX_INPUT_FILE_BYTES=5000000 ``` +Deserialization hardening controls: + +```bash +# Trusted roots used by secure loaders (colon-separated list on Unix) +export CATPRED_TRUSTED_DESERIALIZATION_ROOTS="/srv/catpred:/srv/catpred-data" + +# Backward-compatible default is enabled (1). Set to 0 to block unsafe pickle-based loading. +# Use 0 only after validating your artifacts are safe-load compatible. +export CATPRED_ALLOW_UNSAFE_DESERIALIZATION=1 +``` + ### ๐Ÿงช Fine-Tuning On Custom Data You can fine-tune CatPred on your own regression targets using `train.py`. diff --git a/catpred/__init__.py b/catpred/__init__.py index 92cfc40..75b676b 100644 --- a/catpred/__init__.py +++ b/catpred/__init__.py @@ -13,6 +13,7 @@ "models", "nn_utils", "rdkit", + "security", "train", "uncertainty", "utils", diff --git a/catpred/args.py b/catpred/args.py index d52b198..99cc1b3 100644 --- a/catpred/args.py +++ b/catpred/args.py @@ -1,7 +1,6 @@ import json import os from tempfile import TemporaryDirectory -import pickle from typing import List, Optional from typing_extensions import Literal from packaging import version @@ -15,6 +14,7 @@ import catpred.data.utils from catpred.data import set_cache_mol, empty_cache from catpred.features import get_available_features_generators +from catpred.security import load_index_artifact Metric = Literal['auc', 'prc-auc', 'rmse', 'mae', 'mse', 'r2', 'accuracy', 'cross_entropy', 'binary_cross_entropy', 'sid', 'wasserstein', 'f1', 'mcc', 'bounded_rmse', 'bounded_mae', 'bounded_mse'] @@ -815,8 +815,10 @@ def process_args(self) -> None: raise ValueError('When using crossval or index_predetermined split type, must provide crossval_index_file.') if self.split_type in ['crossval', 'index_predetermined']: - with open(self.crossval_index_file, 'rb') as rf: - self._crossval_index_sets = pickle.load(rf) + self._crossval_index_sets = load_index_artifact( + self.crossval_index_file, + purpose="cross-validation index file", + ) self.num_folds = len(self.crossval_index_sets) self.seed = 0 diff --git a/catpred/data/cache_utils.py b/catpred/data/cache_utils.py index 90cac3a..a72812d 100644 --- a/catpred/data/cache_utils.py +++ b/catpred/data/cache_utils.py @@ -4,6 +4,7 @@ import hashlib from functools import wraps from pathlib import Path +from catpred.security import load_torch_artifact def exists(val): return val is not None @@ -27,13 +28,6 @@ def md5_hash_fn(s): encoded = s.encode('utf-8') return hashlib.md5(encoded).hexdigest() - -def _torch_load_compat(path): - try: - return torch.load(path, weights_only=False) - except TypeError: - return torch.load(path) - # run once function GLOBAL_RUN_RECORDS = dict() @@ -99,7 +93,11 @@ def inner(t, *args, __cache_key = None, **kwargs): if entry_path.exists(): log(f'cache hit: fetching {t} from {str(entry_path)}') - return _torch_load_compat(str(entry_path)) + return load_torch_artifact( + str(entry_path), + purpose="esm cache entry", + roots=[CACHE_PATH], + ) out = fn(t, *args, **kwargs) diff --git a/catpred/data/utils.py b/catpred/data/utils.py index 34f03be..ad23098 100644 --- a/catpred/data/utils.py +++ b/catpred/data/utils.py @@ -3,7 +3,6 @@ import csv import ctypes from logging import Logger -import pickle from random import Random from typing import List, Set, Tuple, Union import os @@ -22,6 +21,7 @@ from catpred.args import PredictArgs, TrainArgs from catpred.features import load_features, load_valid_atom_or_bond_features, is_mol from catpred.rdkit import make_mol +from catpred.security import load_index_artifact, load_pickle_artifact # Increase maximum size of field in the csv processing for the current architecture csv.field_size_limit(int(ctypes.c_ulong(-1).value // 2)) @@ -791,8 +791,12 @@ def split_data(data: MoleculeDataset, for split in range(3): split_indices = [] for index in index_set[split]: - with open(os.path.join(args.crossval_index_dir, f'{index}.pkl'), 'rb') as rf: - split_indices.extend(pickle.load(rf)) + split_indices.extend( + load_index_artifact( + os.path.join(args.crossval_index_dir, f"{index}.pkl"), + purpose="cross-validation fold index file", + ) + ) data_split.append([data[i] for i in split_indices]) train, val, test = tuple(data_split) return MoleculeDataset(train), MoleculeDataset(val), MoleculeDataset(test) @@ -841,12 +845,10 @@ def split_data(data: MoleculeDataset, if test_fold_index is None: raise ValueError('arg "test_fold_index" can not be None!') - try: - with open(folds_file, 'rb') as f: - all_fold_indices = pickle.load(f) - except UnicodeDecodeError: - with open(folds_file, 'rb') as f: - all_fold_indices = pickle.load(f, encoding='latin1') # in case we're loading indices from python2 + all_fold_indices = load_pickle_artifact( + folds_file, + purpose="predetermined folds file", + ) log_scaffold_stats(data, all_fold_indices, logger=logger) diff --git a/catpred/features/utils.py b/catpred/features/utils.py index 617af86..c0e24dd 100644 --- a/catpred/features/utils.py +++ b/catpred/features/utils.py @@ -1,11 +1,11 @@ import csv import os -import pickle from typing import List import numpy as np import pandas as pd from rdkit.Chem import PandasTools +from catpred.security import load_pickle_artifact def save_features(path: str, features: List[np.ndarray]) -> None: @@ -49,8 +49,8 @@ def load_features(path: str) -> np.ndarray: next(reader) # skip header features = np.array([[float(value) for value in row] for row in reader]) elif extension in ['.pkl', '.pckl', '.pickle']: - with open(path, 'rb') as f: - features = np.array([np.squeeze(np.array(feat.todense())) for feat in pickle.load(f)]) + payload = load_pickle_artifact(path, purpose="feature matrix pickle") + features = np.array([np.squeeze(np.array(feat.todense())) for feat in payload]) else: raise ValueError(f'Features path extension {extension} not supported.') diff --git a/catpred/models/model.py b/catpred/models/model.py index 5b50099..16e5c6f 100644 --- a/catpred/models/model.py +++ b/catpred/models/model.py @@ -13,13 +13,7 @@ from catpred.args import TrainArgs from catpred.features import BatchMolGraph from catpred.nn_utils import initialize_weights - - -def _torch_load_compat(path): - try: - return torch.load(path, weights_only=False) - except TypeError: - return torch.load(path) +from catpred.security import load_torch_artifact def exists(val): @@ -126,7 +120,10 @@ def create_protein_model(self, args: TrainArgs) -> None: self.seq_embedder = nn.Embedding(21, args.seq_embed_dim, padding_idx=20) #last index is for padding if self.args.add_pretrained_egnn_feats: - self.pretrained_egnn_feats_dict = _torch_load_compat(self.args.pretrained_egnn_feats_path) + self.pretrained_egnn_feats_dict = load_torch_artifact( + self.args.pretrained_egnn_feats_path, + purpose="pretrained EGNN features", + ) x = list(self.pretrained_egnn_feats_dict.values()) self.pretrained_egnn_feats_avg = torch.stack(x).mean(dim=0) diff --git a/catpred/security/__init__.py b/catpred/security/__init__.py new file mode 100644 index 0000000..8c5f371 --- /dev/null +++ b/catpred/security/__init__.py @@ -0,0 +1,17 @@ +from .deserialization import ( + DeserializationSecurityError, + ensure_trusted_path, + load_index_artifact, + load_pickle_artifact, + load_torch_artifact, + unsafe_deserialization_enabled, +) + +__all__ = [ + "DeserializationSecurityError", + "ensure_trusted_path", + "load_index_artifact", + "load_pickle_artifact", + "load_torch_artifact", + "unsafe_deserialization_enabled", +] diff --git a/catpred/security/deserialization.py b/catpred/security/deserialization.py new file mode 100644 index 0000000..a3fc8f9 --- /dev/null +++ b/catpred/security/deserialization.py @@ -0,0 +1,168 @@ +from __future__ import annotations + +import gzip +import json +import os +import pickle +from pathlib import Path +from typing import Any, Iterable + + +class DeserializationSecurityError(RuntimeError): + """Raised when deserialization is blocked by policy.""" + + +_ALLOW_UNSAFE_ENV = "CATPRED_ALLOW_UNSAFE_DESERIALIZATION" +_TRUSTED_ROOTS_ENV = "CATPRED_TRUSTED_DESERIALIZATION_ROOTS" + + +def _env_flag(name: str, default: bool = False) -> bool: + raw = os.environ.get(name) + if raw is None: + return default + return raw.strip().lower() in {"1", "true", "yes", "y", "on"} + + +def _dedupe_paths(paths: list[Path]) -> list[Path]: + unique: list[Path] = [] + seen = set() + for path in paths: + resolved = path.resolve() + key = str(resolved) + if key not in seen: + unique.append(resolved) + seen.add(key) + return unique + + +def _default_trusted_roots() -> list[Path]: + raw = os.environ.get(_TRUSTED_ROOTS_ENV) + if raw: + candidates = [Path(item) for item in raw.split(os.pathsep) if item.strip()] + else: + cwd = Path.cwd().resolve() + candidates = [cwd, cwd.parent] + return _dedupe_paths(candidates) + + +def trusted_roots(extra_roots: Iterable[str | Path] | None = None) -> list[Path]: + roots = _default_trusted_roots() + if extra_roots: + roots.extend(Path(path) for path in extra_roots) + return _dedupe_paths(roots) + + +def is_trusted_path(path: str | Path, roots: Iterable[str | Path] | None = None) -> bool: + resolved = Path(path).resolve() + candidate_roots = trusted_roots(roots) + for root in candidate_roots: + try: + resolved.relative_to(root) + return True + except ValueError: + continue + return False + + +def ensure_trusted_path( + path: str | Path, + *, + purpose: str, + roots: Iterable[str | Path] | None = None, +) -> Path: + resolved = Path(path).resolve() + candidate_roots = trusted_roots(roots) + if is_trusted_path(resolved, candidate_roots): + return resolved + roots_display = ", ".join(str(item) for item in candidate_roots) + raise DeserializationSecurityError( + f'Refusing to load untrusted {purpose} from "{resolved}". ' + f"Allowed roots: {roots_display}. " + f"Use {_TRUSTED_ROOTS_ENV} to expand trusted roots." + ) + + +def unsafe_deserialization_enabled(default: bool = True) -> bool: + return _env_flag(_ALLOW_UNSAFE_ENV, default=default) + + +def load_pickle_artifact( + path: str | Path, + *, + purpose: str, + roots: Iterable[str | Path] | None = None, + allow_unsafe: bool | None = None, + encoding: str | None = None, +) -> Any: + resolved = ensure_trusted_path(path, purpose=purpose, roots=roots) + unsafe = unsafe_deserialization_enabled() if allow_unsafe is None else allow_unsafe + if not unsafe: + raise DeserializationSecurityError( + f"Pickle deserialization is disabled for {purpose}. " + f"Set {_ALLOW_UNSAFE_ENV}=1 only for trusted artifacts." + ) + + with resolved.open("rb") as handle: + if encoding is None: + try: + return pickle.load(handle) + except UnicodeDecodeError: + handle.seek(0) + return pickle.load(handle, encoding="latin1") + return pickle.load(handle, encoding=encoding) + + +def load_index_artifact( + path: str | Path, + *, + purpose: str, + roots: Iterable[str | Path] | None = None, + allow_unsafe: bool | None = None, +) -> Any: + resolved = ensure_trusted_path(path, purpose=purpose, roots=roots) + suffixes = tuple(s.lower() for s in resolved.suffixes) + if suffixes and suffixes[-1] == ".json": + with resolved.open("rt", encoding="utf-8") as handle: + return json.load(handle) + if suffixes[-2:] == (".json", ".gz"): + with gzip.open(resolved, "rt", encoding="utf-8") as handle: + return json.load(handle) + return load_pickle_artifact( + resolved, + purpose=purpose, + roots=roots, + allow_unsafe=allow_unsafe, + ) + + +def load_torch_artifact( + path: str | Path, + *, + purpose: str, + map_location=None, + roots: Iterable[str | Path] | None = None, + allow_unsafe: bool | None = None, +) -> Any: + resolved = ensure_trusted_path(path, purpose=purpose, roots=roots) + unsafe = unsafe_deserialization_enabled() if allow_unsafe is None else allow_unsafe + + import torch + + if unsafe: + try: + return torch.load(str(resolved), map_location=map_location, weights_only=False) + except TypeError: + return torch.load(str(resolved), map_location=map_location) + + try: + return torch.load(str(resolved), map_location=map_location, weights_only=True) + except TypeError as exc: + raise DeserializationSecurityError( + "Safe torch deserialization requires a torch version that supports weights_only loading. " + f"Set {_ALLOW_UNSAFE_ENV}=1 for trusted legacy checkpoints." + ) from exc + except Exception as exc: + raise DeserializationSecurityError( + f"Safe torch deserialization rejected {purpose}. " + f"If this checkpoint is trusted, set {_ALLOW_UNSAFE_ENV}=1." + ) from exc diff --git a/catpred/utils.py b/catpred/utils.py index 3f70e62..102aeda 100644 --- a/catpred/utils.py +++ b/catpred/utils.py @@ -23,18 +23,7 @@ from catpred.models import MoleculeModel from catpred.nn_utils import NoamLR from catpred.models.ffn import MultiReadout - - -def _torch_load_compat(path, map_location=None): - """ - Explicitly disable weights-only loading for backward compatibility with - CatPred checkpoints that store non-tensor objects (e.g., argparse.Namespace). - """ - try: - return torch.load(path, map_location=map_location, weights_only=False) - except TypeError: - # For older torch versions that do not support weights_only. - return torch.load(path, map_location=map_location) +from catpred.security import load_torch_artifact def makedirs(path: str, isfile: bool = False) -> None: @@ -122,7 +111,11 @@ def load_checkpoint( debug = info = print # Load model and args - state = _torch_load_compat(path, map_location=lambda storage, loc: storage) + state = load_torch_artifact( + path, + purpose="model checkpoint", + map_location=lambda storage, loc: storage, + ) args = TrainArgs() args.from_dict(vars(state["args"]), skip_unsettable=True) if not pretrained_egnn_feats_path is None: @@ -230,7 +223,11 @@ def load_frzn_model( """ debug = logger.debug if logger is not None else print - loaded_mpnn_model = _torch_load_compat(path, map_location=lambda storage, loc: storage) + loaded_mpnn_model = load_torch_artifact( + path, + purpose="frozen model checkpoint", + map_location=lambda storage, loc: storage, + ) loaded_state_dict = loaded_mpnn_model["state_dict"] loaded_args = loaded_mpnn_model["args"] @@ -452,7 +449,11 @@ def load_scalers( :return: A tuple with the data :class:`~catpred.data.scaler.StandardScaler` and features :class:`~catpred.data.scaler.StandardScaler`. """ - state = _torch_load_compat(path, map_location=lambda storage, loc: storage) + state = load_torch_artifact( + path, + purpose="scaler checkpoint", + map_location=lambda storage, loc: storage, + ) if state["data_scaler"] is not None: scaler = StandardScaler(state["data_scaler"]["means"], state["data_scaler"]["stds"]) @@ -507,7 +508,13 @@ def load_args(path: str) -> TrainArgs: """ args = TrainArgs() args.from_dict( - vars(_torch_load_compat(path, map_location=lambda storage, loc: storage)["args"]), + vars( + load_torch_artifact( + path, + purpose="args checkpoint", + map_location=lambda storage, loc: storage, + )["args"] + ), skip_unsettable=True, ) diff --git a/catpred/web/app.py b/catpred/web/app.py index ed1081f..62c38d3 100644 --- a/catpred/web/app.py +++ b/catpred/web/app.py @@ -38,6 +38,7 @@ class APISettings: python_executable: str input_root: str results_root: str + checkpoint_root: str allow_input_file: bool = False allow_unsafe_request_overrides: bool = False max_input_rows: int = 1000 @@ -50,12 +51,18 @@ def from_env(cls) -> "APISettings": repo_root = str(Path(env_repo_root).resolve()) if env_repo_root else str(Path.cwd().resolve()) input_root = os.environ.get("CATPRED_API_INPUT_ROOT") results_root = os.environ.get("CATPRED_API_RESULTS_ROOT") + checkpoint_root = os.environ.get("CATPRED_API_CHECKPOINT_ROOT") return cls( repo_root=repo_root, python_executable=os.environ.get("CATPRED_PYTHON_EXECUTABLE", sys.executable or "python"), input_root=str(Path(input_root).resolve()) if input_root else str((Path(repo_root) / "inputs").resolve()), results_root=str(Path(results_root).resolve()) if results_root else str((Path(repo_root) / "results").resolve()), + checkpoint_root=( + str(Path(checkpoint_root).resolve()) + if checkpoint_root + else str((Path(repo_root) / "checkpoints").resolve()) + ), allow_input_file=_env_flag("CATPRED_API_ALLOW_INPUT_FILE", default=False), allow_unsafe_request_overrides=_env_flag("CATPRED_API_ALLOW_UNSAFE_OVERRIDES", default=False), max_input_rows=max(int(os.environ.get("CATPRED_API_MAX_INPUT_ROWS", "1000")), 1), @@ -155,6 +162,21 @@ def _resolve_results_dir(raw_results_dir: str, settings: APISettings) -> str: return str(resolved) +def _resolve_checkpoint_dir(raw_checkpoint_dir: str, settings: APISettings) -> str: + checkpoint_root = Path(settings.checkpoint_root).resolve() + checkpoint_root.mkdir(parents=True, exist_ok=True) + resolved = _resolve_and_validate_path_under_root( + raw_path=raw_checkpoint_dir, + root=checkpoint_root, + purpose="checkpoint_dir", + ) + if not resolved.exists(): + raise FileNotFoundError(f'Checkpoint directory not found: "{resolved}"') + if not resolved.is_dir(): + raise ValueError(f'checkpoint_dir must be a directory: "{resolved}"') + return str(resolved) + + def _resolve_input_file_path(input_file: str, settings: APISettings) -> Path: if not settings.allow_input_file: raise ValueError( @@ -242,6 +264,7 @@ def ready() -> dict[str, Any]: "max_input_file_bytes": api_settings.max_input_file_bytes, "input_root": str(Path(api_settings.input_root).resolve()), "results_root": str(Path(api_settings.results_root).resolve()), + "checkpoint_root": str(Path(api_settings.checkpoint_root).resolve()), } return readiness @@ -253,20 +276,22 @@ def predict(payload: PredictRequest) -> PredictResponse: python_executable = _resolve_python_executable(payload.python_executable, api_settings) input_file, temp_file = _resolve_input_file(payload, settings=api_settings, repo_root=repo_root) safe_results_dir = _resolve_results_dir(payload.results_dir, api_settings) - + fallback = payload.fallback_to_local + if fallback is None: + fallback = default_fallback + selected_backend = (payload.backend or backend_router.settings.default_backend).lower() + checkpoint_dir = payload.checkpoint_dir + if selected_backend == "local" or fallback: + checkpoint_dir = _resolve_checkpoint_dir(payload.checkpoint_dir, api_settings) request_obj = PredictionRequest( parameter=payload.parameter.lower(), input_file=input_file, - checkpoint_dir=payload.checkpoint_dir, + checkpoint_dir=checkpoint_dir, use_gpu=payload.use_gpu, repo_root=str(repo_root), python_executable=python_executable, ) - fallback = payload.fallback_to_local - if fallback is None: - fallback = default_fallback - result = backend_router.predict( request_obj=request_obj, results_dir=safe_results_dir, diff --git a/scripts/baseline_analysis.py b/scripts/baseline_analysis.py index e2076a1..e87507d 100644 --- a/scripts/baseline_analysis.py +++ b/scripts/baseline_analysis.py @@ -1,6 +1,5 @@ import os import sys -import pickle import pandas as pd from tqdm import tqdm from concurrent.futures import ProcessPoolExecutor @@ -8,14 +7,17 @@ from sklearn.metrics import r2_score, mean_absolute_error import ipdb import csv +from catpred.security import load_pickle_artifact OUTPUT_DIR="../results/reproduce_results" DATA_DIR = "../data/external/Baseline/" def load_identity_data(parameter): """Load pre-calculated identity dictionary and mappings.""" - with open(f'{DATA_DIR}/{parameter}/{parameter}_test_train_identities_updated.pkl', 'rb') as f: - data = pickle.load(f) + data = load_pickle_artifact( + f"{DATA_DIR}/{parameter}/{parameter}_test_train_identities_updated.pkl", + purpose="baseline identity pickle", + ) train_seqs_dict = {val: key for key, val in data['train_seq_mapping'].items()} test_seqs_dict = {val: key for key, val in data['test_seq_mapping'].items()} return data, train_seqs_dict, test_seqs_dict @@ -142,4 +144,4 @@ def main(PARAMETER, OUTPUT_FILE, recalculate=False): if sys.argv[1]=='recalculate': main(PARAMETER, OUTPUT_FILE, True) else: - main(PARAMETER, OUTPUT_FILE, False) \ No newline at end of file + main(PARAMETER, OUTPUT_FILE, False) From 919a8635ae6f6fb2074ec8ec4e57232ac93ebda3 Mon Sep 17 00:00:00 2001 From: theproteinbot Date: Sat, 28 Feb 2026 17:51:41 -0500 Subject: [PATCH 09/34] docs: align API checkpoint_dir guidance with current guardrails --- README.md | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index c0ac109..baec9cd 100644 --- a/README.md +++ b/README.md @@ -126,6 +126,7 @@ By default, the API is hardened for service use: - `input_file` requests are disabled (use `input_rows` instead). - request-time overrides of `repo_root` / `python_executable` are disabled. - `results_dir` is constrained under `CATPRED_API_RESULTS_ROOT`. +- for local backend (and modal requests with fallback enabled), `checkpoint_dir` must resolve under `CATPRED_API_CHECKPOINT_ROOT`. Minimal `POST /predict` example for local inference using `input_rows`: @@ -134,7 +135,7 @@ curl -X POST http://127.0.0.1:8000/predict \ -H "Content-Type: application/json" \ -d '{ "parameter": "kcat", - "checkpoint_dir": "../data/pretrained/reproduce_checkpoints/kcat", + "checkpoint_dir": "kcat", "input_rows": [ {"SMILES": "CCO", "sequence": "ACDEFGHIK", "pdbpath": "seq_a"}, {"SMILES": "CCN", "sequence": "LMNPQRSTV", "pdbpath": "seq_b"} @@ -154,6 +155,7 @@ export CATPRED_MODAL_FALLBACK_TO_LOCAL=1 ``` Use `"backend": "modal"` in `/predict` requests to route through Modal. If fallback is enabled (env var above or request field `fallback_to_local`), failed modal requests can automatically reroute to local inference. +For local backend requests, place local checkpoints under `CATPRED_API_CHECKPOINT_ROOT` and pass a path relative to that root (for example, `"checkpoint_dir": "kcat"`). Optional API environment variables: From 135b9e4e97ac1c9ea3162c25b89e7f046f1a6229 Mon Sep 17 00:00:00 2001 From: theproteinbot Date: Sat, 28 Feb 2026 23:43:04 -0500 Subject: [PATCH 10/34] feat: ship web UI refresh and vercel deployment support --- .vercelignore | 8 + README.md | 22 + api/index.py | 21 + catpred/data/utils.py | 8 +- catpred/features/features_generators.py | 1 - catpred/models/model.py | 7 +- catpred/web/app.py | 103 ++- catpred/web/static/catpred.css | 1099 +++++++++++++++++++++++ catpred/web/static/catpred.js | 730 +++++++++++++++ catpred/web/static/index.html | 214 +++++ requirements.txt | 3 + setup.cfg | 6 +- vercel.json | 21 + 13 files changed, 2227 insertions(+), 16 deletions(-) create mode 100644 .vercelignore create mode 100644 api/index.py create mode 100644 catpred/web/static/catpred.css create mode 100644 catpred/web/static/catpred.js create mode 100644 catpred/web/static/index.html create mode 100644 requirements.txt create mode 100644 vercel.json diff --git a/.vercelignore b/.vercelignore new file mode 100644 index 0000000..e3a5762 --- /dev/null +++ b/.vercelignore @@ -0,0 +1,8 @@ +.venv/ +.e2e-assets/ +.e2e-tests/ +results/ +output/ +checkpoints/ +external/ +*.ipynb diff --git a/README.md b/README.md index baec9cd..00c08c2 100644 --- a/README.md +++ b/README.md @@ -24,6 +24,7 @@ - [Installation](#installing) - [Prediction](#predict) - [Web API (Optional)](#web-api-optional) + - [Vercel Deployment (Optional)](#vercel-deployment-optional) - [Reproducibility](#reproduce) - [Acknowledgements](#acknw) - [License](#license) @@ -185,6 +186,27 @@ export CATPRED_TRUSTED_DESERIALIZATION_ROOTS="/srv/catpred:/srv/catpred-data" export CATPRED_ALLOW_UNSAFE_DESERIALIZATION=1 ``` +### โ–ฒ Vercel Deployment (Optional) + +This repository includes a Vercel-ready ASGI entrypoint at `api/index.py` and a `vercel.json` route config. + +1. Push this repository to GitHub. +2. In Vercel, create a new project from that repo. +3. Set Environment Variables in Vercel Project Settings: + +```bash +# Use remote inference backend in serverless deployments +CATPRED_DEFAULT_BACKEND=modal +CATPRED_MODAL_ENDPOINT=https:// +CATPRED_MODAL_TOKEN= +CATPRED_MODAL_FALLBACK_TO_LOCAL=0 +``` + +Notes: +- Serverless filesystems are ephemeral/read-only except `/tmp`; this app auto-uses `/tmp/catpred` on Vercel. +- Local checkpoint-based inference is not recommended on Vercel serverless due runtime/dependency limits. +- If `CATPRED_MODAL_ENDPOINT` is not configured, the UI still loads but prediction requests will be limited by backend readiness. + ### ๐Ÿงช Fine-Tuning On Custom Data You can fine-tune CatPred on your own regression targets using `train.py`. diff --git a/api/index.py b/api/index.py new file mode 100644 index 0000000..962060d --- /dev/null +++ b/api/index.py @@ -0,0 +1,21 @@ +from __future__ import annotations + +from pathlib import Path +import os +import sys + + +ROOT = Path(__file__).resolve().parent.parent +if str(ROOT) not in sys.path: + sys.path.insert(0, str(ROOT)) + +if os.environ.get("VERCEL"): + os.environ.setdefault("CATPRED_API_RUNTIME_ROOT", "/tmp/catpred") + os.environ.setdefault("CATPRED_MODAL_FALLBACK_TO_LOCAL", "0") + if os.environ.get("CATPRED_MODAL_ENDPOINT"): + os.environ.setdefault("CATPRED_DEFAULT_BACKEND", "modal") + +from catpred.web.app import app + + +__all__ = ["app"] diff --git a/catpred/data/utils.py b/catpred/data/utils.py index ad23098..02f6d62 100644 --- a/catpred/data/utils.py +++ b/catpred/data/utils.py @@ -1,10 +1,12 @@ +from __future__ import annotations + from collections import OrderedDict, defaultdict import sys import csv import ctypes from logging import Logger from random import Random -from typing import List, Set, Tuple, Union +from typing import List, Set, Tuple, Union, TYPE_CHECKING import os import json import torch @@ -18,11 +20,13 @@ from .esm_utils import get_protein_embedder, get_coords from .data import MoleculeDatapoint, MoleculeDataset, make_mols from .scaffold import log_scaffold_stats, scaffold_split -from catpred.args import PredictArgs, TrainArgs from catpred.features import load_features, load_valid_atom_or_bond_features, is_mol from catpred.rdkit import make_mol from catpred.security import load_index_artifact, load_pickle_artifact +if TYPE_CHECKING: + from catpred.args import PredictArgs, TrainArgs + # Increase maximum size of field in the csv processing for the current architecture csv.field_size_limit(int(ctypes.c_ulong(-1).value // 2)) diff --git a/catpred/features/features_generators.py b/catpred/features/features_generators.py index 40226d2..26ec91f 100644 --- a/catpred/features/features_generators.py +++ b/catpred/features/features_generators.py @@ -85,7 +85,6 @@ def morgan_binary_features_generator(mol: Molecule, # return features -import ipdb @register_features_generator('morgan_diff_fp') def morgan_difference_features_generator(rxn: Reaction) -> np.ndarray: """ diff --git a/catpred/models/model.py b/catpred/models/model.py index 16e5c6f..916cd81 100644 --- a/catpred/models/model.py +++ b/catpred/models/model.py @@ -127,8 +127,11 @@ def create_protein_model(self, args: TrainArgs) -> None: x = list(self.pretrained_egnn_feats_dict.values()) self.pretrained_egnn_feats_avg = torch.stack(x).mean(dim=0) - # For rotary positional embeddings - self.rotary_embedder = RotaryEmbedding(dim=args.seq_embed_dim//4) + # Rotary embeddings require an even feature dimension. + rotary_dim = max(2, args.seq_embed_dim // 4) + if rotary_dim % 2 != 0: + rotary_dim -= 1 + self.rotary_embedder = RotaryEmbedding(dim=rotary_dim) # For self-attention self.multihead_attn = nn.MultiheadAttention(args.seq_embed_dim, diff --git a/catpred/web/app.py b/catpred/web/app.py index 62c38d3..8158f24 100644 --- a/catpred/web/app.py +++ b/catpred/web/app.py @@ -12,6 +12,8 @@ try: from fastapi import FastAPI, HTTPException + from fastapi.responses import FileResponse + from fastapi.staticfiles import StaticFiles from pydantic import BaseModel, Field, root_validator except ImportError as exc: # pragma: no cover - import guard for optional dependency raise ImportError( @@ -32,12 +34,56 @@ def _env_flag(name: str, default: bool = False) -> bool: return value.strip().lower() in {"1", "true", "yes", "y", "on"} +_SUPPORTED_PARAMETERS = ("kcat", "km", "ki") + + +def _contains_model_checkpoints(path: Path) -> bool: + if not path.exists() or not path.is_dir(): + return False + return any(path.rglob("model.pt")) + + +def _discover_default_checkpoint_root(repo_root: Path) -> Path: + default_root = (repo_root / "checkpoints").resolve() + production_root = (repo_root / ".e2e-assets" / "pretrained" / "production").resolve() + candidates = [default_root, production_root] + + best_root: Optional[Path] = None + best_score = -1 + for candidate in candidates: + if not candidate.exists() or not candidate.is_dir(): + continue + score = sum(int((candidate / parameter).is_dir()) for parameter in _SUPPORTED_PARAMETERS) + if score > best_score: + best_score = score + best_root = candidate + + if best_root and best_score > 0: + return best_root + + for candidate in candidates: + if _contains_model_checkpoints(candidate): + return candidate + + return default_root + + +def _discover_available_checkpoints(checkpoint_root: Path) -> dict[str, str]: + available: dict[str, str] = {} + for parameter in _SUPPORTED_PARAMETERS: + param_dir = (checkpoint_root / parameter).resolve() + if param_dir.is_dir() and _contains_model_checkpoints(param_dir): + available[parameter] = parameter + return available + + @dataclass(frozen=True) class APISettings: repo_root: str python_executable: str input_root: str results_root: str + temp_root: str checkpoint_root: str allow_input_file: bool = False allow_unsafe_request_overrides: bool = False @@ -49,19 +95,41 @@ class APISettings: def from_env(cls) -> "APISettings": env_repo_root = os.environ.get("CATPRED_REPO_ROOT") repo_root = str(Path(env_repo_root).resolve()) if env_repo_root else str(Path.cwd().resolve()) + repo_root_path = Path(repo_root).resolve() + env_runtime_root = os.environ.get("CATPRED_API_RUNTIME_ROOT") + if env_runtime_root: + default_runtime_root = Path(env_runtime_root).resolve() + elif os.environ.get("VERCEL"): + default_runtime_root = Path("/tmp/catpred").resolve() + else: + default_runtime_root = repo_root_path input_root = os.environ.get("CATPRED_API_INPUT_ROOT") results_root = os.environ.get("CATPRED_API_RESULTS_ROOT") + temp_root = os.environ.get("CATPRED_API_TEMP_ROOT") checkpoint_root = os.environ.get("CATPRED_API_CHECKPOINT_ROOT") return cls( repo_root=repo_root, python_executable=os.environ.get("CATPRED_PYTHON_EXECUTABLE", sys.executable or "python"), - input_root=str(Path(input_root).resolve()) if input_root else str((Path(repo_root) / "inputs").resolve()), - results_root=str(Path(results_root).resolve()) if results_root else str((Path(repo_root) / "results").resolve()), + input_root=( + str(Path(input_root).resolve()) + if input_root + else str((default_runtime_root / "inputs").resolve()) + ), + results_root=( + str(Path(results_root).resolve()) + if results_root + else str((default_runtime_root / "results").resolve()) + ), + temp_root=( + str(Path(temp_root).resolve()) + if temp_root + else str((default_runtime_root / "tmp").resolve()) + ), checkpoint_root=( str(Path(checkpoint_root).resolve()) if checkpoint_root - else str((Path(repo_root) / "checkpoints").resolve()) + else str(_discover_default_checkpoint_root(repo_root_path)) ), allow_input_file=_env_flag("CATPRED_API_ALLOW_INPUT_FILE", default=False), allow_unsafe_request_overrides=_env_flag("CATPRED_API_ALLOW_UNSAFE_OVERRIDES", default=False), @@ -164,7 +232,6 @@ def _resolve_results_dir(raw_results_dir: str, settings: APISettings) -> str: def _resolve_checkpoint_dir(raw_checkpoint_dir: str, settings: APISettings) -> str: checkpoint_root = Path(settings.checkpoint_root).resolve() - checkpoint_root.mkdir(parents=True, exist_ok=True) resolved = _resolve_and_validate_path_under_root( raw_path=raw_checkpoint_dir, root=checkpoint_root, @@ -207,7 +274,6 @@ def _resolve_input_file_path(input_file: str, settings: APISettings) -> Path: def _write_rows_to_temp_csv( rows: list[dict[str, Any]], settings: APISettings, - repo_root: Path, ) -> tuple[str, str]: if len(rows) == 0: raise ValueError("`input_rows` cannot be empty.") @@ -216,7 +282,7 @@ def _write_rows_to_temp_csv( f"`input_rows` exceeds CATPRED_API_MAX_INPUT_ROWS ({settings.max_input_rows})." ) - tmp_dir = (repo_root.resolve() / ".e2e-tests" / "api_tmp").resolve() + tmp_dir = Path(settings.temp_root).resolve() tmp_dir.mkdir(parents=True, exist_ok=True) fd, tmp_path = tempfile.mkstemp(prefix="api_input_", suffix=".csv", dir=str(tmp_dir)) os.close(fd) @@ -227,13 +293,12 @@ def _write_rows_to_temp_csv( def _resolve_input_file( payload: PredictRequest, settings: APISettings, - repo_root: Path, ) -> tuple[str, Optional[str]]: if payload.input_file: resolved = _resolve_input_file_path(payload.input_file, settings) return str(resolved), None - return _write_rows_to_temp_csv(payload.input_rows or [], settings=settings, repo_root=repo_root) + return _write_rows_to_temp_csv(payload.input_rows or [], settings=settings) def _preview_output(output_file: str, preview_limit: int) -> tuple[int, list[dict[str, Any]]]: @@ -249,6 +314,17 @@ def create_app( app = FastAPI(title="CatPred API", version="0.2.0") backend_router = router or InferenceBackendRouter() default_fallback = _env_flag("CATPRED_MODAL_FALLBACK_TO_LOCAL", default=False) + static_root = (Path(__file__).resolve().parent / "static").resolve() + + if static_root.exists(): + app.mount("/static", StaticFiles(directory=str(static_root)), name="static") + + @app.get("/", include_in_schema=False) + def root() -> FileResponse: + index_path = static_root / "index.html" + if not index_path.exists(): + raise HTTPException(status_code=404, detail="Landing page not found.") + return FileResponse(index_path) @app.get("/health") def health() -> dict[str, str]: @@ -257,6 +333,8 @@ def health() -> dict[str, str]: @app.get("/ready") def ready() -> dict[str, Any]: readiness = backend_router.readiness() + checkpoint_root = Path(api_settings.checkpoint_root).resolve() + available_checkpoints = _discover_available_checkpoints(checkpoint_root) readiness["api"] = { "allow_input_file": api_settings.allow_input_file, "allow_unsafe_request_overrides": api_settings.allow_unsafe_request_overrides, @@ -264,7 +342,12 @@ def ready() -> dict[str, Any]: "max_input_file_bytes": api_settings.max_input_file_bytes, "input_root": str(Path(api_settings.input_root).resolve()), "results_root": str(Path(api_settings.results_root).resolve()), - "checkpoint_root": str(Path(api_settings.checkpoint_root).resolve()), + "temp_root": str(Path(api_settings.temp_root).resolve()), + "checkpoint_root": str(checkpoint_root), + "available_checkpoints": available_checkpoints, + "missing_checkpoints": [ + parameter for parameter in _SUPPORTED_PARAMETERS if parameter not in available_checkpoints + ], } return readiness @@ -274,7 +357,7 @@ def predict(payload: PredictRequest) -> PredictResponse: try: repo_root = _resolve_repo_root(payload.repo_root, api_settings) python_executable = _resolve_python_executable(payload.python_executable, api_settings) - input_file, temp_file = _resolve_input_file(payload, settings=api_settings, repo_root=repo_root) + input_file, temp_file = _resolve_input_file(payload, settings=api_settings) safe_results_dir = _resolve_results_dir(payload.results_dir, api_settings) fallback = payload.fallback_to_local if fallback is None: diff --git a/catpred/web/static/catpred.css b/catpred/web/static/catpred.css new file mode 100644 index 0000000..90bb76f --- /dev/null +++ b/catpred/web/static/catpred.css @@ -0,0 +1,1099 @@ +:root { + --bg: #f6f4f0; + --bg-warm: #efecea; + --surface: #ffffff; + --border: rgba(0, 0, 0, 0.06); + --border-soft: rgba(0, 0, 0, 0.04); + + --text: #1a1816; + --text-secondary: #6b6560; + --text-tertiary: #a09890; + --text-muted: #c4bdb5; + + --protein-rose: #c4897a; + --protein-sage: #7a9e8e; + --protein-slate: #7a8a9e; + --protein-lavender: #9a8aae; + + --focus: rgba(122, 158, 142, 0.2); + --danger: #9b4542; + --ok: #3d6c55; + + --radius-lg: 22px; + --radius-md: 14px; + --shadow-card: 0 12px 36px rgba(17, 15, 12, 0.07); + --shadow-float: 0 16px 32px rgba(17, 15, 12, 0.11); +} + +* { + margin: 0; + padding: 0; + box-sizing: border-box; +} + +html { + scroll-behavior: smooth; + height: 100%; + scroll-snap-type: y mandatory; + overscroll-behavior-y: contain; +} + +body { + min-height: 100%; + background: var(--bg); + color: var(--text); + font-family: "Outfit", "Segoe UI", sans-serif; + font-weight: 300; + line-height: 1.6; + -webkit-font-smoothing: antialiased; + overflow-x: hidden; + position: relative; +} + +.texture { + position: fixed; + inset: 0; + pointer-events: none; + z-index: -2; + background: + radial-gradient(circle at 12% 14%, rgba(122, 158, 142, 0.12) 0%, transparent 32%), + radial-gradient(circle at 88% 22%, rgba(196, 137, 122, 0.11) 0%, transparent 33%), + radial-gradient(circle at 32% 80%, rgba(154, 138, 174, 0.08) 0%, transparent 36%); +} + +.texture::before { + content: ""; + position: absolute; + inset: 0; + background-image: + linear-gradient(to right, rgba(255, 255, 255, 0.16) 1px, transparent 1px), + linear-gradient(to bottom, rgba(255, 255, 255, 0.11) 1px, transparent 1px); + background-size: 28px 28px; + mix-blend-mode: soft-light; + opacity: 0.85; +} + +::selection { + background: var(--protein-sage); + color: #fff; +} + +.container { + width: min(1280px, calc(100% - 2.8rem)); + margin-inline: auto; +} + +.top-nav { + position: sticky; + top: 0; + z-index: 90; + backdrop-filter: blur(18px); + -webkit-backdrop-filter: blur(18px); + background: rgba(246, 244, 240, 0.84); + border-bottom: 1px solid var(--border-soft); +} + +.nav-content { + min-height: 78px; + display: flex; + align-items: center; + justify-content: space-between; + gap: 1rem; +} + +.brand { + display: inline-flex; + align-items: center; + gap: 0.72rem; + text-decoration: none; + color: var(--text); +} + +.brand-mark { + width: 30px; + height: 30px; + border-radius: 999px; + display: grid; + place-items: center; + color: var(--protein-sage); + background: rgba(122, 158, 142, 0.1); +} + +.brand-word { + font-family: "Newsreader", serif; + font-size: 1.32rem; + font-weight: 400; + letter-spacing: -0.02em; +} + +.desktop-nav { + display: flex; + align-items: center; + gap: 1.65rem; +} + +.desktop-nav a { + text-decoration: none; + color: var(--text-secondary); + font-size: 0.9rem; + font-weight: 300; + letter-spacing: 0.01em; + transition: color 0.25s ease; +} + +.desktop-nav a:hover { + color: var(--text); +} + +.hero { + min-height: calc(100svh - 78px); + display: grid; + grid-template-columns: 0.96fr 1.04fr; + gap: clamp(1.3rem, 4vw, 4.2rem); + align-items: center; + padding-block: clamp(2.2rem, 7.4vw, 5.8rem); +} + +.hero-copy { + position: relative; + z-index: 1; +} + +.eyebrow { + font-family: "IBM Plex Mono", monospace; + font-size: 0.68rem; + font-weight: 300; + letter-spacing: 0.13em; + text-transform: uppercase; + color: var(--text-tertiary); + margin-bottom: 1.2rem; +} + +.hero h1 { + font-family: "Newsreader", serif; + font-size: clamp(2.4rem, 5.4vw, 4.8rem); + font-weight: 300; + line-height: 1.05; + letter-spacing: -0.03em; + margin-bottom: 1.25rem; +} + +.hero h1 em { + font-style: italic; + color: var(--protein-sage); +} + +.lead { + max-width: 60ch; + font-size: clamp(0.98rem, 1.9vw, 1.07rem); + color: var(--text-secondary); + line-height: 1.75; + margin-bottom: 1.8rem; +} + +.lead code, +.muted code, +.helper code, +.faq-item code, +.steps code { + font-family: "IBM Plex Mono", monospace; + font-size: 0.82em; + padding: 0.08rem 0.28rem; + border-radius: 6px; + border: 1px solid var(--border); + background: rgba(255, 255, 255, 0.66); +} + +.hero-actions { + display: flex; + flex-wrap: wrap; + gap: 0.8rem; + margin-bottom: 1.75rem; +} + +.btn { + display: inline-flex; + align-items: center; + justify-content: center; + gap: 0.45rem; + border-radius: 999px; + border: 1px solid var(--border); + padding: 0.76rem 1.25rem; + text-decoration: none; + cursor: pointer; + font-family: "Outfit", sans-serif; + font-size: 0.87rem; + font-weight: 400; + letter-spacing: 0.01em; + transition: transform 0.26s ease, background-color 0.26s ease, color 0.26s ease, border-color 0.26s ease, + box-shadow 0.26s ease; +} + +.btn:hover { + transform: translateY(-1px); +} + +.btn.is-running { + position: relative; + cursor: wait; + transform: none; +} + +.btn.is-running::before { + content: ""; + width: 0.78rem; + height: 0.78rem; + border-radius: 999px; + border: 2px solid rgba(255, 255, 255, 0.45); + border-top-color: rgba(255, 255, 255, 0.96); + animation: spin 0.75s linear infinite; +} + +.btn.is-running:hover { + transform: none; +} + +.btn-dark { + background: var(--text); + border-color: var(--text); + color: var(--bg); + box-shadow: var(--shadow-float); +} + +.btn-dark:hover { + background: #2c2925; + border-color: #2c2925; +} + +.btn-ghost { + background: transparent; + color: var(--text-secondary); +} + +.btn-ghost:hover { + color: var(--text); + border-color: rgba(0, 0, 0, 0.12); +} + +.btn-outline { + background: transparent; + color: var(--text); +} + +.btn-outline:hover { + background: var(--text); + color: var(--bg); + border-color: var(--text); +} + +.nav-cta { + padding-inline: 1.1rem; +} + +.hero-stats { + list-style: none; + display: grid; + grid-template-columns: repeat(3, minmax(0, 1fr)); + gap: 0.7rem; + max-width: 720px; +} + +.hero-stats li { + border: 1px solid var(--border); + background: rgba(255, 255, 255, 0.68); + border-radius: var(--radius-md); + padding: 0.76rem 0.78rem; +} + +.hero-stats strong { + display: block; + font-family: "IBM Plex Mono", monospace; + font-size: 0.94rem; + font-weight: 400; + color: var(--text); +} + +.hero-stats span { + display: block; + margin-top: 0.25rem; + font-size: 0.84rem; + color: var(--text-secondary); +} + +.hero-visual-wrap { + position: relative; + isolation: isolate; +} + +.hero-visual { + position: relative; + width: min(560px, 100%); + aspect-ratio: 1 / 1; + margin-inline: auto; +} + +.hero-visual::before { + content: ""; + position: absolute; + inset: 10%; + border-radius: 999px; + background: radial-gradient(circle, rgba(122, 158, 142, 0.16) 0%, rgba(196, 137, 122, 0.08) 42%, transparent 74%); + filter: blur(24px); + animation: breathe 9s ease-in-out infinite; +} + +@keyframes breathe { + 0%, + 100% { + transform: scale(1); + opacity: 0.85; + } + + 50% { + transform: scale(1.06); + opacity: 1; + } +} + +.protein-structure { + width: 100%; + height: 100%; + position: relative; + z-index: 1; + animation: proteinFloat 18s ease-in-out infinite; +} + +@keyframes proteinFloat { + 0%, + 100% { + transform: translateY(0) rotate(0deg); + } + + 30% { + transform: translateY(-8px) rotate(0.4deg); + } + + 60% { + transform: translateY(3px) rotate(-0.25deg); + } + + 85% { + transform: translateY(-4px) rotate(0.18deg); + } +} + +.section { + padding-block: clamp(2.8rem, 7vw, 5.4rem); +} + +.snap-section { + min-height: 100svh; + scroll-snap-align: start; + scroll-snap-stop: always; + scroll-margin-top: 84px; +} + +.section-alt { + background: var(--bg-warm); + border-top: 1px solid var(--border); + border-bottom: 1px solid var(--border); +} + +.section-head { + margin-bottom: 1.2rem; +} + +.section-head.centered { + text-align: center; + max-width: 900px; + margin-inline: auto; +} + +.section-head h2 { + font-family: "Newsreader", serif; + font-size: clamp(1.95rem, 3.5vw, 3.1rem); + font-weight: 300; + line-height: 1.08; + letter-spacing: -0.02em; + margin-bottom: 0.56rem; +} + +.muted { + color: var(--text-secondary); + font-size: 0.98rem; + line-height: 1.72; +} + +.service-strip { + margin-top: 0.9rem; + display: inline-flex; + align-items: center; + gap: 0.6rem; + border: 1px solid var(--border); + border-radius: 999px; + background: rgba(255, 255, 255, 0.62); + padding: 0.36rem 0.62rem; +} + +.service-label { + font-family: "IBM Plex Mono", monospace; + font-size: 0.66rem; + letter-spacing: 0.08em; + text-transform: uppercase; + color: var(--text-tertiary); +} + +.badge { + border-radius: 999px; + border: 1px solid var(--border); + padding: 0.16rem 0.52rem; + font-family: "IBM Plex Mono", monospace; + font-size: 0.68rem; + font-weight: 400; + color: var(--text-secondary); + background: #fff; +} + +.badge.ok { + color: var(--ok); + border-color: rgba(61, 108, 85, 0.38); + background: rgba(122, 158, 142, 0.1); +} + +.badge.error { + color: var(--danger); + border-color: rgba(155, 69, 66, 0.38); + background: rgba(196, 137, 122, 0.1); +} + +.service-hint { + color: var(--text-secondary); + font-size: 0.82rem; +} + +.predict-layout { + display: grid; + grid-template-columns: 0.96fr 1.04fr; + gap: 1rem; + align-items: start; +} + +.card { + border: 1px solid var(--border); + border-radius: var(--radius-lg); + background: rgba(255, 255, 255, 0.8); + box-shadow: var(--shadow-card); + padding: 1rem; +} + +.card h3 { + font-family: "Newsreader", serif; + font-size: 1.38rem; + font-weight: 400; + line-height: 1.2; +} + +.input-card { + position: sticky; + top: 98px; +} + +.preset-row { + margin-top: 0.72rem; + display: flex; + gap: 0.45rem; + flex-wrap: wrap; +} + +.preset-btn { + border: 1px solid var(--border); + border-radius: 999px; + padding: 0.42rem 0.78rem; + background: rgba(255, 255, 255, 0.7); + color: var(--text-secondary); + cursor: pointer; + font-family: "IBM Plex Mono", monospace; + font-size: 0.68rem; + letter-spacing: 0.05em; + text-transform: uppercase; + transition: all 0.24s ease; +} + +.preset-btn:hover, +.preset-btn.active { + color: var(--protein-sage); + border-color: rgba(122, 158, 142, 0.4); + background: rgba(122, 158, 142, 0.08); +} + +.preset-row + .row-container { + margin-top: 0.78rem; +} + +.field-label { + display: block; + margin-bottom: 0.3rem; + font-family: "IBM Plex Mono", monospace; + font-size: 0.66rem; + letter-spacing: 0.11em; + text-transform: uppercase; + color: var(--text-tertiary); +} + +input, +textarea, +select, +button { + font: inherit; +} + +input, +textarea, +select { + width: 100%; + border: 1px solid var(--border); + border-radius: 10px; + background: rgba(255, 255, 255, 0.9); + color: var(--text); + padding: 0.62rem 0.68rem; + font-size: 0.9rem; + font-weight: 300; +} + +textarea { + line-height: 1.46; + resize: vertical; +} + +input:focus, +textarea:focus, +select:focus, +button:focus-visible { + outline: 2px solid var(--focus); + outline-offset: 1px; +} + +.row-container { + margin-top: 0.78rem; + display: grid; + gap: 0.72rem; +} + +.row-item { + border: 1px solid var(--border); + border-radius: var(--radius-md); + background: rgba(255, 255, 255, 0.7); + padding: 0.72rem; +} + +.row-item-head { + display: flex; + align-items: center; + justify-content: space-between; + gap: 0.6rem; + margin-bottom: 0.58rem; +} + +.row-item-head[hidden] { + display: none !important; +} + +.row-item-head h4 { + font-family: "IBM Plex Mono", monospace; + font-size: 0.72rem; + color: var(--text-tertiary); + text-transform: none; + letter-spacing: 0.02em; + font-weight: 400; +} + +.row-grid { + display: grid; + grid-template-columns: 1fr; + gap: 0.55rem; +} + +.icon-btn { + border: 1px solid var(--border); + border-radius: 999px; + background: rgba(255, 255, 255, 0.82); + color: var(--danger); + padding: 0.34rem 0.6rem; + cursor: pointer; + font-family: "IBM Plex Mono", monospace; + font-size: 0.65rem; + transition: background-color 0.24s ease; +} + +.icon-btn:hover { + background: rgba(196, 137, 122, 0.09); +} + +.form-actions { + margin-top: 0.82rem; + display: flex; + flex-wrap: wrap; + gap: 0.55rem; +} + +.helper { + margin-top: 0.76rem; + color: var(--text-secondary); + font-size: 0.86rem; + line-height: 1.6; +} + +.status-box { + margin-top: 0.72rem; + position: relative; + overflow: hidden; + border-radius: 12px; + border: 1px solid var(--border); + background: rgba(255, 255, 255, 0.75); + min-height: 44px; + padding: 0.58rem 0.72rem; + display: flex; + align-items: center; + gap: 0.52rem; + color: var(--text-secondary); + font-family: "IBM Plex Mono", monospace; + font-size: 0.68rem; + letter-spacing: 0.06em; + text-transform: uppercase; +} + +.status-box.running { + color: var(--protein-sage); + border-color: rgba(122, 158, 142, 0.42); + background: linear-gradient( + 120deg, + rgba(122, 158, 142, 0.14), + rgba(122, 158, 142, 0.06) 46%, + rgba(122, 138, 158, 0.09) + ); +} + +.status-box.running::before { + content: ""; + width: 0.75rem; + height: 0.75rem; + flex: 0 0 auto; + border-radius: 999px; + border: 2px solid rgba(122, 158, 142, 0.32); + border-top-color: rgba(122, 158, 142, 0.96); + animation: spin 0.74s linear infinite; +} + +.status-box.ok { + color: var(--ok); + border-color: rgba(61, 108, 85, 0.35); + background: rgba(122, 158, 142, 0.08); +} + +.status-box.error { + color: var(--danger); + border-color: rgba(155, 69, 66, 0.36); + background: rgba(196, 137, 122, 0.09); +} + +.result-cards { + margin-top: 0.72rem; + display: grid; + gap: 0.68rem; +} + +.output-card.is-busy { + position: relative; +} + +.output-card.is-busy::after { + content: ""; + position: absolute; + inset: 0; + border-radius: var(--radius-lg); + pointer-events: none; + background: linear-gradient( + 110deg, + rgba(255, 255, 255, 0) 0%, + rgba(122, 158, 142, 0.08) 35%, + rgba(255, 255, 255, 0) 65% + ); + transform: translateX(-100%); + animation: busy-sweep 1.7s ease-in-out infinite; +} + +.output-card.is-busy .result-cards, +.output-card.is-busy .details-output { + opacity: 0.76; +} + +.empty-result { + border: 1px dashed var(--border); + border-radius: var(--radius-md); + background: rgba(255, 255, 255, 0.65); + padding: 0.92rem; + color: var(--text-secondary); +} + +.result-card { + border: 1px solid var(--border); + border-radius: var(--radius-md); + background: rgba(255, 255, 255, 0.76); + padding: 0.84rem; +} + +.result-card-head { + display: flex; + justify-content: space-between; + align-items: center; + gap: 0.6rem; +} + +.result-card-head h4 { + font-family: "Newsreader", serif; + font-size: 1.08rem; + font-weight: 400; +} + +.result-chip { + border: 1px solid var(--border); + border-radius: 999px; + padding: 0.15rem 0.5rem; + font-family: "IBM Plex Mono", monospace; + font-size: 0.64rem; + color: var(--text-secondary); +} + +.result-main { + margin-top: 0.52rem; + display: flex; + align-items: baseline; + gap: 0.45rem; + flex-wrap: wrap; +} + +.result-main strong { + font-family: "Newsreader", serif; + font-size: 1.82rem; + line-height: 1; + font-weight: 400; +} + +.result-main span { + color: var(--text-secondary); + font-size: 0.86rem; +} + +.metric-grid { + margin-top: 0.62rem; + display: grid; + grid-template-columns: repeat(3, minmax(0, 1fr)); + gap: 0.45rem; +} + +.metric-grid div { + border: 1px solid var(--border); + border-radius: 10px; + background: rgba(255, 255, 255, 0.74); + padding: 0.45rem; +} + +.metric-grid dt { + font-family: "IBM Plex Mono", monospace; + font-size: 0.62rem; + color: var(--text-tertiary); + letter-spacing: 0.06em; + text-transform: uppercase; +} + +.metric-grid dd { + margin-top: 0.16rem; + font-size: 0.9rem; + color: var(--text-secondary); +} + +.result-meta { + margin-top: 0.6rem; + display: flex; + flex-wrap: wrap; + gap: 0.42rem; +} + +.result-meta span { + font-family: "IBM Plex Mono", monospace; + font-size: 0.62rem; + color: var(--text-secondary); + border: 1px solid var(--border); + border-radius: 999px; + padding: 0.14rem 0.48rem; + background: rgba(255, 255, 255, 0.66); + max-width: 100%; + overflow: hidden; + text-overflow: ellipsis; + white-space: nowrap; +} + +.details-output { + margin-top: 0.8rem; + border-top: 1px solid var(--border); + padding-top: 0.65rem; +} + +.details-output summary { + cursor: pointer; + font-family: "IBM Plex Mono", monospace; + font-size: 0.66rem; + letter-spacing: 0.08em; + text-transform: uppercase; + color: var(--text-tertiary); +} + +.rows-wrap { + margin-top: 0.72rem; + overflow: auto; +} + +.input-table { + width: 100%; + min-width: 680px; + border-collapse: collapse; +} + +.input-table th, +.input-table td { + text-align: left; + border-bottom: 1px solid var(--border); + padding: 0.42rem; + vertical-align: top; + font-size: 0.86rem; + color: var(--text-secondary); +} + +.input-table th { + font-family: "IBM Plex Mono", monospace; + font-size: 0.65rem; + font-weight: 300; + letter-spacing: 0.1em; + color: var(--text-tertiary); + text-transform: uppercase; +} + +.steps { + display: grid; + grid-template-columns: repeat(3, minmax(0, 1fr)); + gap: 1rem; +} + +.guide-grid { + margin-top: 0.35rem; +} + +.steps article { + border-radius: 18px; + border: 1px solid var(--border); + background: rgba(255, 255, 255, 0.72); + padding: 1rem; +} + +.steps span { + font-family: "IBM Plex Mono", monospace; + font-size: 0.67rem; + color: var(--text-tertiary); + letter-spacing: 0.1em; +} + +.steps h3 { + margin-top: 0.38rem; + margin-bottom: 0.4rem; + font-family: "Newsreader", serif; + font-size: 1.3rem; + font-weight: 400; +} + +.steps p { + color: var(--text-secondary); + font-size: 0.93rem; + line-height: 1.68; +} + +.guide-note { + margin-top: 0.88rem; + border: 1px solid var(--border); + border-radius: 14px; + background: rgba(255, 255, 255, 0.72); + padding: 0.72rem 0.82rem; + color: var(--text-secondary); + font-size: 0.9rem; +} + +.guide-note strong { + color: var(--text); + font-family: "IBM Plex Mono", monospace; + font-size: 0.82em; + letter-spacing: 0.03em; +} + +.faq-list { + border-top: 1px solid var(--border); +} + +.faq-item { + border-bottom: 1px solid var(--border); + padding-block: 0.86rem; +} + +.faq-item button { + width: 100%; + border: 0; + background: transparent; + text-align: left; + cursor: pointer; + display: flex; + justify-content: space-between; + align-items: center; + gap: 1rem; + color: var(--text); + font-family: "Newsreader", serif; + font-size: 1.24rem; + font-weight: 400; +} + +.faq-item button::after { + content: "+"; + color: var(--text-tertiary); + font-family: "IBM Plex Mono", monospace; + font-size: 0.95rem; +} + +.faq-item button[aria-expanded="true"]::after { + content: "โˆ’"; +} + +.faq-item p { + max-height: 0; + overflow: hidden; + margin: 0; + color: var(--text-secondary); + font-size: 0.95rem; + line-height: 1.67; + transition: max-height 0.24s ease, margin-top 0.24s ease; +} + +.faq-item.open p { + max-height: 12rem; + margin-top: 0.4rem; +} + +.footer { + padding: 1.2rem 0 2.4rem; +} + +.footer-inner { + border-top: 1px solid var(--border); + padding-top: 0.95rem; + display: flex; + justify-content: space-between; + align-items: center; + gap: 1rem; + color: var(--text-tertiary); + font-size: 0.84rem; +} + +.mobile-stick { + position: fixed; + left: 1rem; + right: 1rem; + bottom: 1rem; + z-index: 70; + display: none; + text-align: center; + text-decoration: none; + border-radius: 999px; + padding: 0.82rem 1rem; + background: var(--text); + color: var(--bg); + font-family: "Outfit", sans-serif; + font-size: 0.88rem; + font-weight: 400; + box-shadow: var(--shadow-float); +} + +.reveal { + opacity: 0; + transform: translateY(20px); + transition: opacity 0.48s ease, transform 0.48s ease; +} + +.reveal.show { + opacity: 1; + transform: translateY(0); +} + +@keyframes spin { + to { + transform: rotate(360deg); + } +} + +@keyframes busy-sweep { + 100% { + transform: translateX(100%); + } +} + +@media (max-width: 1120px) { + html { + scroll-snap-type: y proximity; + } + + .hero { + grid-template-columns: 1fr; + min-height: auto; + } + + .hero-visual { + width: min(500px, 100%); + } + + .predict-layout, + .steps, + .hero-stats, + .metric-grid { + grid-template-columns: 1fr; + } + + .input-card { + position: static; + } +} + +@media (max-width: 780px) { + .desktop-nav { + display: none; + } + + .container { + width: min(1280px, calc(100% - 1.5rem)); + } + + .hero { + padding-top: 1.5rem; + } + + .snap-section { + min-height: auto; + scroll-snap-stop: normal; + } + + .input-table { + min-width: 560px; + } + + .mobile-stick { + display: block; + } + + .footer { + padding-bottom: 5rem; + } +} diff --git a/catpred/web/static/catpred.js b/catpred/web/static/catpred.js new file mode 100644 index 0000000..707d620 --- /dev/null +++ b/catpred/web/static/catpred.js @@ -0,0 +1,730 @@ +(function () { + const form = document.getElementById("predictForm"); + const rowContainer = document.getElementById("rowContainer"); + const outputCard = document.querySelector(".output-card"); + + const addRowBtn = document.getElementById("addRowBtn"); + const loadSampleBtn = document.getElementById("loadSampleBtn"); + const runBtn = document.getElementById("runBtn"); + + const statusBox = document.getElementById("statusBox"); + const resultCards = document.getElementById("resultCards"); + const previewTable = document.getElementById("previewTable"); + + const serviceBadge = document.getElementById("serviceBadge"); + const serviceHint = document.getElementById("serviceHint"); + + const presetButtons = document.querySelectorAll(".preset-btn"); + + const sampleRows = [ + { + SMILES: "CCO", + sequence: "ACDEFGHIK", + pdbpath: "seq_001", + }, + { + SMILES: "CCN", + sequence: "LMNPQRSTV", + pdbpath: "seq_002", + }, + ]; + + const supportedParameters = ["kcat", "km", "ki"]; + let availableCheckpointParams = new Set(supportedParameters); + let selectedParameter = "kcat"; + const runningPhaseMessages = [ + "validating input", + "building protein records", + "running model ensemble", + "aggregating uncertainty", + ]; + const defaultRunButtonLabel = runBtn ? runBtn.textContent : "Run prediction"; + let runningStatusInterval = null; + let runStartedAtMs = null; + + function jsonPretty(data) { + return JSON.stringify(data, null, 2); + } + + function escapeHtml(text) { + return String(text) + .replace(/&/g, "&") + .replace(//g, ">") + .replace(/\"/g, """) + .replace(/'/g, "'"); + } + + function formatNumber(value) { + const n = Number(value); + if (!Number.isFinite(n)) { + return "โ€”"; + } + return n.toFixed(1); + } + + function truncateText(value, maxLength) { + const text = String(value || ""); + if (text.length <= maxLength) { + return text; + } + return text.slice(0, maxLength - 1) + "โ€ฆ"; + } + + function setStatus(text, kind) { + if (!statusBox) return; + statusBox.textContent = text; + statusBox.classList.remove("ok", "error", "running"); + if (kind) { + statusBox.classList.add(kind); + } + } + + function formatElapsed(seconds) { + const safeSeconds = Math.max(0, Math.floor(seconds)); + const minutes = Math.floor(safeSeconds / 60); + const remainder = safeSeconds % 60; + return String(minutes).padStart(2, "0") + ":" + String(remainder).padStart(2, "0"); + } + + function getElapsedSeconds() { + if (!runStartedAtMs) return 0; + return Math.floor((Date.now() - runStartedAtMs) / 1000); + } + + function setRunButtonState(isRunning, elapsedLabel) { + if (!runBtn) return; + if (isRunning) { + const suffix = elapsedLabel ? " " + elapsedLabel : ""; + runBtn.textContent = "Running" + suffix; + runBtn.classList.add("is-running"); + runBtn.disabled = true; + runBtn.setAttribute("aria-busy", "true"); + return; + } + + runBtn.textContent = defaultRunButtonLabel || "Run prediction"; + runBtn.classList.remove("is-running"); + runBtn.disabled = false; + runBtn.removeAttribute("aria-busy"); + } + + function setBusyUi(isBusy) { + if (form) { + form.classList.toggle("is-busy", isBusy); + form.setAttribute("aria-busy", isBusy ? "true" : "false"); + } + if (outputCard) { + outputCard.classList.toggle("is-busy", isBusy); + outputCard.setAttribute("aria-busy", isBusy ? "true" : "false"); + } + + if (addRowBtn) { + addRowBtn.disabled = isBusy; + } + if (loadSampleBtn) { + loadSampleBtn.disabled = isBusy; + } + if (presetButtons && presetButtons.length) { + presetButtons.forEach((btn) => { + if (!isBusy && !isParameterAvailable(btn.dataset.param)) { + btn.disabled = true; + return; + } + btn.disabled = isBusy; + }); + } + } + + function startRunningFeedback(payload) { + stopRunningFeedback(); + runStartedAtMs = Date.now(); + setBusyUi(true); + + const parameterLabel = String(payload.parameter || "kcat").toUpperCase(); + const rowCount = Array.isArray(payload.input_rows) ? payload.input_rows.length : 0; + const rowLabel = rowCount === 1 ? "1 row" : String(rowCount) + " rows"; + + const updateRunningStatus = () => { + const elapsed = getElapsedSeconds(); + const elapsedLabel = formatElapsed(elapsed); + const phaseIndex = Math.floor(elapsed / 3) % runningPhaseMessages.length; + const phaseText = runningPhaseMessages[phaseIndex]; + setRunButtonState(true, elapsedLabel); + setStatus( + "Running " + parameterLabel + " on " + rowLabel + " โ€ข " + elapsedLabel + " โ€ข " + phaseText, + "running" + ); + }; + + updateRunningStatus(); + runningStatusInterval = window.setInterval(updateRunningStatus, 1000); + } + + function stopRunningFeedback() { + if (runningStatusInterval) { + window.clearInterval(runningStatusInterval); + runningStatusInterval = null; + } + setBusyUi(false); + setRunButtonState(false); + } + + function setServiceState(text, hint, kind) { + if (serviceBadge) { + serviceBadge.textContent = text; + serviceBadge.classList.remove("ok", "error"); + if (kind) { + serviceBadge.classList.add(kind); + } + } + if (serviceHint) { + serviceHint.textContent = hint || ""; + } + } + + function setActivePreset(paramValue) { + if (!presetButtons || !presetButtons.length) return; + presetButtons.forEach((btn) => { + if (!(btn instanceof HTMLElement)) return; + btn.classList.toggle("active", btn.dataset.param === paramValue); + }); + } + + function getSelectedParameter() { + return String(selectedParameter || "kcat").toLowerCase(); + } + + function firstAvailableParameter() { + for (const param of supportedParameters) { + if (availableCheckpointParams.has(param)) { + return param; + } + } + return null; + } + + function setParameterAvailability(availableCheckpoints) { + if (availableCheckpoints && typeof availableCheckpoints === "object") { + availableCheckpointParams = new Set( + Object.keys(availableCheckpoints) + .map((key) => String(key).toLowerCase()) + .filter((key) => supportedParameters.includes(key)) + ); + } else { + availableCheckpointParams = new Set(supportedParameters); + } + + if (presetButtons && presetButtons.length) { + presetButtons.forEach((btn) => { + const paramValue = String(btn.dataset.param || "").toLowerCase(); + const enabled = availableCheckpointParams.has(paramValue); + btn.disabled = !enabled; + btn.setAttribute("aria-disabled", String(!enabled)); + }); + } + + const fallbackParam = firstAvailableParameter(); + if (fallbackParam && !availableCheckpointParams.has(getSelectedParameter())) { + selectedParameter = fallbackParam; + setActivePreset(fallbackParam); + } + } + + function isParameterAvailable(paramValue) { + return availableCheckpointParams.has(String(paramValue || "").toLowerCase()); + } + + function formatSequenceId(index) { + const safeIndex = Math.max(1, Number(index) || 1); + return "seq_" + String(safeIndex).padStart(3, "0"); + } + + function getNextSequenceId() { + if (!rowContainer) { + return formatSequenceId(1); + } + + let maxSeen = 0; + const idInputs = rowContainer.querySelectorAll('input[name="pdbpath"]'); + idInputs.forEach((input) => { + const raw = String(input.value || "").trim(); + const match = raw.match(/^seq_(\d+)$/i); + if (!match) return; + const parsed = Number(match[1]); + if (Number.isFinite(parsed)) { + maxSeen = Math.max(maxSeen, parsed); + } + }); + + if (maxSeen > 0) { + return formatSequenceId(maxSeen + 1); + } + + const currentRowCount = rowContainer.querySelectorAll(".row-item").length; + return formatSequenceId(currentRowCount + 1); + } + + function rowTemplate(index, values) { + const smiles = values && values.SMILES ? values.SMILES : ""; + const seq = values && values.sequence ? values.sequence : ""; + const pdb = values && values.pdbpath ? values.pdbpath : ""; + + return ( + '
' + + '
' + + "

Entry " + + (index + 1) + + "

" + + '' + + "
" + + '
' + + '' + + '' + + '" + + "
" + + "
" + ); + } + + function renumberRows() { + if (!rowContainer) return; + const items = rowContainer.querySelectorAll(".row-item"); + const shouldShowHeader = items.length > 1; + items.forEach((item, idx) => { + const head = item.querySelector(".row-item-head"); + if (head instanceof HTMLElement) { + head.hidden = !shouldShowHeader; + } + const heading = item.querySelector("h4"); + if (heading) { + heading.textContent = "Entry " + String(idx + 1); + } + }); + } + + function addRow(values) { + if (!rowContainer) return; + const nextValues = values ? { ...values } : { SMILES: "", sequence: "", pdbpath: "" }; + if (!String(nextValues.pdbpath || "").trim()) { + nextValues.pdbpath = getNextSequenceId(); + } + const index = rowContainer.querySelectorAll(".row-item").length; + rowContainer.insertAdjacentHTML("beforeend", rowTemplate(index, nextValues)); + const last = rowContainer.lastElementChild; + if (last) { + last.animate( + [ + { opacity: 0, transform: "translateY(8px)" }, + { opacity: 1, transform: "translateY(0)" }, + ], + { duration: 220, easing: "ease-out" } + ); + } + renumberRows(); + } + + function clearRows() { + if (!rowContainer) return; + rowContainer.innerHTML = ""; + } + + function loadSampleRows() { + clearRows(); + sampleRows.forEach((row) => addRow(row)); + } + + function collectRows() { + if (!rowContainer) return []; + + const rows = []; + const items = rowContainer.querySelectorAll(".row-item"); + + items.forEach((item) => { + const smilesInput = item.querySelector('input[name="SMILES"]'); + const sequenceInput = item.querySelector('textarea[name="sequence"]'); + const pdbpathInput = item.querySelector('input[name="pdbpath"]'); + + if (!smilesInput || !sequenceInput || !pdbpathInput) { + return; + } + + const smiles = smilesInput.value.trim(); + const sequence = sequenceInput.value.trim().toUpperCase(); + const pdbpath = pdbpathInput.value.trim(); + + if (!smiles || !sequence || !pdbpath) { + return; + } + + rows.push({ + SMILES: smiles, + sequence: sequence, + pdbpath: pdbpath, + }); + }); + + return rows; + } + + function validateRows(rows) { + if (!rows.length) { + return "Please add at least one complete input row."; + } + + const mapping = new Map(); + for (let i = 0; i < rows.length; i++) { + const row = rows[i]; + const key = row.pdbpath; + if (mapping.has(key) && mapping.get(key) !== row.sequence) { + return "Each Sequence ID must map to one unique enzyme sequence."; + } + mapping.set(key, row.sequence); + } + + return ""; + } + + function buildPayload(rows) { + const target = getSelectedParameter(); + + return { + parameter: target, + checkpoint_dir: target, + input_rows: rows, + use_gpu: false, + results_dir: "web-app", + fallback_to_local: true, + }; + } + + function parsePrediction(row) { + const keys = Object.keys(row || {}); + const linearKey = keys.find((key) => key.startsWith("Prediction_(")); + const unitMatch = linearKey ? linearKey.match(/^Prediction_\((.*)\)$/) : null; + const unit = unitMatch ? unitMatch[1] : ""; + + return { + linear: linearKey ? row[linearKey] : null, + linearKey: linearKey || "Prediction", + unit: unit, + log10: row.Prediction_log10, + sdTotal: row.SD_total, + sdAleatoric: row.SD_aleatoric, + sdEpistemic: row.SD_epistemic, + }; + } + + function renderResultCards(previewRows, selectedParam) { + if (!resultCards) return; + + if (!previewRows || !previewRows.length) { + resultCards.innerHTML = + '

No preview rows were returned for this run.

'; + return; + } + + const cardsHtml = previewRows + .map((row, idx) => { + const p = parsePrediction(row); + + return ( + '
' + + '
' + + "

Result " + + String(idx + 1) + + "

" + + '' + + escapeHtml(selectedParam.toUpperCase()) + + "" + + "
" + + '
' + + "" + + escapeHtml(formatNumber(p.linear)) + + "" + + "" + + escapeHtml(p.unit || "predicted unit") + + "" + + "
" + + '
' + + "
log10
" + + escapeHtml(formatNumber(p.log10)) + + "
" + + "
Total SD
" + + escapeHtml(formatNumber(p.sdTotal)) + + "
" + + "
Epistemic SD
" + + escapeHtml(formatNumber(p.sdEpistemic)) + + "
" + + "
" + + '
' + + "SMILES: " + + escapeHtml(truncateText(row.SMILES, 24)) + + "" + + "Sequence ID: " + + escapeHtml(row.pdbpath || "โ€”") + + "" + + "
" + + "
" + ); + }) + .join(""); + + resultCards.innerHTML = cardsHtml; + } + + function renderPreviewTable(previewRows) { + if (!previewTable) return; + + if (!previewRows || !previewRows.length) { + previewTable.innerHTML = "No rows to show."; + return; + } + + const keys = Object.keys(previewRows[0]); + const head = + "" + + keys.map((k) => "" + escapeHtml(k) + "").join("") + + ""; + + const bodyRows = previewRows + .map((row) => { + const cells = keys + .map((k) => { + const value = row[k]; + if (value === null || value === undefined) { + return ""; + } + const displayValue = typeof value === "number" ? formatNumber(value) : value; + return "" + escapeHtml(displayValue) + ""; + }) + .join(""); + return "" + cells + ""; + }) + .join(""); + + previewTable.innerHTML = head + "" + bodyRows + ""; + } + + async function fetchReady() { + setServiceState("Checking...", "", ""); + + try { + const response = await fetch("/ready", { + method: "GET", + headers: { Accept: "application/json" }, + }); + + let data; + try { + data = await response.json(); + } catch (_err) { + data = {}; + } + + if (!response.ok) { + setServiceState("Offline", "Service not reachable", "error"); + return; + } + + setParameterAvailability(data && data.api ? data.api.available_checkpoints : null); + const availableParams = Array.from(availableCheckpointParams.values()); + + if (data && data.ready) { + const backend = data.default_backend ? String(data.default_backend) : "default"; + const hint = availableParams.length + ? "Backend: " + backend + " | Checkpoints: " + availableParams.join(", ") + : "Backend: " + backend + " | No local checkpoints found"; + setServiceState("Online", hint, "ok"); + } else { + const hint = availableParams.length + ? "Backend configuration needed" + : "Backend configuration needed | No local checkpoints found"; + setServiceState("Limited", hint, "error"); + } + } catch (_err) { + setServiceState("Offline", "Could not contact service", "error"); + } + } + + async function submitPrediction(event) { + event.preventDefault(); + + const rows = collectRows(); + const rowError = validateRows(rows); + if (rowError) { + setStatus(rowError, "error"); + return; + } + + const selectedParameter = getSelectedParameter(); + if (!isParameterAvailable(selectedParameter)) { + setStatus("No local checkpoint available for " + selectedParameter.toUpperCase() + ".", "error"); + return; + } + + const payload = buildPayload(rows); + startRunningFeedback(payload); + await new Promise((resolve) => window.requestAnimationFrame(resolve)); + + try { + const response = await fetch("/predict", { + method: "POST", + headers: { + "Content-Type": "application/json", + Accept: "application/json", + }, + body: JSON.stringify(payload), + }); + + let data; + try { + data = await response.json(); + } catch (_err) { + data = { detail: "Unexpected response format." }; + } + + if (!response.ok) { + const message = data && data.detail ? String(data.detail) : "Prediction could not be completed."; + renderResultCards([], payload.parameter); + renderPreviewTable([]); + const elapsedLabel = formatElapsed(getElapsedSeconds()); + setStatus(message + " (" + elapsedLabel + ")", "error"); + return; + } + + renderResultCards(data.preview_rows || [], payload.parameter); + renderPreviewTable(data.preview_rows || []); + const elapsedLabel = formatElapsed(getElapsedSeconds()); + setStatus("Prediction complete โ€ข " + elapsedLabel, "ok"); + } catch (_err) { + renderResultCards([], payload.parameter); + renderPreviewTable([]); + const elapsedLabel = formatElapsed(getElapsedSeconds()); + setStatus("Network error while running prediction. (" + elapsedLabel + ")", "error"); + } finally { + stopRunningFeedback(); + runStartedAtMs = null; + } + } + + function setupFaq() { + const faqRoot = document.getElementById("faqList"); + if (!faqRoot) return; + + const items = faqRoot.querySelectorAll(".faq-item"); + items.forEach((item) => { + const button = item.querySelector("button"); + if (!button) return; + + button.addEventListener("click", function () { + const isOpen = item.classList.contains("open"); + items.forEach((row) => { + row.classList.remove("open"); + const rowButton = row.querySelector("button"); + if (rowButton) { + rowButton.setAttribute("aria-expanded", "false"); + } + }); + if (!isOpen) { + item.classList.add("open"); + button.setAttribute("aria-expanded", "true"); + } + }); + }); + } + + function setupReveal() { + const revealItems = document.querySelectorAll(".reveal"); + if (!revealItems.length) return; + + const observer = new IntersectionObserver( + (entries) => { + entries.forEach((entry) => { + if (entry.isIntersecting) { + entry.target.classList.add("show"); + observer.unobserve(entry.target); + } + }); + }, + { + threshold: 0.12, + rootMargin: "0px 0px -36px 0px", + } + ); + + revealItems.forEach((item) => observer.observe(item)); + } + + function setupEvents() { + if (presetButtons && presetButtons.length) { + presetButtons.forEach((btn) => { + btn.addEventListener("click", function () { + if (btn.disabled) return; + const chosenParam = btn.dataset.param || "kcat"; + selectedParameter = String(chosenParam).toLowerCase(); + setActivePreset(chosenParam); + }); + }); + } + + if (addRowBtn) { + addRowBtn.addEventListener("click", function () { + addRow({ SMILES: "", sequence: "", pdbpath: "" }); + }); + } + + if (loadSampleBtn) { + loadSampleBtn.addEventListener("click", function () { + loadSampleRows(); + setStatus("Sample inputs loaded", "ok"); + }); + } + + if (rowContainer) { + rowContainer.addEventListener("click", function (event) { + const target = event.target; + if (!(target instanceof HTMLElement)) return; + if (!target.matches("[data-remove-row]")) return; + + const row = target.closest(".row-item"); + if (!row) return; + + if (rowContainer.querySelectorAll(".row-item").length === 1) { + setStatus("At least one row is required", "error"); + return; + } + + row.remove(); + renumberRows(); + }); + } + + if (form) { + form.addEventListener("submit", submitPrediction); + } + } + + function bootstrap() { + clearRows(); + addRow(sampleRows[0]); + setActivePreset("kcat"); + + renderResultCards([], "kcat"); + renderPreviewTable([]); + + setStatus("Ready when you are.", "ok"); + setupEvents(); + setupFaq(); + setupReveal(); + fetchReady(); + } + + bootstrap(); +})(); diff --git a/catpred/web/static/index.html b/catpred/web/static/index.html new file mode 100644 index 0000000..2ffa804 --- /dev/null +++ b/catpred/web/static/index.html @@ -0,0 +1,214 @@ + + + + + + CatPred | Enzyme Kinetics Prediction + + + + + + + + + +
+
+ + + CatPred + + + + + Start prediction +
+
+ +
+
+
+

AI for Enzyme Kinetics

+

+ Get kinetic estimates in minutes, + not days. +

+

+ CatPred predicts kcat, Km, and Ki from substrate + SMILES and enzyme sequence inputs, including uncertainty for + confidence-aware decisions. +

+ +
+ Run prediction + Quick guide +
+ +
    +
  • + 3 parameters + kcat / Km / Ki +
    • +
  • + Batch ready + Run one or multiple rows +
    • +
+
+ + +
+ +
+
+

Prediction Studio

+

Enter inputs and run

+

+ Choose a target, submit one or many rows, and review predictions with uncertainty. +

+ +
+ Service status + Checking... + +
+
+ +
+
+

Inputs

+ +
+ + + +
+ +
+ +
+ + + +
+ +

+ Each row needs: SMILES, sequence, and a Sequence ID. + Sequence IDs auto-fill as seq_001, seq_002, etc. (editable). + Use one Sequence ID per unique sequence. No structure file upload is required. + For CSV/API usage, this field maps to pdbpath. +

+
+ +
+

Results

+
Ready when you are.
+ +
+
+

Run a prediction to see kinetic estimates here.

+
+
+ +
+ View detailed output table +
+
+
+
+
+
+
+ +
+
+
+

Quick Guide

+

Three rules for reliable runs

+
+ +
+
+ 01 +

Use clean inputs

+

+ Each row needs valid SMILES, uppercase amino acid sequence, + and a Sequence ID (auto-filled, internal field: pdbpath). +

+
+
+ 02 +

Keep sequence IDs consistent

+

+ Reuse the same Sequence ID only for the same sequence. Different sequences should + use different IDs. +

+
+
+ 03 +

Read uncertainty first

+

+ Prioritize results with lower uncertainty before selecting candidates for experiments. +

+
+
+

+ Tip: use Load sample in the predictor to test your environment end-to-end before + running larger batches. +

+
+
+
+ + Run prediction + + + + diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 0000000..621e861 --- /dev/null +++ b/requirements.txt @@ -0,0 +1,3 @@ +fastapi>=0.95,<0.100 +pydantic>=1.10,<2.0 +pandas>=1.5,<2.3 diff --git a/setup.cfg b/setup.cfg index 3db702f..b61db52 100644 --- a/setup.cfg +++ b/setup.cfg @@ -60,4 +60,8 @@ web = uvicorn>=0.22,<1.0 [options.package_data] -catpred = py.typed +catpred = + py.typed + web/static/*.html + web/static/*.css + web/static/*.js diff --git a/vercel.json b/vercel.json new file mode 100644 index 0000000..4582896 --- /dev/null +++ b/vercel.json @@ -0,0 +1,21 @@ +{ + "$schema": "https://openapi.vercel.sh/vercel.json", + "version": 2, + "builds": [ + { + "src": "api/index.py", + "use": "@vercel/python" + } + ], + "functions": { + "api/index.py": { + "maxDuration": 300 + } + }, + "routes": [ + { + "src": "/(.*)", + "dest": "api/index.py" + } + ] +} From cd655e0810b629a1bce4ede1fc9f7d644511ba30 Mon Sep 17 00:00:00 2001 From: theproteinbot Date: Sat, 28 Feb 2026 23:56:37 -0500 Subject: [PATCH 11/34] docs+deploy: add modal endpoint scaffold for vercel backend --- .env.vercel | 4 ++ README.md | 41 +++++++++++++++ modal_app.py | 141 +++++++++++++++++++++++++++++++++++++++++++++++++++ 3 files changed, 186 insertions(+) create mode 100644 .env.vercel create mode 100644 modal_app.py diff --git a/.env.vercel b/.env.vercel new file mode 100644 index 0000000..7db578e --- /dev/null +++ b/.env.vercel @@ -0,0 +1,4 @@ +CATPRED_DEFAULT_BACKEND=modal +CATPRED_MODAL_ENDPOINT=https:// +CATPRED_MODAL_TOKEN= +CATPRED_MODAL_FALLBACK_TO_LOCAL=0 diff --git a/README.md b/README.md index 00c08c2..1275805 100644 --- a/README.md +++ b/README.md @@ -207,6 +207,47 @@ Notes: - Local checkpoint-based inference is not recommended on Vercel serverless due runtime/dependency limits. - If `CATPRED_MODAL_ENDPOINT` is not configured, the UI still loads but prediction requests will be limited by backend readiness. +#### Deploy a Modal endpoint for Vercel + +This repo includes `modal_app.py`, a Modal `POST` endpoint compatible with CatPred's `/predict` modal backend contract. + +1. Install and authenticate Modal CLI: + +```bash +pip install modal +modal setup +``` + +2. Create/upload checkpoints into a Modal Volume (one-time): + +```bash +modal volume create catpred-checkpoints +modal volume put catpred-checkpoints ./checkpoints/kcat kcat +modal volume put catpred-checkpoints ./checkpoints/km km +modal volume put catpred-checkpoints ./checkpoints/ki ki +``` + +3. (Recommended) create a secret token for endpoint auth: + +```bash +modal secret create catpred-modal-auth CATPRED_MODAL_AUTH_TOKEN="" +``` + +4. Deploy: + +```bash +modal deploy modal_app.py +``` + +After deploy, copy the printed endpoint URL (for function `predict`) and set Vercel variables: + +```bash +CATPRED_DEFAULT_BACKEND=modal +CATPRED_MODAL_ENDPOINT=https:// +CATPRED_MODAL_TOKEN= +CATPRED_MODAL_FALLBACK_TO_LOCAL=0 +``` + ### ๐Ÿงช Fine-Tuning On Custom Data You can fine-tune CatPred on your own regression targets using `train.py`. diff --git a/modal_app.py b/modal_app.py new file mode 100644 index 0000000..2769457 --- /dev/null +++ b/modal_app.py @@ -0,0 +1,141 @@ +from __future__ import annotations + +from pathlib import Path +import os +import tempfile +from typing import Any + +from fastapi import Header, HTTPException +import modal +from pydantic import BaseModel, Field + + +image = ( + modal.Image.debian_slim(python_version="3.10") + .pip_install( + "fastapi[standard]>=0.115,<1.0", + "pydantic>=1.10,<2.0", + "pandas>=1.5,<2.3", + "numpy>=1.26,<2.3", + "scikit-learn>=1.3,<1.7", + "scipy>=1.10,<1.16", + "torch>=2.1,<2.7", + "tqdm>=4.66", + "typed-argument-parser>=1.10", + "rdkit-pypi>=2022.9.5", + "descriptastorus>=2.6", + "transformers>=4.47,<5", + "sentencepiece>=0.2.0", + "fair-esm==2.0.0", + "progres==0.2.7", + "rotary-embedding-torch==0.6.5", + "ipdb==0.13.13", + "pandas-flavor>=0.6.0", + ) + .add_local_python_source("catpred") + .add_local_dir("scripts", remote_path="/root/scripts") + .add_local_file("predict.py", remote_path="/root/predict.py") +) + +app = modal.App("catpred-modal-api", image=image) +checkpoints_volume = modal.Volume.from_name("catpred-checkpoints", create_if_missing=True) + + +class PredictPayload(BaseModel): + parameter: str = Field(..., description="One of: kcat, km, ki") + checkpoint_dir: str = Field(..., description="Checkpoint subdirectory inside /checkpoints") + use_gpu: bool = Field(default=False) + input_rows: list[dict[str, Any]] = Field(default_factory=list) + input_filename: str | None = Field(default=None) + + +def _safe_checkpoint_path(raw_checkpoint_dir: str) -> Path: + checkpoint_root = Path("/checkpoints").resolve() + checkpoint_dir = (checkpoint_root / raw_checkpoint_dir).resolve() + try: + checkpoint_dir.relative_to(checkpoint_root) + except ValueError as exc: + raise ValueError("checkpoint_dir must stay inside /checkpoints.") from exc + if not checkpoint_dir.is_dir(): + raise ValueError(f'Checkpoint directory not found: "{checkpoint_dir}"') + return checkpoint_dir + + +@app.function( + timeout=60 * 15, + cpu=4.0, + memory=16384, + volumes={"/checkpoints": checkpoints_volume}, +) +@modal.fastapi_endpoint(method="POST", docs=True) +def predict( + payload: PredictPayload, + authorization: str | None = Header(default=None), +) -> dict[str, Any]: + import pandas as pd + + from catpred.inference.service import run_prediction_pipeline + from catpred.inference.types import PredictionRequest + + expected_token = os.environ.get("CATPRED_MODAL_AUTH_TOKEN") + if expected_token: + provided = "" + if authorization: + lower = authorization.lower() + if lower.startswith("bearer "): + provided = authorization[7:].strip() + else: + provided = authorization.strip() + if provided != expected_token: + raise HTTPException(status_code=401, detail="Unauthorized") + + if not payload.input_rows: + raise HTTPException(status_code=400, detail="input_rows cannot be empty.") + + parameter = payload.parameter.lower() + if parameter not in {"kcat", "km", "ki"}: + raise HTTPException(status_code=400, detail="parameter must be one of: kcat, km, ki.") + + try: + checkpoint_dir = _safe_checkpoint_path(payload.checkpoint_dir) + except ValueError as exc: + raise HTTPException(status_code=400, detail=str(exc)) from exc + + safe_name = Path(payload.input_filename or "api_input.csv").name + if not safe_name.endswith(".csv"): + safe_name = f"{safe_name}.csv" + + runtime_dir = Path("/tmp/catpred-modal").resolve() + runtime_dir.mkdir(parents=True, exist_ok=True) + fd, tmp_input_path = tempfile.mkstemp(prefix="modal_input_", suffix=".csv", dir=str(runtime_dir)) + os.close(fd) + + try: + pd.DataFrame(payload.input_rows).to_csv(tmp_input_path, index=False) + + request_obj = PredictionRequest( + parameter=parameter, + input_file=tmp_input_path, + checkpoint_dir=str(checkpoint_dir), + use_gpu=payload.use_gpu, + repo_root="/root", + python_executable="python", + ) + results_dir = str((runtime_dir / "results").resolve()) + output_file = run_prediction_pipeline(request_obj, results_dir=results_dir) + output_df = pd.read_csv(output_file) + + return { + "output_rows": output_df.to_dict(orient="records"), + "output_filename": Path(output_file).name, + "row_count": int(len(output_df)), + "backend": "modal", + } + except HTTPException: + raise + except Exception as exc: + raise HTTPException(status_code=500, detail=f"Modal prediction failed: {exc}") from exc + finally: + input_path = Path(tmp_input_path) + if input_path.exists(): + input_path.unlink() From db8a2bb643018f9556ab379419196ce9b8fadb77 Mon Sep 17 00:00:00 2001 From: theproteinbot Date: Sun, 1 Mar 2026 00:12:40 -0500 Subject: [PATCH 12/34] fix(modal): make prediction endpoint runtime-compatible and lazy-load train package --- catpred/train/__init__.py | 99 +++++++++++++++++++++------------------ modal_app.py | 11 +++-- 2 files changed, 61 insertions(+), 49 deletions(-) diff --git a/catpred/train/__init__.py b/catpred/train/__init__.py index d8c18d9..4f66fe7 100644 --- a/catpred/train/__init__.py +++ b/catpred/train/__init__.py @@ -1,47 +1,54 @@ -from .metrics import get_metric_func, prc_auc, bce, rmse, bounded_mse, bounded_mae, \ - bounded_rmse, accuracy, f1_metric, mcc_metric, sid_metric, wasserstein_metric -from .loss_functions import get_loss_func, bounded_mse_loss, \ - mcc_class_loss, mcc_multiclass_loss, sid_loss, wasserstein_loss -from .cross_validate import catpred_train, cross_validate, TRAIN_LOGGER_NAME -from .evaluate import evaluate, evaluate_predictions -from .make_predictions import catpred_predict, make_predictions, load_model, set_features, load_data, predict_and_save -from .molecule_fingerprint import catpred_fingerprint, model_fingerprint -from .predict import predict -from .run_training import run_training -from .train import train +from __future__ import annotations -__all__ = [ - 'catpred_train', - 'cross_validate', - 'TRAIN_LOGGER_NAME', - 'evaluate', - 'evaluate_predictions', - 'catpred_predict', - 'catpred_fingerprint', - 'make_predictions', - 'load_model', - 'set_features', - 'load_data', - 'predict_and_save', - 'predict', - 'run_training', - 'train', - 'get_metric_func', - 'prc_auc', - 'bce', - 'rmse', - 'bounded_mse', - 'bounded_mae', - 'bounded_rmse', - 'accuracy', - 'f1_metric', - 'mcc_metric', - 'sid_metric', - 'wasserstein_metric', - 'get_loss_func', - 'bounded_mse_loss', - 'mcc_class_loss', - 'mcc_multiclass_loss', - 'sid_loss', - 'wasserstein_loss' -] +import importlib + +_LAZY_ATTRS = { + "get_metric_func": ("metrics", "get_metric_func"), + "prc_auc": ("metrics", "prc_auc"), + "bce": ("metrics", "bce"), + "rmse": ("metrics", "rmse"), + "bounded_mse": ("metrics", "bounded_mse"), + "bounded_mae": ("metrics", "bounded_mae"), + "bounded_rmse": ("metrics", "bounded_rmse"), + "accuracy": ("metrics", "accuracy"), + "f1_metric": ("metrics", "f1_metric"), + "mcc_metric": ("metrics", "mcc_metric"), + "sid_metric": ("metrics", "sid_metric"), + "wasserstein_metric": ("metrics", "wasserstein_metric"), + "get_loss_func": ("loss_functions", "get_loss_func"), + "bounded_mse_loss": ("loss_functions", "bounded_mse_loss"), + "mcc_class_loss": ("loss_functions", "mcc_class_loss"), + "mcc_multiclass_loss": ("loss_functions", "mcc_multiclass_loss"), + "sid_loss": ("loss_functions", "sid_loss"), + "wasserstein_loss": ("loss_functions", "wasserstein_loss"), + "catpred_train": ("cross_validate", "catpred_train"), + "cross_validate": ("cross_validate", "cross_validate"), + "TRAIN_LOGGER_NAME": ("cross_validate", "TRAIN_LOGGER_NAME"), + "evaluate": ("evaluate", "evaluate"), + "evaluate_predictions": ("evaluate", "evaluate_predictions"), + "catpred_predict": ("make_predictions", "catpred_predict"), + "make_predictions": ("make_predictions", "make_predictions"), + "load_model": ("make_predictions", "load_model"), + "set_features": ("make_predictions", "set_features"), + "load_data": ("make_predictions", "load_data"), + "predict_and_save": ("make_predictions", "predict_and_save"), + "catpred_fingerprint": ("molecule_fingerprint", "catpred_fingerprint"), + "model_fingerprint": ("molecule_fingerprint", "model_fingerprint"), + "predict": ("predict", "predict"), + "run_training": ("run_training", "run_training"), + "train": ("train", "train"), +} + +__all__ = sorted(_LAZY_ATTRS.keys()) + + +def __getattr__(name: str): + target = _LAZY_ATTRS.get(name) + if target is None: + raise AttributeError(f"module 'catpred.train' has no attribute '{name}'") + + module_name, attr_name = target + module = importlib.import_module(f".{module_name}", __name__) + value = getattr(module, attr_name) + globals()[name] = value + return value diff --git a/modal_app.py b/modal_app.py index 2769457..b1bdee0 100644 --- a/modal_app.py +++ b/modal_app.py @@ -3,7 +3,7 @@ from pathlib import Path import os import tempfile -from typing import Any +from typing import Any, Optional from fastapi import Header, HTTPException import modal @@ -12,6 +12,11 @@ image = ( modal.Image.debian_slim(python_version="3.10") + .apt_install( + "libxrender1", + "libxext6", + "libsm6", + ) .pip_install( "fastapi[standard]>=0.115,<1.0", "pydantic>=1.10,<2.0", @@ -46,7 +51,7 @@ class PredictPayload(BaseModel): checkpoint_dir: str = Field(..., description="Checkpoint subdirectory inside /checkpoints") use_gpu: bool = Field(default=False) input_rows: list[dict[str, Any]] = Field(default_factory=list) - input_filename: str | None = Field(default=None) + input_filename: Optional[str] = Field(default=None) def _safe_checkpoint_path(raw_checkpoint_dir: str) -> Path: @@ -70,7 +75,7 @@ def _safe_checkpoint_path(raw_checkpoint_dir: str) -> Path: @modal.fastapi_endpoint(method="POST", docs=True) def predict( payload: PredictPayload, - authorization: str | None = Header(default=None), + authorization: Optional[str] = Header(default=None), ) -> dict[str, Any]: import pandas as pd From da7b8673bc21a33cf6707bf6c35d39d14815c29f Mon Sep 17 00:00:00 2001 From: theproteinbot Date: Sun, 1 Mar 2026 00:12:54 -0500 Subject: [PATCH 13/34] chore(vercel): prefill env file with deployed modal endpoint --- .env.vercel | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/.env.vercel b/.env.vercel index 7db578e..2b705e4 100644 --- a/.env.vercel +++ b/.env.vercel @@ -1,4 +1,4 @@ CATPRED_DEFAULT_BACKEND=modal -CATPRED_MODAL_ENDPOINT=https:// -CATPRED_MODAL_TOKEN= +CATPRED_MODAL_ENDPOINT=https://kaalabhairava2026--catpred-modal-api-predict.modal.run +CATPRED_MODAL_TOKEN= CATPRED_MODAL_FALLBACK_TO_LOCAL=0 From 0c902da55bae4cde248254836b5f005549f1aad2 Mon Sep 17 00:00:00 2001 From: theproteinbot Date: Sun, 1 Mar 2026 00:18:21 -0500 Subject: [PATCH 14/34] fix(vercel): remove functions/builds conflict --- vercel.json | 5 ----- 1 file changed, 5 deletions(-) diff --git a/vercel.json b/vercel.json index 4582896..c146952 100644 --- a/vercel.json +++ b/vercel.json @@ -7,11 +7,6 @@ "use": "@vercel/python" } ], - "functions": { - "api/index.py": { - "maxDuration": 300 - } - }, "routes": [ { "src": "/(.*)", From a89216cff6bd7a5a5bfb42fc59d4d0a59c87db91 Mon Sep 17 00:00:00 2001 From: theproteinbot Date: Sun, 1 Mar 2026 00:31:38 -0500 Subject: [PATCH 15/34] ci: add GitHub Actions validation and Modal auto-deploy --- .github/workflows/ci.yml | 42 ++++++++++++++++++++++++++++++ .github/workflows/deploy-modal.yml | 42 ++++++++++++++++++++++++++++++ README.md | 25 ++++++++++++++++++ 3 files changed, 109 insertions(+) create mode 100644 .github/workflows/ci.yml create mode 100644 .github/workflows/deploy-modal.yml diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml new file mode 100644 index 0000000..1467ea9 --- /dev/null +++ b/.github/workflows/ci.yml @@ -0,0 +1,42 @@ +name: CI + +on: + pull_request: + push: + branches: + - main + +jobs: + validate: + runs-on: ubuntu-latest + permissions: + contents: read + steps: + - name: Checkout + uses: actions/checkout@v4 + + - name: Set up Python + uses: actions/setup-python@v5 + with: + python-version: "3.10" + + - name: Install minimal runtime dependencies + run: | + python -m pip install --upgrade pip + pip install -r requirements.txt + + - name: Compile Python sources + run: | + git ls-files '*.py' | xargs -r python -m py_compile + + - name: Smoke test API entrypoints + run: | + python - <<'PY' + from catpred.web.app import create_app + from api.index import app as vercel_app + + api_app = create_app() + assert api_app.title == "CatPred API" + assert vercel_app is not None + print("API smoke checks passed.") + PY diff --git a/.github/workflows/deploy-modal.yml b/.github/workflows/deploy-modal.yml new file mode 100644 index 0000000..88735bb --- /dev/null +++ b/.github/workflows/deploy-modal.yml @@ -0,0 +1,42 @@ +name: Deploy Modal + +on: + workflow_dispatch: + push: + branches: + - main + paths: + - ".github/workflows/deploy-modal.yml" + - "modal_app.py" + - "predict.py" + - "scripts/create_pdbrecords.py" + - "catpred/**" + +jobs: + deploy: + if: ${{ secrets.MODAL_TOKEN_ID != '' && secrets.MODAL_TOKEN_SECRET != '' }} + runs-on: ubuntu-latest + permissions: + contents: read + concurrency: + group: modal-production-deploy + cancel-in-progress: true + steps: + - name: Checkout + uses: actions/checkout@v4 + + - name: Set up Python + uses: actions/setup-python@v5 + with: + python-version: "3.10" + + - name: Install Modal CLI + run: | + python -m pip install --upgrade pip + pip install "modal>=0.73" + + - name: Deploy modal_app.py + env: + MODAL_TOKEN_ID: ${{ secrets.MODAL_TOKEN_ID }} + MODAL_TOKEN_SECRET: ${{ secrets.MODAL_TOKEN_SECRET }} + run: modal deploy modal_app.py diff --git a/README.md b/README.md index 1275805..080ba25 100644 --- a/README.md +++ b/README.md @@ -248,6 +248,31 @@ CATPRED_MODAL_TOKEN= CATPRED_MODAL_FALLBACK_TO_LOCAL=0 ``` +#### CI/CD (GitHub Actions + Vercel + Modal) + +This repo includes two GitHub Actions workflows: + +- `.github/workflows/ci.yml` + - Runs on every PR and push to `main`. + - Installs minimal API dependencies, compiles all Python files, and smoke-tests API entrypoints. +- `.github/workflows/deploy-modal.yml` + - Runs on push to `main` when backend files change (and manually via `workflow_dispatch`). + - Deploys `modal_app.py` automatically. + +To enable automatic Modal deploys from GitHub Actions, add repository secrets: + +- `MODAL_TOKEN_ID` +- `MODAL_TOKEN_SECRET` + +Create these from Modal: + +1. Go to [https://modal.com/settings/tokens](https://modal.com/settings/tokens). +2. Create a token with deploy permissions for your workspace. +3. Copy token ID and secret into GitHub repo settings: + `Settings -> Secrets and variables -> Actions -> New repository secret`. + +Vercel deployment remains automatic from the connected GitHub branch (`main`). + ### ๐Ÿงช Fine-Tuning On Custom Data You can fine-tune CatPred on your own regression targets using `train.py`. From 75fe7b52e31b070aaa398fa092bb6bbaebe8bba3 Mon Sep 17 00:00:00 2001 From: theproteinbot Date: Sun, 1 Mar 2026 00:35:57 -0500 Subject: [PATCH 16/34] fix(web): support modal-only deployments and backend-aware fallback --- catpred/web/static/catpred.js | 86 ++++++++++++++++++++++++++++++----- 1 file changed, 75 insertions(+), 11 deletions(-) diff --git a/catpred/web/static/catpred.js b/catpred/web/static/catpred.js index 707d620..bac7128 100644 --- a/catpred/web/static/catpred.js +++ b/catpred/web/static/catpred.js @@ -31,7 +31,14 @@ const supportedParameters = ["kcat", "km", "ki"]; let availableCheckpointParams = new Set(supportedParameters); + let localCheckpointParams = new Set(supportedParameters); let selectedParameter = "kcat"; + const runtimeState = { + defaultBackend: "local", + modalReady: false, + localReady: false, + fallbackToLocalEnabled: false, + }; const runningPhaseMessages = [ "validating input", "building protein records", @@ -204,16 +211,19 @@ return null; } - function setParameterAvailability(availableCheckpoints) { + function parseAvailableCheckpointParams(availableCheckpoints) { if (availableCheckpoints && typeof availableCheckpoints === "object") { - availableCheckpointParams = new Set( + return new Set( Object.keys(availableCheckpoints) .map((key) => String(key).toLowerCase()) .filter((key) => supportedParameters.includes(key)) ); - } else { - availableCheckpointParams = new Set(supportedParameters); } + return new Set(supportedParameters); + } + + function setParameterAvailability(availableCheckpoints) { + availableCheckpointParams = parseAvailableCheckpointParams(availableCheckpoints); if (presetButtons && presetButtons.length) { presetButtons.forEach((btn) => { @@ -235,6 +245,35 @@ return availableCheckpointParams.has(String(paramValue || "").toLowerCase()); } + function chooseRequestBackend() { + if (runtimeState.defaultBackend === "modal" && runtimeState.modalReady) { + return "modal"; + } + if (runtimeState.defaultBackend === "local" && localCheckpointParams.size > 0) { + return "local"; + } + if (runtimeState.modalReady) { + return "modal"; + } + if (runtimeState.localReady) { + return "local"; + } + return runtimeState.defaultBackend || "local"; + } + + function shouldFallbackToLocal(requestBackend, targetParam) { + if (requestBackend !== "modal") { + return false; + } + if (!runtimeState.fallbackToLocalEnabled) { + return false; + } + if (!runtimeState.localReady) { + return false; + } + return localCheckpointParams.has(String(targetParam || "").toLowerCase()); + } + function formatSequenceId(index) { const safeIndex = Math.max(1, Number(index) || 1); return "seq_" + String(safeIndex).padStart(3, "0"); @@ -393,6 +432,7 @@ function buildPayload(rows) { const target = getSelectedParameter(); + const requestBackend = chooseRequestBackend(); return { parameter: target, @@ -400,7 +440,8 @@ input_rows: rows, use_gpu: false, results_dir: "web-app", - fallback_to_local: true, + backend: requestBackend, + fallback_to_local: shouldFallbackToLocal(requestBackend, target), }; } @@ -533,18 +574,41 @@ return; } - setParameterAvailability(data && data.api ? data.api.available_checkpoints : null); + const backends = data && data.backends ? data.backends : {}; + runtimeState.defaultBackend = data && data.default_backend ? String(data.default_backend) : "local"; + runtimeState.modalReady = Boolean(backends.modal && backends.modal.ready); + runtimeState.localReady = Boolean(backends.local && backends.local.ready); + runtimeState.fallbackToLocalEnabled = Boolean(data && data.fallback_to_local_enabled); + + localCheckpointParams = parseAvailableCheckpointParams( + data && data.api ? data.api.available_checkpoints : null + ); + + if (localCheckpointParams.size > 0) { + setParameterAvailability(Object.fromEntries(Array.from(localCheckpointParams).map((key) => [key, key]))); + } else if (runtimeState.modalReady) { + setParameterAvailability({ kcat: "kcat", km: "km", ki: "ki" }); + } else { + setParameterAvailability({}); + } + const availableParams = Array.from(availableCheckpointParams.values()); + const localParams = Array.from(localCheckpointParams.values()); if (data && data.ready) { const backend = data.default_backend ? String(data.default_backend) : "default"; - const hint = availableParams.length - ? "Backend: " + backend + " | Checkpoints: " + availableParams.join(", ") - : "Backend: " + backend + " | No local checkpoints found"; + let hint = ""; + if (localParams.length) { + hint = "Backend: " + backend + " | Checkpoints: " + localParams.join(", "); + } else if (runtimeState.modalReady) { + hint = "Backend: " + backend + " | Remote checkpoints: " + availableParams.join(", "); + } else { + hint = "Backend: " + backend + " | No local checkpoints found"; + } setServiceState("Online", hint, "ok"); } else { - const hint = availableParams.length - ? "Backend configuration needed" + const hint = runtimeState.modalReady + ? "Backend available in limited mode" : "Backend configuration needed | No local checkpoints found"; setServiceState("Limited", hint, "error"); } From d8a095dac47902afcf951dd12de55147a941238d Mon Sep 17 00:00:00 2001 From: theproteinbot Date: Sun, 1 Mar 2026 00:42:37 -0500 Subject: [PATCH 17/34] fix(web): timeout long predict calls and cache-bust static assets --- catpred/web/static/catpred.js | 24 ++++++++++++++++++++++-- catpred/web/static/index.html | 4 ++-- 2 files changed, 24 insertions(+), 4 deletions(-) diff --git a/catpred/web/static/catpred.js b/catpred/web/static/catpred.js index bac7128..4abe371 100644 --- a/catpred/web/static/catpred.js +++ b/catpred/web/static/catpred.js @@ -15,6 +15,7 @@ const serviceHint = document.getElementById("serviceHint"); const presetButtons = document.querySelectorAll(".preset-btn"); + const predictionTimeoutMs = 120000; const sampleRows = [ { @@ -637,6 +638,11 @@ startRunningFeedback(payload); await new Promise((resolve) => window.requestAnimationFrame(resolve)); + const requestController = new AbortController(); + const requestTimeout = window.setTimeout(() => { + requestController.abort(); + }, predictionTimeoutMs); + try { const response = await fetch("/predict", { method: "POST", @@ -644,6 +650,7 @@ "Content-Type": "application/json", Accept: "application/json", }, + signal: requestController.signal, body: JSON.stringify(payload), }); @@ -667,12 +674,25 @@ renderPreviewTable(data.preview_rows || []); const elapsedLabel = formatElapsed(getElapsedSeconds()); setStatus("Prediction complete โ€ข " + elapsedLabel, "ok"); - } catch (_err) { + } catch (err) { renderResultCards([], payload.parameter); renderPreviewTable([]); const elapsedLabel = formatElapsed(getElapsedSeconds()); - setStatus("Network error while running prediction. (" + elapsedLabel + ")", "error"); + if (err && err.name === "AbortError") { + const timeoutLabel = formatElapsed(Math.floor(predictionTimeoutMs / 1000)); + setStatus( + "Prediction timed out after " + + timeoutLabel + + ". This is often a cold-start delay; retry once or check backend logs. (" + + elapsedLabel + + ")", + "error" + ); + } else { + setStatus("Network error while running prediction. (" + elapsedLabel + ")", "error"); + } } finally { + window.clearTimeout(requestTimeout); stopRunningFeedback(); runStartedAtMs = null; } diff --git a/catpred/web/static/index.html b/catpred/web/static/index.html index 2ffa804..e4374fe 100644 --- a/catpred/web/static/index.html +++ b/catpred/web/static/index.html @@ -14,7 +14,7 @@ href="https://fonts.googleapis.com/css2?family=Newsreader:ital,opsz,wght@0,6..72,300;0,6..72,400;1,6..72,300;1,6..72,400&family=Outfit:wght@200;300;400;500&family=IBM+Plex+Mono:wght@300;400&display=swap" rel="stylesheet" /> - + @@ -209,6 +209,6 @@

Read uncertainty first

Run prediction - + From 39d6bdcf0401e767fa7f3b04fbe472860e805704 Mon Sep 17 00:00:00 2001 From: theproteinbot Date: Sun, 1 Mar 2026 15:36:08 -0500 Subject: [PATCH 18/34] feat(web): streamline production landing and improve scrolling --- catpred/web/static/catpred-favicon.svg | 11 ++ catpred/web/static/catpred.css | 146 +++++++---------------- catpred/web/static/index.html | 156 ++++++------------------- setup.cfg | 1 + 4 files changed, 92 insertions(+), 222 deletions(-) create mode 100644 catpred/web/static/catpred-favicon.svg diff --git a/catpred/web/static/catpred-favicon.svg b/catpred/web/static/catpred-favicon.svg new file mode 100644 index 0000000..3c33710 --- /dev/null +++ b/catpred/web/static/catpred-favicon.svg @@ -0,0 +1,11 @@ + + + + + + + + + + + diff --git a/catpred/web/static/catpred.css b/catpred/web/static/catpred.css index 90bb76f..51d546b 100644 --- a/catpred/web/static/catpred.css +++ b/catpred/web/static/catpred.css @@ -34,8 +34,7 @@ html { scroll-behavior: smooth; height: 100%; - scroll-snap-type: y mandatory; - overscroll-behavior-y: contain; + overscroll-behavior-y: auto; } body { @@ -146,17 +145,27 @@ body { } .hero { - min-height: calc(100svh - 78px); + min-height: auto; display: grid; - grid-template-columns: 0.96fr 1.04fr; - gap: clamp(1.3rem, 4vw, 4.2rem); - align-items: center; - padding-block: clamp(2.2rem, 7.4vw, 5.8rem); + grid-template-columns: minmax(0, 0.92fr) minmax(0, 1.08fr); + gap: 1rem clamp(1rem, 3.2vw, 2.2rem); + align-items: start; + padding-block: clamp(1.6rem, 5.2vw, 3.4rem); } .hero-copy { position: relative; z-index: 1; + padding-top: 0.3rem; +} + +.hero-studio { + align-self: start; +} + +.hero > .output-card { + grid-column: 1 / -1; + margin-top: 0.45rem; } .eyebrow { @@ -171,9 +180,9 @@ body { .hero h1 { font-family: "Newsreader", serif; - font-size: clamp(2.4rem, 5.4vw, 4.8rem); + font-size: clamp(1.78rem, 3.3vw, 2.75rem); font-weight: 300; - line-height: 1.05; + line-height: 1.08; letter-spacing: -0.03em; margin-bottom: 1.25rem; } @@ -188,7 +197,7 @@ body { font-size: clamp(0.98rem, 1.9vw, 1.07rem); color: var(--text-secondary); line-height: 1.75; - margin-bottom: 1.8rem; + margin-bottom: 1.1rem; } .lead code, @@ -208,7 +217,7 @@ body { display: flex; flex-wrap: wrap; gap: 0.8rem; - margin-bottom: 1.75rem; + margin-bottom: 1.1rem; } .btn { @@ -293,9 +302,10 @@ body { .hero-stats { list-style: none; display: grid; - grid-template-columns: repeat(3, minmax(0, 1fr)); + grid-template-columns: repeat(auto-fit, minmax(180px, 1fr)); gap: 0.7rem; - max-width: 720px; + margin-top: 0.86rem; + max-width: 880px; } .hero-stats li { @@ -320,76 +330,12 @@ body { color: var(--text-secondary); } -.hero-visual-wrap { - position: relative; - isolation: isolate; -} - -.hero-visual { - position: relative; - width: min(560px, 100%); - aspect-ratio: 1 / 1; - margin-inline: auto; -} - -.hero-visual::before { - content: ""; - position: absolute; - inset: 10%; - border-radius: 999px; - background: radial-gradient(circle, rgba(122, 158, 142, 0.16) 0%, rgba(196, 137, 122, 0.08) 42%, transparent 74%); - filter: blur(24px); - animation: breathe 9s ease-in-out infinite; -} - -@keyframes breathe { - 0%, - 100% { - transform: scale(1); - opacity: 0.85; - } - - 50% { - transform: scale(1.06); - opacity: 1; - } -} - -.protein-structure { - width: 100%; - height: 100%; - position: relative; - z-index: 1; - animation: proteinFloat 18s ease-in-out infinite; -} - -@keyframes proteinFloat { - 0%, - 100% { - transform: translateY(0) rotate(0deg); - } - - 30% { - transform: translateY(-8px) rotate(0.4deg); - } - - 60% { - transform: translateY(3px) rotate(-0.25deg); - } - - 85% { - transform: translateY(-4px) rotate(0.18deg); - } -} - .section { padding-block: clamp(2.8rem, 7vw, 5.4rem); } .snap-section { - min-height: 100svh; - scroll-snap-align: start; - scroll-snap-stop: always; + min-height: auto; scroll-margin-top: 84px; } @@ -427,6 +373,7 @@ body { .service-strip { margin-top: 0.9rem; display: inline-flex; + flex-wrap: wrap; align-items: center; gap: 0.6rem; border: 1px solid var(--border); @@ -471,13 +418,6 @@ body { font-size: 0.82rem; } -.predict-layout { - display: grid; - grid-template-columns: 0.96fr 1.04fr; - gap: 1rem; - align-items: start; -} - .card { border: 1px solid var(--border); border-radius: var(--radius-lg); @@ -494,8 +434,7 @@ body { } .input-card { - position: sticky; - top: 98px; + position: static; } .preset-row { @@ -1019,14 +958,14 @@ button:focus-visible { } .reveal { - opacity: 0; - transform: translateY(20px); - transition: opacity 0.48s ease, transform 0.48s ease; + opacity: 1; + transform: none; + transition: none; } .reveal.show { opacity: 1; - transform: translateY(0); + transform: none; } @keyframes spin { @@ -1042,28 +981,22 @@ button:focus-visible { } @media (max-width: 1120px) { - html { - scroll-snap-type: y proximity; - } - .hero { grid-template-columns: 1fr; min-height: auto; } - .hero-visual { - width: min(500px, 100%); + .hero > .output-card { + margin-top: 0; } - .predict-layout, .steps, - .hero-stats, .metric-grid { grid-template-columns: 1fr; } - .input-card { - position: static; + .hero-stats { + grid-template-columns: repeat(2, minmax(0, 1fr)); } } @@ -1080,9 +1013,14 @@ button:focus-visible { padding-top: 1.5rem; } - .snap-section { - min-height: auto; - scroll-snap-stop: normal; + .hero-stats { + grid-template-columns: 1fr; + } + + .service-strip { + display: flex; + border-radius: 14px; + padding: 0.5rem 0.62rem; } .input-table { diff --git a/catpred/web/static/index.html b/catpred/web/static/index.html index e4374fe..9e2f04e 100644 --- a/catpred/web/static/index.html +++ b/catpred/web/static/index.html @@ -14,14 +14,15 @@ href="https://fonts.googleapis.com/css2?family=Newsreader:ital,opsz,wght@0,6..72,300;0,6..72,400;1,6..72,300;1,6..72,400&family=Outfit:wght@200;300;400;500&family=IBM+Plex+Mono:wght@300;400&display=swap" rel="stylesheet" /> - + +
- +
-
+

AI for Enzyme Kinetics

- Get kinetic estimates in minutes, - not days. + Predict kcat, Km, and Ki + for production enzyme workflows.

- CatPred predicts kcat, Km, and Ki from substrate - SMILES and enzyme sequence inputs, including uncertainty for - confidence-aware decisions. + Enter substrate SMILES and enzyme sequence, then run a single + prediction or a small batch with uncertainty-aware output.

- Run prediction - Quick guide + Go to form +
+ +
+ Service status + Checking... +
  • - 3 parameters + 3 targets kcat / Km / Ki
  • Batch ready - Run one or multiple rows + Add and run multiple rows +
    • +
  • + Uncertainty + Total and epistemic SD reported
- -
- -
-
-

Prediction Studio

-

Enter inputs and run

-

- Choose a target, submit one or many rows, and review predictions with uncertainty. -

- -
- Service status - Checking... - -
-
- -
+
-

Inputs

+

Prediction Studio

@@ -146,65 +104,27 @@

Inputs

For CSV/API usage, this field maps to pdbpath.

- -
-

Results

-
Ready when you are.
- -
-
-

Run a prediction to see kinetic estimates here.

-
-
- -
- View detailed output table -
-
-
-
-
-
-
-
-
-

Quick Guide

-

Three rules for reliable runs

-
+
+

Results

+
Ready when you are.
-
-
- 01 -

Use clean inputs

-

- Each row needs valid SMILES, uppercase amino acid sequence, - and a Sequence ID (auto-filled, internal field: pdbpath). -

-
-
- 02 -

Keep sequence IDs consistent

-

- Reuse the same Sequence ID only for the same sequence. Different sequences should - use different IDs. -

-
-
- 03 -

Read uncertainty first

-

- Prioritize results with lower uncertainty before selecting candidates for experiments. -

+
+
+

Run a prediction to see kinetic estimates here.

-

- Tip: use Load sample in the predictor to test your environment end-to-end before - running larger batches. -

-
+ +
+ View detailed output table +
+
+
+
+
+
Run prediction diff --git a/setup.cfg b/setup.cfg index b61db52..b2c6b99 100644 --- a/setup.cfg +++ b/setup.cfg @@ -65,3 +65,4 @@ catpred = web/static/*.html web/static/*.css web/static/*.js + web/static/*.svg From eec74d56682dc66dfb06bbe738db293281a443e2 Mon Sep 17 00:00:00 2001 From: theproteinbot Date: Mon, 9 Mar 2026 22:54:30 -0400 Subject: [PATCH 19/34] refactor(web): reduce landing page to prediction studio --- catpred/web/static/catpred.css | 282 ++++++++------------------------- catpred/web/static/index.html | 94 ++++++----- 2 files changed, 109 insertions(+), 267 deletions(-) diff --git a/catpred/web/static/catpred.css b/catpred/web/static/catpred.css index 51d546b..ac188aa 100644 --- a/catpred/web/static/catpred.css +++ b/catpred/web/static/catpred.css @@ -144,28 +144,39 @@ body { color: var(--text); } -.hero { - min-height: auto; - display: grid; - grid-template-columns: minmax(0, 0.92fr) minmax(0, 1.08fr); - gap: 1rem clamp(1rem, 3.2vw, 2.2rem); - align-items: start; - padding-block: clamp(1.6rem, 5.2vw, 3.4rem); +.studio { + padding-block: clamp(1.2rem, 4vw, 2.4rem); } -.hero-copy { - position: relative; - z-index: 1; - padding-top: 0.3rem; +.studio-head { + display: flex; + align-items: end; + justify-content: space-between; + gap: 1rem; + margin-bottom: 1rem; +} + +.studio-head h1 { + font-family: "Newsreader", serif; + font-size: clamp(1.35rem, 2.2vw, 1.9rem); + font-weight: 400; + line-height: 1.05; + letter-spacing: -0.03em; } -.hero-studio { - align-self: start; +.studio-meta { + display: flex; + align-items: center; + justify-content: flex-end; + gap: 0.75rem; + flex-wrap: wrap; } -.hero > .output-card { - grid-column: 1 / -1; - margin-top: 0.45rem; +.workspace-grid { + display: grid; + grid-template-columns: minmax(0, 0.98fr) minmax(0, 1.02fr); + gap: 1rem; + align-items: start; } .eyebrow { @@ -178,28 +189,6 @@ body { margin-bottom: 1.2rem; } -.hero h1 { - font-family: "Newsreader", serif; - font-size: clamp(1.78rem, 3.3vw, 2.75rem); - font-weight: 300; - line-height: 1.08; - letter-spacing: -0.03em; - margin-bottom: 1.25rem; -} - -.hero h1 em { - font-style: italic; - color: var(--protein-sage); -} - -.lead { - max-width: 60ch; - font-size: clamp(0.98rem, 1.9vw, 1.07rem); - color: var(--text-secondary); - line-height: 1.75; - margin-bottom: 1.1rem; -} - .lead code, .muted code, .helper code, @@ -213,13 +202,6 @@ body { background: rgba(255, 255, 255, 0.66); } -.hero-actions { - display: flex; - flex-wrap: wrap; - gap: 0.8rem; - margin-bottom: 1.1rem; -} - .btn { display: inline-flex; align-items: center; @@ -295,41 +277,6 @@ body { border-color: var(--text); } -.nav-cta { - padding-inline: 1.1rem; -} - -.hero-stats { - list-style: none; - display: grid; - grid-template-columns: repeat(auto-fit, minmax(180px, 1fr)); - gap: 0.7rem; - margin-top: 0.86rem; - max-width: 880px; -} - -.hero-stats li { - border: 1px solid var(--border); - background: rgba(255, 255, 255, 0.68); - border-radius: var(--radius-md); - padding: 0.76rem 0.78rem; -} - -.hero-stats strong { - display: block; - font-family: "IBM Plex Mono", monospace; - font-size: 0.94rem; - font-weight: 400; - color: var(--text); -} - -.hero-stats span { - display: block; - margin-top: 0.25rem; - font-size: 0.84rem; - color: var(--text-secondary); -} - .section { padding-block: clamp(2.8rem, 7vw, 5.4rem); } @@ -418,6 +365,34 @@ body { font-size: 0.82rem; } +.resource-links { + display: flex; + flex-wrap: wrap; + gap: 0.55rem; +} + +.resource-link { + display: inline-flex; + align-items: center; + min-height: 2rem; + padding: 0.3rem 0.62rem; + border: 1px solid var(--border); + border-radius: 999px; + background: rgba(255, 255, 255, 0.72); + color: var(--text-secondary); + text-decoration: none; + font-family: "IBM Plex Mono", monospace; + font-size: 0.67rem; + letter-spacing: 0.03em; + transition: border-color 0.2s ease, color 0.2s ease, background-color 0.2s ease; +} + +.resource-link:hover { + color: var(--text); + border-color: rgba(122, 158, 142, 0.32); + background: rgba(122, 158, 142, 0.08); +} + .card { border: 1px solid var(--border); border-radius: var(--radius-lg); @@ -817,127 +792,6 @@ button:focus-visible { text-transform: uppercase; } -.steps { - display: grid; - grid-template-columns: repeat(3, minmax(0, 1fr)); - gap: 1rem; -} - -.guide-grid { - margin-top: 0.35rem; -} - -.steps article { - border-radius: 18px; - border: 1px solid var(--border); - background: rgba(255, 255, 255, 0.72); - padding: 1rem; -} - -.steps span { - font-family: "IBM Plex Mono", monospace; - font-size: 0.67rem; - color: var(--text-tertiary); - letter-spacing: 0.1em; -} - -.steps h3 { - margin-top: 0.38rem; - margin-bottom: 0.4rem; - font-family: "Newsreader", serif; - font-size: 1.3rem; - font-weight: 400; -} - -.steps p { - color: var(--text-secondary); - font-size: 0.93rem; - line-height: 1.68; -} - -.guide-note { - margin-top: 0.88rem; - border: 1px solid var(--border); - border-radius: 14px; - background: rgba(255, 255, 255, 0.72); - padding: 0.72rem 0.82rem; - color: var(--text-secondary); - font-size: 0.9rem; -} - -.guide-note strong { - color: var(--text); - font-family: "IBM Plex Mono", monospace; - font-size: 0.82em; - letter-spacing: 0.03em; -} - -.faq-list { - border-top: 1px solid var(--border); -} - -.faq-item { - border-bottom: 1px solid var(--border); - padding-block: 0.86rem; -} - -.faq-item button { - width: 100%; - border: 0; - background: transparent; - text-align: left; - cursor: pointer; - display: flex; - justify-content: space-between; - align-items: center; - gap: 1rem; - color: var(--text); - font-family: "Newsreader", serif; - font-size: 1.24rem; - font-weight: 400; -} - -.faq-item button::after { - content: "+"; - color: var(--text-tertiary); - font-family: "IBM Plex Mono", monospace; - font-size: 0.95rem; -} - -.faq-item button[aria-expanded="true"]::after { - content: "โˆ’"; -} - -.faq-item p { - max-height: 0; - overflow: hidden; - margin: 0; - color: var(--text-secondary); - font-size: 0.95rem; - line-height: 1.67; - transition: max-height 0.24s ease, margin-top 0.24s ease; -} - -.faq-item.open p { - max-height: 12rem; - margin-top: 0.4rem; -} - -.footer { - padding: 1.2rem 0 2.4rem; -} - -.footer-inner { - border-top: 1px solid var(--border); - padding-top: 0.95rem; - display: flex; - justify-content: space-between; - align-items: center; - gap: 1rem; - color: var(--text-tertiary); - font-size: 0.84rem; -} - .mobile-stick { position: fixed; left: 1rem; @@ -981,23 +835,19 @@ button:focus-visible { } @media (max-width: 1120px) { - .hero { + .studio-head, + .workspace-grid { grid-template-columns: 1fr; - min-height: auto; } - .hero > .output-card { - margin-top: 0; + .studio-head { + display: grid; + align-items: start; } - .steps, .metric-grid { grid-template-columns: 1fr; } - - .hero-stats { - grid-template-columns: repeat(2, minmax(0, 1fr)); - } } @media (max-width: 780px) { @@ -1009,12 +859,12 @@ button:focus-visible { width: min(1280px, calc(100% - 1.5rem)); } - .hero { - padding-top: 1.5rem; + .studio { + padding-top: 1rem; } - .hero-stats { - grid-template-columns: 1fr; + .studio-head h1 { + font-size: 1.5rem; } .service-strip { @@ -1030,8 +880,4 @@ button:focus-visible { .mobile-stick { display: block; } - - .footer { - padding-bottom: 5rem; - } } diff --git a/catpred/web/static/index.html b/catpred/web/static/index.html index 9e2f04e..396d41f 100644 --- a/catpred/web/static/index.html +++ b/catpred/web/static/index.html @@ -15,7 +15,7 @@ rel="stylesheet" /> - + @@ -33,53 +33,49 @@ - - Start prediction
-
-
-

AI for Enzyme Kinetics

-

- Predict kcat, Km, and Ki - for production enzyme workflows. -

-

- Enter substrate SMILES and enzyme sequence, then run a single - prediction or a small batch with uncertainty-aware output. -

- -
- Go to form +
+
+
+

Prediction Studio

+

Prediction Studio

+
Service status Checking...
-
    -
  • - 3 targets - kcat / Km / Ki -
    • -
  • - Batch ready - Add and run multiple rows -
    • -
  • - Uncertainty - Total and epistemic SD reported -
    • -
+
+ + Nat. Commun. paper + + + GitHub repository + +
+
-
+

Prediction Studio

@@ -104,25 +100,25 @@

Prediction Studio

For CSV/API usage, this field maps to pdbpath.

-
-
-

Results

-
Ready when you are.
+
+

Results

+
Ready when you are.
-
-
-

Run a prediction to see kinetic estimates here.

-
-
- -
- View detailed output table -
-
+
+
+

Run a prediction to see kinetic estimates here.

+
-
-
+ +
+ View detailed output table +
+
+
+
+
+
From 52476d1f062a54658adeaa592f96a19c4521fdd8 Mon Sep 17 00:00:00 2001 From: theproteinbot Date: Mon, 9 Mar 2026 23:16:02 -0400 Subject: [PATCH 20/34] style(web): highlight publication and repository links --- catpred/web/static/catpred.css | 91 +++++++++++++++++++++++++++------- catpred/web/static/index.html | 24 ++++----- 2 files changed, 85 insertions(+), 30 deletions(-) diff --git a/catpred/web/static/catpred.css b/catpred/web/static/catpred.css index ac188aa..9b12b0c 100644 --- a/catpred/web/static/catpred.css +++ b/catpred/web/static/catpred.css @@ -128,20 +128,33 @@ body { .desktop-nav { display: flex; align-items: center; - gap: 1.65rem; + gap: 0.6rem; } .desktop-nav a { + display: inline-flex; + align-items: center; + min-height: 2.1rem; + padding: 0.35rem 0.7rem; + border: 1px solid var(--border); + border-radius: 999px; + background: rgba(255, 255, 255, 0.72); text-decoration: none; color: var(--text-secondary); - font-size: 0.9rem; - font-weight: 300; - letter-spacing: 0.01em; - transition: color 0.25s ease; + font-family: "IBM Plex Mono", monospace; + font-size: 0.68rem; + font-weight: 400; + letter-spacing: 0.04em; + text-transform: uppercase; + transition: color 0.2s ease, border-color 0.2s ease, background-color 0.2s ease, + transform 0.2s ease; } .desktop-nav a:hover { color: var(--text); + border-color: rgba(122, 158, 142, 0.34); + background: rgba(122, 158, 142, 0.08); + transform: translateY(-1px); } .studio { @@ -164,9 +177,9 @@ body { letter-spacing: -0.03em; } -.studio-meta { +.studio-tools { display: flex; - align-items: center; + align-items: flex-end; justify-content: flex-end; gap: 0.75rem; flex-wrap: wrap; @@ -375,22 +388,44 @@ body { display: inline-flex; align-items: center; min-height: 2rem; - padding: 0.3rem 0.62rem; + padding: 0.34rem 0.68rem; border: 1px solid var(--border); border-radius: 999px; - background: rgba(255, 255, 255, 0.72); - color: var(--text-secondary); + background: rgba(255, 255, 255, 0.88); + color: var(--text); text-decoration: none; font-family: "IBM Plex Mono", monospace; font-size: 0.67rem; - letter-spacing: 0.03em; - transition: border-color 0.2s ease, color 0.2s ease, background-color 0.2s ease; + letter-spacing: 0.04em; + box-shadow: 0 8px 20px rgba(17, 15, 12, 0.05); + transition: border-color 0.2s ease, color 0.2s ease, background-color 0.2s ease, + transform 0.2s ease, box-shadow 0.2s ease; } .resource-link:hover { color: var(--text); - border-color: rgba(122, 158, 142, 0.32); - background: rgba(122, 158, 142, 0.08); + transform: translateY(-1px); + box-shadow: 0 12px 24px rgba(17, 15, 12, 0.08); +} + +.resource-link-paper { + border-color: rgba(196, 137, 122, 0.38); + background: linear-gradient(180deg, rgba(255, 255, 255, 0.94), rgba(196, 137, 122, 0.08)); +} + +.resource-link-paper:hover { + border-color: rgba(196, 137, 122, 0.64); + background: linear-gradient(180deg, rgba(255, 255, 255, 0.98), rgba(196, 137, 122, 0.14)); +} + +.resource-link-code { + border-color: rgba(122, 158, 142, 0.4); + background: linear-gradient(180deg, rgba(255, 255, 255, 0.94), rgba(122, 158, 142, 0.08)); +} + +.resource-link-code:hover { + border-color: rgba(122, 158, 142, 0.66); + background: linear-gradient(180deg, rgba(255, 255, 255, 0.98), rgba(122, 158, 142, 0.15)); } .card { @@ -845,16 +880,17 @@ button:focus-visible { align-items: start; } + .studio-tools { + justify-content: flex-start; + align-items: flex-start; + } + .metric-grid { grid-template-columns: 1fr; } } @media (max-width: 780px) { - .desktop-nav { - display: none; - } - .container { width: min(1280px, calc(100% - 1.5rem)); } @@ -867,12 +903,31 @@ button:focus-visible { font-size: 1.5rem; } + .desktop-nav { + gap: 0.4rem; + } + + .desktop-nav a { + min-height: 1.95rem; + padding-inline: 0.58rem; + font-size: 0.62rem; + } + .service-strip { display: flex; border-radius: 14px; padding: 0.5rem 0.62rem; } + .resource-links { + gap: 0.42rem; + } + + .resource-link { + width: 100%; + justify-content: center; + } + .input-table { min-width: 560px; } diff --git a/catpred/web/static/index.html b/catpred/web/static/index.html index 396d41f..30e3c9a 100644 --- a/catpred/web/static/index.html +++ b/catpred/web/static/index.html @@ -42,21 +42,21 @@
-
-

Prediction Studio

-

Prediction Studio

+
+

Enzyme Kinetics Prediction

+

kcat, Km, and Ki with uncertainty estimates

-
-
- Service status - Checking... - -
+
+
+ Service status + Checking... + +
Prediction Studio Nat. Commun. paper Prediction Studio
-

Prediction Studio

+

Inputs

From e382f93137a30b626c2c24ce0cf1f22bc45ca825 Mon Sep 17 00:00:00 2001 From: theproteinbot Date: Wed, 11 Mar 2026 00:16:18 -0400 Subject: [PATCH 21/34] fix(ci): avoid secrets in deploy workflow conditionals --- .github/workflows/deploy-modal.yml | 15 +++++++++++---- 1 file changed, 11 insertions(+), 4 deletions(-) diff --git a/.github/workflows/deploy-modal.yml b/.github/workflows/deploy-modal.yml index 88735bb..af3ba68 100644 --- a/.github/workflows/deploy-modal.yml +++ b/.github/workflows/deploy-modal.yml @@ -14,29 +14,36 @@ on: jobs: deploy: - if: ${{ secrets.MODAL_TOKEN_ID != '' && secrets.MODAL_TOKEN_SECRET != '' }} runs-on: ubuntu-latest permissions: contents: read concurrency: group: modal-production-deploy cancel-in-progress: true + env: + MODAL_TOKEN_ID: ${{ secrets.MODAL_TOKEN_ID }} + MODAL_TOKEN_SECRET: ${{ secrets.MODAL_TOKEN_SECRET }} steps: + - name: Skip when Modal credentials are not configured + if: ${{ env.MODAL_TOKEN_ID == '' || env.MODAL_TOKEN_SECRET == '' }} + run: echo "Skipping Modal deploy because MODAL_TOKEN_ID / MODAL_TOKEN_SECRET are not configured." + - name: Checkout + if: ${{ env.MODAL_TOKEN_ID != '' && env.MODAL_TOKEN_SECRET != '' }} uses: actions/checkout@v4 - name: Set up Python + if: ${{ env.MODAL_TOKEN_ID != '' && env.MODAL_TOKEN_SECRET != '' }} uses: actions/setup-python@v5 with: python-version: "3.10" - name: Install Modal CLI + if: ${{ env.MODAL_TOKEN_ID != '' && env.MODAL_TOKEN_SECRET != '' }} run: | python -m pip install --upgrade pip pip install "modal>=0.73" - name: Deploy modal_app.py - env: - MODAL_TOKEN_ID: ${{ secrets.MODAL_TOKEN_ID }} - MODAL_TOKEN_SECRET: ${{ secrets.MODAL_TOKEN_SECRET }} + if: ${{ env.MODAL_TOKEN_ID != '' && env.MODAL_TOKEN_SECRET != '' }} run: modal deploy modal_app.py From 3a35d0b3166f87fd1bf07a1a2c443a22edf91f36 Mon Sep 17 00:00:00 2001 From: theproteinbot Date: Sat, 14 Mar 2026 21:43:58 -0400 Subject: [PATCH 22/34] feat(web): redesign input form with multi-substrate support and Ki separation MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Replace the 3-pill parameter selector with two top-level modes: - Substrate kinetics (kcat/Km): per-entry multi-substrate inputs with primary marker โ€” primary goes to Km, all joined for kcat - Inhibition (Ki): single inhibitor SMILES input Key changes: - New SubstrateInputs component with radio primary selector - ParameterSelector now shows mode pills + kcat/Km checkboxes - usePrediction supports running multiple predictions in parallel - ResultPanel displays grouped results per parameter - Units render with Unicode superscripts (sโปยน instead of s^(-1)) - Header updated with Maranas Group link, field order: sequence first - Sample data for both modes (glucokinase/glucose+ATP, coumaric acid/P11544) Co-Authored-By: Claude Opus 4.6 (1M context) --- catpred/web/frontend/index.html | 23 + catpred/web/frontend/package-lock.json | 1504 +++++++++++++++++ catpred/web/frontend/package.json | 20 + catpred/web/frontend/src/App.vue | 277 +++ catpred/web/frontend/src/api/client.ts | 63 + catpred/web/frontend/src/api/types.ts | 74 + .../web/frontend/src/components/AppFooter.vue | 41 + .../web/frontend/src/components/AppHeader.vue | 92 + .../frontend/src/components/BatchUpload.vue | 128 ++ .../frontend/src/components/InputPanel.vue | 186 ++ .../web/frontend/src/components/InputRow.vue | 217 +++ .../src/components/ParameterSelector.vue | 163 ++ .../frontend/src/components/ResultPanel.vue | 425 +++++ .../web/frontend/src/components/SkipLink.vue | 22 + .../web/frontend/src/components/StatusBar.vue | 177 ++ .../src/components/SubstrateInputs.vue | 193 +++ .../frontend/src/composables/useInputRows.ts | 357 ++++ .../frontend/src/composables/usePrediction.ts | 176 ++ .../frontend/src/composables/useReadiness.ts | 103 ++ catpred/web/frontend/src/env.d.ts | 7 + catpred/web/frontend/src/main.ts | 8 + catpred/web/frontend/src/styles/base.css | 41 + catpred/web/frontend/src/styles/reset.css | 61 + catpred/web/frontend/src/styles/tokens.css | 43 + catpred/web/frontend/src/styles/utilities.css | 11 + catpred/web/frontend/tsconfig.app.json | 20 + catpred/web/frontend/tsconfig.app.tsbuildinfo | 1 + catpred/web/frontend/tsconfig.json | 6 + catpred/web/frontend/vite.config.ts | 18 + .../web/static/dist/assets/index-BmkWAspG.css | 1 + .../web/static/dist/assets/index-DWuYjQ6d.js | 20 + catpred/web/static/dist/index.html | 24 + 32 files changed, 4502 insertions(+) create mode 100644 catpred/web/frontend/index.html create mode 100644 catpred/web/frontend/package-lock.json create mode 100644 catpred/web/frontend/package.json create mode 100644 catpred/web/frontend/src/App.vue create mode 100644 catpred/web/frontend/src/api/client.ts create mode 100644 catpred/web/frontend/src/api/types.ts create mode 100644 catpred/web/frontend/src/components/AppFooter.vue create mode 100644 catpred/web/frontend/src/components/AppHeader.vue create mode 100644 catpred/web/frontend/src/components/BatchUpload.vue create mode 100644 catpred/web/frontend/src/components/InputPanel.vue create mode 100644 catpred/web/frontend/src/components/InputRow.vue create mode 100644 catpred/web/frontend/src/components/ParameterSelector.vue create mode 100644 catpred/web/frontend/src/components/ResultPanel.vue create mode 100644 catpred/web/frontend/src/components/SkipLink.vue create mode 100644 catpred/web/frontend/src/components/StatusBar.vue create mode 100644 catpred/web/frontend/src/components/SubstrateInputs.vue create mode 100644 catpred/web/frontend/src/composables/useInputRows.ts create mode 100644 catpred/web/frontend/src/composables/usePrediction.ts create mode 100644 catpred/web/frontend/src/composables/useReadiness.ts create mode 100644 catpred/web/frontend/src/env.d.ts create mode 100644 catpred/web/frontend/src/main.ts create mode 100644 catpred/web/frontend/src/styles/base.css create mode 100644 catpred/web/frontend/src/styles/reset.css create mode 100644 catpred/web/frontend/src/styles/tokens.css create mode 100644 catpred/web/frontend/src/styles/utilities.css create mode 100644 catpred/web/frontend/tsconfig.app.json create mode 100644 catpred/web/frontend/tsconfig.app.tsbuildinfo create mode 100644 catpred/web/frontend/tsconfig.json create mode 100644 catpred/web/frontend/vite.config.ts create mode 100644 catpred/web/static/dist/assets/index-BmkWAspG.css create mode 100644 catpred/web/static/dist/assets/index-DWuYjQ6d.js create mode 100644 catpred/web/static/dist/index.html diff --git a/catpred/web/frontend/index.html b/catpred/web/frontend/index.html new file mode 100644 index 0000000..42c4030 --- /dev/null +++ b/catpred/web/frontend/index.html @@ -0,0 +1,23 @@ + + + + + + CatPred | Enzyme Kinetics Prediction + + + + + + + +
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"scripts": { + "dev": "vite", + "build": "vue-tsc -b && vite build", + "preview": "vite preview" + }, + "dependencies": { + "vue": "^3.5.13" + }, + "devDependencies": { + "@vitejs/plugin-vue": "^5.2.3", + "typescript": "~5.7.3", + "vite": "^6.3.5", + "vue-tsc": "^2.2.8" + } +} diff --git a/catpred/web/frontend/src/App.vue b/catpred/web/frontend/src/App.vue new file mode 100644 index 0000000..f193aab --- /dev/null +++ b/catpred/web/frontend/src/App.vue @@ -0,0 +1,277 @@ + + + + + diff --git a/catpred/web/frontend/src/api/client.ts b/catpred/web/frontend/src/api/client.ts new file mode 100644 index 0000000..e41fc9c --- /dev/null +++ b/catpred/web/frontend/src/api/client.ts @@ -0,0 +1,63 @@ +import type { ReadyResponse, PredictPayload, PredictResponse } from './types' + +const PREDICT_TIMEOUT_MS = 120_000 + +export async function fetchReady(signal?: AbortSignal): Promise { + const res = await fetch('/ready', { + method: 'GET', + headers: { Accept: 'application/json' }, + signal, + }) + if (!res.ok) { + throw new Error(`Service check failed (${res.status})`) + } + return res.json() +} + +export async function fetchPredict( + payload: PredictPayload, + signal?: AbortSignal, +): Promise { + const controller = new AbortController() + const timeout = setTimeout(() => controller.abort(), PREDICT_TIMEOUT_MS) + + // Combine external signal with timeout + if (signal) { + signal.addEventListener('abort', () => controller.abort()) + } + + try { + const res = await fetch('/predict', { + method: 'POST', + headers: { + 'Content-Type': 'application/json', + Accept: 'application/json', + }, + body: JSON.stringify(payload), + signal: controller.signal, + }) + + if (!res.ok) { + let detail = 'Prediction could not be completed.' + try { + const data = await res.json() + if (data?.detail) detail = String(data.detail) + } catch { + // ignore parse error + } + throw new Error(detail) + } + + return res.json() + } catch (err) { + if (err instanceof DOMException && err.name === 'AbortError') { + throw new Error( + `Prediction timed out after ${Math.floor(PREDICT_TIMEOUT_MS / 1000)}s. ` + + 'This may be a cold-start delay โ€” try again.', + ) + } + throw err + } finally { + clearTimeout(timeout) + } +} diff --git a/catpred/web/frontend/src/api/types.ts b/catpred/web/frontend/src/api/types.ts new file mode 100644 index 0000000..64046b3 --- /dev/null +++ b/catpred/web/frontend/src/api/types.ts @@ -0,0 +1,74 @@ +export interface ReadyResponse { + ready: boolean + default_backend: string + fallback_to_local_enabled: boolean + backends: { + local?: { ready: boolean } + modal?: { ready: boolean } + } + api?: { + allow_input_file: boolean + allow_unsafe_request_overrides: boolean + max_input_rows: number + max_input_file_bytes: number + available_checkpoints: Record + missing_checkpoints: string[] + } +} + +export interface InputRow { + SMILES: string + sequence: string + pdbpath: string +} + +export interface PredictPayload { + parameter: string + checkpoint_dir: string + input_rows: InputRow[] + use_gpu: boolean + results_dir: string + backend: string + fallback_to_local: boolean +} + +export interface PredictResponse { + backend: string + output_file: string + row_count: number + preview_rows: Record[] + metadata: Record +} + +export type Parameter = 'kcat' | 'km' | 'ki' + +export const SUPPORTED_PARAMETERS: Parameter[] = ['kcat', 'km', 'ki'] + +export const PARAMETER_LABELS: Record = { + kcat: 'kcat', + km: 'Km', + ki: 'Ki', +} + +export interface ParsedPrediction { + linear: number | null + linearKey: string + unit: string + log10: number | null + sdTotal: number | null + sdAleatoric: number | null + sdEpistemic: number | null +} + +export type PredictionMode = 'substrate' | 'inhibition' + +export interface SubstrateEntry { + id: number + smiles: string + isPrimary: boolean +} + +export interface PredictionResultEntry { + parameter: Parameter + response: PredictResponse +} diff --git a/catpred/web/frontend/src/components/AppFooter.vue b/catpred/web/frontend/src/components/AppFooter.vue new file mode 100644 index 0000000..b74a8ff --- /dev/null +++ b/catpred/web/frontend/src/components/AppFooter.vue @@ -0,0 +1,41 @@ + + + diff --git a/catpred/web/frontend/src/components/AppHeader.vue b/catpred/web/frontend/src/components/AppHeader.vue new file mode 100644 index 0000000..e1a42f8 --- /dev/null +++ b/catpred/web/frontend/src/components/AppHeader.vue @@ -0,0 +1,92 @@ + + + diff --git a/catpred/web/frontend/src/components/BatchUpload.vue b/catpred/web/frontend/src/components/BatchUpload.vue new file mode 100644 index 0000000..350ef5b --- /dev/null +++ b/catpred/web/frontend/src/components/BatchUpload.vue @@ -0,0 +1,128 @@ + + + + + diff --git a/catpred/web/frontend/src/components/InputPanel.vue b/catpred/web/frontend/src/components/InputPanel.vue new file mode 100644 index 0000000..2bff371 --- /dev/null +++ b/catpred/web/frontend/src/components/InputPanel.vue @@ -0,0 +1,186 @@ + + + + + diff --git a/catpred/web/frontend/src/components/InputRow.vue b/catpred/web/frontend/src/components/InputRow.vue new file mode 100644 index 0000000..bb8dbb7 --- /dev/null +++ b/catpred/web/frontend/src/components/InputRow.vue @@ -0,0 +1,217 @@ + + + + + diff --git a/catpred/web/frontend/src/components/ParameterSelector.vue b/catpred/web/frontend/src/components/ParameterSelector.vue new file mode 100644 index 0000000..f4b9143 --- /dev/null +++ b/catpred/web/frontend/src/components/ParameterSelector.vue @@ -0,0 +1,163 @@ + + + + + diff --git a/catpred/web/frontend/src/components/ResultPanel.vue b/catpred/web/frontend/src/components/ResultPanel.vue new file mode 100644 index 0000000..7d41a6f --- /dev/null +++ b/catpred/web/frontend/src/components/ResultPanel.vue @@ -0,0 +1,425 @@ + + + + + diff --git a/catpred/web/frontend/src/components/SkipLink.vue b/catpred/web/frontend/src/components/SkipLink.vue new file mode 100644 index 0000000..d3c8075 --- /dev/null +++ b/catpred/web/frontend/src/components/SkipLink.vue @@ -0,0 +1,22 @@ + + + diff --git a/catpred/web/frontend/src/components/StatusBar.vue b/catpred/web/frontend/src/components/StatusBar.vue new file mode 100644 index 0000000..1b0a15e --- /dev/null +++ b/catpred/web/frontend/src/components/StatusBar.vue @@ -0,0 +1,177 @@ + + + + + diff --git a/catpred/web/frontend/src/components/SubstrateInputs.vue b/catpred/web/frontend/src/components/SubstrateInputs.vue new file mode 100644 index 0000000..68dd215 --- /dev/null +++ b/catpred/web/frontend/src/components/SubstrateInputs.vue @@ -0,0 +1,193 @@ + + + + + diff --git a/catpred/web/frontend/src/composables/useInputRows.ts b/catpred/web/frontend/src/composables/useInputRows.ts new file mode 100644 index 0000000..216671c --- /dev/null +++ b/catpred/web/frontend/src/composables/useInputRows.ts @@ -0,0 +1,357 @@ +import { ref, computed } from 'vue' +import type { InputRow, Parameter, PredictionMode, SubstrateEntry } from '../api/types' + +export interface InputRowEntry { + id: number + substrates: SubstrateEntry[] + inhibitorSmiles: string + sequence: string + pdbpath: string +} + +// Human glucokinase / hexokinase-4 (UniProt P35557) + D-glucose +// Related to paper example (Boorla & Maranas, Nat. Commun. 2025, Fig 7a) +const GCK_SEQUENCE = + 'MLDDRARMEAAKKEKVEQILAEFQLQEEDLKKVMRRMQKEMDRGLRLETHEEASVKMLPTYVRSTPEGS' + + 'EVGDFLSLDLGGTNFRVMLVKVGEGEEGQWSVKTKHQMYSIPEDAMTGTAEMLFDYISECISDFLDKHQM' + + 'KHKKLPLGFTFSFPVRHEDIDKGILLNWTKGFKASGAEGNNVVGLLRDAIKRRGDFEMDVVAMVNDTVAT' + + 'MISCYYEDHQCEVGMIVGTGCNACYMEEMQNVELVEGDEGRMCVNTEWGAFGDSGELDEFLLEYDRLVDE' + + 'SSANPGQQLYEKLIGGKYMGELVRLVLLRLVDENLLFHGEASEQLRTRGAFETRFVSQVESDTGDRKQIYN' + + 'ILSTLGLRPSTTDCDIVRRACESVSTRAAHMCSAGLAGVINRMRESRSEDVMRITVGVDGSVYKLHPSFKE' + + 'RFHASVRRLTPSCEITFIESEEGSGRGAALVSAVACKKACMLGQ' + +// Classic enzyme: Human L-lactate dehydrogenase A (UniProt P00338) + pyruvate +const LDHA_SEQUENCE = + 'MATLKDQLIYNLLKEEQTPQNKITVVGVGAVGMACAISILMKDLADELALVDVIEDKLKGEMMDLQHGS' + + 'LFLRTPKIVSGKDYNVTANSKLVIITAGARQQEGESRLNLVQRNVNIFKFIIPNVVKYSPNCKLLIV' + + 'SNPVDILTYVAWKISGFPKNRVIGSGCNLDSARFRYLMGERLGVHPLSCHGWVLGEHGDSSVPVWSGMNV' + + 'AGVSLKTLHPDLGTDKDKEQWKEVHKQVVESAYEVIKLKGYTSWAIGLSVADLAESIMKNLRRVHPVSTM' + + 'IKGLYGIKDDVFLSVPCILGQNGISDLVKVTLTSEEEARLKKSADTLWGIQKELQF' + +// Phenylalanine ammonia-lyase (UniProt P11544) โ€” Ki sample enzyme +const P11544_SEQUENCE = + 'MAPSLDSISHSFANGVASAKQAVNGASTNLAVAGSHLPTTQVTQVDIVEKMLAAPTDSTLELDGYSLNLG' + + 'DVVSAARKGRPVRVKDSDEIRSKIDKSVEFLRSQLSMSVYGVTTGFGGSADTRTEDAISLQKALLEHQLCG' + + 'VLPSSFDSFRLGRGLENSLPLEVVRGAMTIRVNSLTRGHSAVRLVVLEALTNFLNHGITPIVPLRGTISAS' + + 'GDLSPLSYIAAAISGHPDSKVHVVHEGKEKILYAREAMALFNLEPVVLGPKEGLGLVNGTAVSASMATLA' + + 'LHDAHMLSLLSQSLTAMTVEAMVGHAGSFHPFLHDVTRPHPTQIEVAGNIRKLLEGSRFAVHHEEEVKVKD' + + 'DEGILRQDRYPLRTSPQWLGPLVSDLIHAHAVLTIEAGQSTTDNPLIDVENKTSHHGGNFQAAAVANTMEK' + + 'TRLGLAQIGKLNFTQLTEMLNAGMNRGLPSCLAAEDPSLSYHCKGLDIAAAAYTSELGHLANPVTTHVQPA' + + 'EMANQAVNSLALISARRTTESNDVLSLLLATHLYCVLQAIDLRAIEFEFKKQFGPAIVSLIDQHFGSAMTG' + + 'SNLRDELVEKVNKTLAKRLEQTNSYDLVPRWHDAFSFAAGTVVEVLSSTSLSLAAVNAWKVAAAESAISLT' + + 'RQVRETFWSAASTSSPALSYLSPRTQILYAFVREELGVKARRGDVFLGKQEVTIGSNVSKIYEAIKSGRINN' + + 'VLLKMLA' + +const GLUCOSE_SMILES = 'C(C1C(C(C(C(O1)O)O)O)O)O' +const ATP_SMILES = 'C1=NC(=C2C(=N1)N(C=N2)C3C(C(C(O3)COP(=O)(O)OP(=O)(O)OP(=O)(O)O)O)O)N' +const PYRUVATE_SMILES = 'CC(=O)C(=O)O' +const NADH_SMILES = 'C1C=CN(C=C1C(=O)N)C2C(C(C(O2)COP(=O)(O)OP(=O)(O)OCC3C(C(C(O3)N4C=NC5=C(N=CN=C54)N)O)O)O)O' +const COUMARIC_ACID_SMILES = 'C1=CC(=CC=C1/C=C/C(=O)O)O' + +const SAMPLE_SUBSTRATE: Omit[] = [ + { + substrates: [ + { id: 1, smiles: GLUCOSE_SMILES, isPrimary: true }, + { id: 2, smiles: ATP_SMILES, isPrimary: false }, + ], + inhibitorSmiles: '', + sequence: GCK_SEQUENCE, + pdbpath: 'GCK_HUMAN', + }, + { + substrates: [ + { id: 1, smiles: PYRUVATE_SMILES, isPrimary: true }, + { id: 2, smiles: NADH_SMILES, isPrimary: false }, + ], + inhibitorSmiles: '', + sequence: LDHA_SEQUENCE, + pdbpath: 'LDHA_HUMAN', + }, +] + +const SAMPLE_INHIBITION: Omit[] = [ + { + substrates: [], + inhibitorSmiles: COUMARIC_ACID_SMILES, + sequence: P11544_SEQUENCE, + pdbpath: 'P11544', + }, +] + +// Validation helpers +const SMILES_VALID_CHARS = /^[A-Za-z0-9@+\-\[\]\\\/().=#%$:~&!*]+$/ +const AMINO_ACIDS = new Set('ACDEFGHIKLMNPQRSTVWY'.split('')) + +export function validateSmiles(s: string): string { + if (!s.trim()) return 'SMILES is required.' + if (!SMILES_VALID_CHARS.test(s.trim())) return 'Invalid SMILES characters.' + return '' +} + +export function validateSequence(s: string): string { + if (!s.trim()) return 'Sequence is required.' + const upper = s.trim().toUpperCase() + for (const ch of upper) { + if (!AMINO_ACIDS.has(ch)) return `Invalid amino acid: "${ch}".` + } + return '' +} + +export function validatePdbpath(s: string): string { + if (!s.trim()) return 'Sequence ID is required.' + return '' +} + +export function useInputRows() { + let nextRowId = 1 + let nextSubId = 100 + const rows = ref([]) + + function formatSeqId(n: number): string { + return `seq_${String(n).padStart(3, '0')}` + } + + function getNextSeqId(): string { + let maxSeen = 0 + for (const row of rows.value) { + const match = row.pdbpath.match(/^seq_(\d+)$/i) + if (match) { + maxSeen = Math.max(maxSeen, Number(match[1])) + } + } + if (maxSeen > 0) return formatSeqId(maxSeen + 1) + return formatSeqId(rows.value.length + 1) + } + + function makeSubstrate(smiles = '', isPrimary = false): SubstrateEntry { + return { id: nextSubId++, smiles, isPrimary } + } + + function addRow(values?: Partial>) { + const substrates = values?.substrates?.length + ? values.substrates.map((s) => makeSubstrate(s.smiles, s.isPrimary)) + : [makeSubstrate('', true)] + + rows.value.push({ + id: nextRowId++, + substrates, + inhibitorSmiles: values?.inhibitorSmiles ?? '', + sequence: values?.sequence ?? '', + pdbpath: values?.pdbpath || getNextSeqId(), + }) + } + + function removeRow(id: number) { + if (rows.value.length <= 1) return + rows.value = rows.value.filter((r) => r.id !== id) + } + + function updateField( + id: number, + field: 'sequence' | 'pdbpath' | 'inhibitorSmiles', + value: string, + ) { + const row = rows.value.find((r) => r.id === id) + if (row) { + row[field] = value + } + } + + function addSubstrate(rowId: number) { + const row = rows.value.find((r) => r.id === rowId) + if (row) { + row.substrates.push(makeSubstrate('', false)) + } + } + + function removeSubstrate(rowId: number, subId: number) { + const row = rows.value.find((r) => r.id === rowId) + if (!row || row.substrates.length <= 1) return + const wasPrimary = row.substrates.find((s) => s.id === subId)?.isPrimary + row.substrates = row.substrates.filter((s) => s.id !== subId) + if (wasPrimary && row.substrates.length > 0) { + row.substrates[0].isPrimary = true + } + } + + function updateSubstrateSmiles(rowId: number, subId: number, smiles: string) { + const row = rows.value.find((r) => r.id === rowId) + if (!row) return + const sub = row.substrates.find((s) => s.id === subId) + if (sub) sub.smiles = smiles + } + + function setPrimary(rowId: number, subId: number) { + const row = rows.value.find((r) => r.id === rowId) + if (!row) return + for (const sub of row.substrates) { + sub.isPrimary = sub.id === subId + } + } + + function loadSample(mode: PredictionMode) { + rows.value = [] + nextRowId = 1 + nextSubId = 100 + const samples = mode === 'substrate' ? SAMPLE_SUBSTRATE : SAMPLE_INHIBITION + for (const s of samples) { + addRow(s) + } + } + + function clear() { + rows.value = [] + nextRowId = 1 + nextSubId = 100 + addRow() + } + + function importCsv(text: string, mode: PredictionMode): string { + const lines = text.trim().split('\n') + if (lines.length < 2) return 'CSV must have a header row and at least one data row.' + + const header = lines[0].split(',').map((h) => h.trim()) + const smilesIdx = header.findIndex((h) => h.toLowerCase() === 'smiles') + const seqIdx = header.findIndex((h) => h.toLowerCase() === 'sequence') + const pdbIdx = header.findIndex((h) => h.toLowerCase() === 'pdbpath') + + if (smilesIdx === -1 || seqIdx === -1) { + return 'CSV must have SMILES and sequence columns.' + } + + const newRows: Partial>[] = [] + for (let i = 1; i < lines.length; i++) { + const cols = lines[i].split(',').map((c) => c.trim()) + if (!cols[smilesIdx] && !cols[seqIdx]) continue + + const smiles = cols[smilesIdx] || '' + const entry: Partial> = { + sequence: cols[seqIdx] || '', + pdbpath: cols[pdbIdx] || formatSeqId(i), + } + + if (mode === 'substrate') { + const parts = smiles.split('.') + entry.substrates = parts.map((s, idx) => ({ + id: idx + 1, + smiles: s, + isPrimary: idx === 0, + })) + } else { + entry.inhibitorSmiles = smiles + } + + newRows.push(entry) + } + + if (newRows.length === 0) return 'No valid data rows found.' + + rows.value = [] + nextRowId = 1 + nextSubId = 100 + for (const r of newRows) { + addRow(r) + } + return '' + } + + function collectRowsForParameter( + mode: PredictionMode, + parameter: Parameter, + ): InputRow[] { + return rows.value + .filter((r) => { + if (mode === 'substrate') { + return ( + r.substrates.some((s) => s.smiles.trim()) && + r.sequence.trim() && + r.pdbpath.trim() + ) + } + return r.inhibitorSmiles.trim() && r.sequence.trim() && r.pdbpath.trim() + }) + .map((r) => { + let smiles: string + if (mode === 'inhibition') { + smiles = r.inhibitorSmiles.trim() + } else if (parameter === 'km') { + const primary = r.substrates.find((s) => s.isPrimary) + smiles = primary ? primary.smiles.trim() : r.substrates[0]?.smiles.trim() || '' + } else { + smiles = r.substrates + .filter((s) => s.smiles.trim()) + .map((s) => s.smiles.trim()) + .join('.') + } + return { + SMILES: smiles, + sequence: r.sequence.trim().toUpperCase(), + pdbpath: r.pdbpath.trim(), + } + }) + } + + function validateAll(mode: PredictionMode): string { + if (rows.value.length === 0) return 'Please add at least one entry.' + + for (const row of rows.value) { + if (mode === 'substrate') { + if (!row.substrates.some((s) => s.smiles.trim())) { + return 'Each entry needs at least one substrate SMILES.' + } + for (const sub of row.substrates) { + if (sub.smiles.trim()) { + const err = validateSmiles(sub.smiles) + if (err) return err + } + } + } else { + const err = validateSmiles(row.inhibitorSmiles) + if (err) return `Inhibitor: ${err}` + } + + const seqErr = validateSequence(row.sequence) + if (seqErr) return seqErr + + const idErr = validatePdbpath(row.pdbpath) + if (idErr) return idErr + } + + const collected = + mode === 'substrate' + ? collectRowsForParameter(mode, 'kcat') + : collectRowsForParameter(mode, 'ki') + + const mapping = new Map() + for (const row of collected) { + const existing = mapping.get(row.pdbpath) + if (existing && existing !== row.sequence) { + return 'Each Sequence ID must map to one unique enzyme sequence.' + } + mapping.set(row.pdbpath, row.sequence) + } + return '' + } + + const rowCount = computed(() => rows.value.length) + + // Init with first substrate sample + addRow(SAMPLE_SUBSTRATE[0]) + + return { + rows, + rowCount, + addRow, + removeRow, + updateField, + addSubstrate, + removeSubstrate, + updateSubstrateSmiles, + setPrimary, + loadSample, + clear, + importCsv, + collectRowsForParameter, + validateAll, + } +} diff --git a/catpred/web/frontend/src/composables/usePrediction.ts b/catpred/web/frontend/src/composables/usePrediction.ts new file mode 100644 index 0000000..be6d6c8 --- /dev/null +++ b/catpred/web/frontend/src/composables/usePrediction.ts @@ -0,0 +1,176 @@ +import { ref, computed } from 'vue' +import { fetchPredict } from '../api/client' +import type { + Parameter, + PredictPayload, + PredictionResultEntry, + ParsedPrediction, +} from '../api/types' + +export type PredictionStatus = 'idle' | 'running' | 'success' | 'error' + +export interface PredictionJob { + parameter: Parameter + payload: PredictPayload +} + +const STORAGE_KEY = 'catpred:lastResults' + +function loadStoredResults(): PredictionResultEntry[] { + try { + const raw = localStorage.getItem(STORAGE_KEY) + if (!raw) return [] + const parsed = JSON.parse(raw) + if (!Array.isArray(parsed)) return [] + return parsed as PredictionResultEntry[] + } catch { + return [] + } +} + +function storeResults(data: PredictionResultEntry[]) { + try { + localStorage.setItem(STORAGE_KEY, JSON.stringify(data)) + } catch { + // storage full or unavailable + } +} + +export function parsePrediction(row: Record): ParsedPrediction { + const keys = Object.keys(row) + const linearKey = keys.find((k) => k.startsWith('Prediction_(')) + const unitMatch = linearKey?.match(/^Prediction_\((.*)\)$/) + const unit = unitMatch ? unitMatch[1] : '' + + return { + linear: linearKey ? (row[linearKey] as number | null) : null, + linearKey: linearKey || 'Prediction', + unit, + log10: (row.Prediction_log10 as number | null) ?? null, + sdTotal: (row.SD_total as number | null) ?? null, + sdAleatoric: (row.SD_aleatoric as number | null) ?? null, + sdEpistemic: (row.SD_epistemic as number | null) ?? null, + } +} + +const SUPERSCRIPT: Record = { + '0': '\u2070', '1': '\u00B9', '2': '\u00B2', '3': '\u00B3', + '4': '\u2074', '5': '\u2075', '6': '\u2076', '7': '\u2077', + '8': '\u2078', '9': '\u2079', '-': '\u207B', '+': '\u207A', +} + +export function formatUnit(unit: string): string { + if (!unit) return '' + // Replace ^(...) and ^N patterns with Unicode superscripts + return unit.replace(/\^(?:\(([^)]+)\)|([0-9+-]+))/g, (_, group, bare) => { + const content = group ?? bare + return [...content].map((ch) => SUPERSCRIPT[ch] ?? ch).join('') + }) +} + +export function formatNumber(value: unknown): string { + const n = Number(value) + if (!Number.isFinite(n)) return '\u2014' + return n.toFixed(1) +} + +export function confidenceRange( + log10: number | null, + sd: number | null, +): [string, string] | null { + if (log10 === null || sd === null || !Number.isFinite(log10) || !Number.isFinite(sd)) { + return null + } + const lo = Math.pow(10, log10 - sd) + const hi = Math.pow(10, log10 + sd) + return [formatNumber(lo), formatNumber(hi)] +} + +export function formatElapsed(seconds: number): string { + const safe = Math.max(0, Math.floor(seconds)) + const mins = Math.floor(safe / 60) + const secs = safe % 60 + return `${String(mins).padStart(2, '0')}:${String(secs).padStart(2, '0')}` +} + +export function usePrediction() { + const status = ref('idle') + const error = ref('') + const results = ref(loadStoredResults()) + const elapsedSeconds = ref(0) + const lastJobs = ref(null) + + let startTime = 0 + let timerInterval: ReturnType | null = null + let abortController: AbortController | null = null + + const isRunning = computed(() => status.value === 'running') + const hasResults = computed(() => results.value.length > 0) + + function startTimer() { + stopTimer() + startTime = Date.now() + elapsedSeconds.value = 0 + timerInterval = setInterval(() => { + elapsedSeconds.value = Math.floor((Date.now() - startTime) / 1000) + }, 1000) + } + + function stopTimer() { + if (timerInterval) { + clearInterval(timerInterval) + timerInterval = null + } + } + + async function runAll(jobs: PredictionJob[]) { + abortController?.abort() + abortController = new AbortController() + lastJobs.value = jobs + status.value = 'running' + error.value = '' + results.value = [] + startTimer() + + try { + const settled = await Promise.all( + jobs.map(async (job) => { + const data = await fetchPredict(job.payload, abortController!.signal) + return { parameter: job.parameter, response: data } as PredictionResultEntry + }), + ) + results.value = settled + storeResults(settled) + status.value = 'success' + } catch (err) { + error.value = err instanceof Error ? err.message : 'Prediction failed.' + status.value = 'error' + } finally { + stopTimer() + abortController = null + } + } + + function retry() { + if (lastJobs.value) { + runAll(lastJobs.value) + } + } + + function cancel() { + abortController?.abort() + } + + return { + status, + error, + results, + elapsedSeconds, + lastJobs, + isRunning, + hasResults, + runAll, + retry, + cancel, + } +} diff --git a/catpred/web/frontend/src/composables/useReadiness.ts b/catpred/web/frontend/src/composables/useReadiness.ts new file mode 100644 index 0000000..30f682d --- /dev/null +++ b/catpred/web/frontend/src/composables/useReadiness.ts @@ -0,0 +1,103 @@ +import { ref, computed } from 'vue' +import { fetchReady } from '../api/client' +import type { Parameter } from '../api/types' +import { SUPPORTED_PARAMETERS } from '../api/types' + +export type ServiceStatus = 'checking' | 'online' | 'limited' | 'offline' + +export function useReadiness() { + const status = ref('checking') + const statusHint = ref('') + const defaultBackend = ref('local') + const modalReady = ref(false) + const localReady = ref(false) + const fallbackToLocalEnabled = ref(false) + const availableCheckpoints = ref>(new Set(SUPPORTED_PARAMETERS)) + const localCheckpoints = ref>(new Set(SUPPORTED_PARAMETERS)) + + function isParameterAvailable(param: Parameter): boolean { + return availableCheckpoints.value.has(param) + } + + const firstAvailable = computed(() => { + for (const p of SUPPORTED_PARAMETERS) { + if (availableCheckpoints.value.has(p)) return p + } + return null + }) + + function chooseBackend(): string { + if (defaultBackend.value === 'modal' && modalReady.value) return 'modal' + if (defaultBackend.value === 'local' && localCheckpoints.value.size > 0) return 'local' + if (modalReady.value) return 'modal' + if (localReady.value) return 'local' + return defaultBackend.value || 'local' + } + + function shouldFallback(requestBackend: string, param: Parameter): boolean { + if (requestBackend !== 'modal') return false + if (!fallbackToLocalEnabled.value) return false + if (!localReady.value) return false + return localCheckpoints.value.has(param) + } + + function parseCheckpoints(available: Record | undefined): Set { + if (!available) return new Set(SUPPORTED_PARAMETERS) + return new Set( + Object.keys(available) + .map((k) => k.toLowerCase() as Parameter) + .filter((k) => SUPPORTED_PARAMETERS.includes(k)), + ) + } + + async function check() { + status.value = 'checking' + statusHint.value = '' + + try { + const data = await fetchReady() + + defaultBackend.value = data.default_backend || 'local' + modalReady.value = Boolean(data.backends?.modal?.ready) + localReady.value = Boolean(data.backends?.local?.ready) + fallbackToLocalEnabled.value = Boolean(data.fallback_to_local_enabled) + + localCheckpoints.value = parseCheckpoints(data.api?.available_checkpoints) + + if (localCheckpoints.value.size > 0) { + availableCheckpoints.value = new Set(localCheckpoints.value) + } else if (modalReady.value) { + availableCheckpoints.value = new Set(SUPPORTED_PARAMETERS) + } else { + availableCheckpoints.value = new Set() + } + + if (data.ready) { + status.value = 'online' + const params = Array.from(localCheckpoints.value).join(', ') + statusHint.value = params + ? `${data.default_backend} ยท ${params}` + : `${data.default_backend} ยท no local checkpoints` + } else { + status.value = 'limited' + statusHint.value = modalReady.value + ? 'Backend available in limited mode' + : 'No local checkpoints found' + } + } catch { + status.value = 'offline' + statusHint.value = 'Could not contact service' + } + } + + return { + status, + statusHint, + availableCheckpoints, + firstAvailable, + isParameterAvailable, + chooseBackend, + shouldFallback, + check, + } +} diff --git a/catpred/web/frontend/src/env.d.ts b/catpred/web/frontend/src/env.d.ts new file mode 100644 index 0000000..3885263 --- /dev/null +++ b/catpred/web/frontend/src/env.d.ts @@ -0,0 +1,7 @@ +/// + +declare module '*.vue' { + import type { DefineComponent } from 'vue' + const component: DefineComponent + export default component +} diff --git a/catpred/web/frontend/src/main.ts b/catpred/web/frontend/src/main.ts new file mode 100644 index 0000000..43370bb --- /dev/null +++ b/catpred/web/frontend/src/main.ts @@ -0,0 +1,8 @@ +import { createApp } from 'vue' +import App from './App.vue' +import './styles/tokens.css' +import './styles/reset.css' +import './styles/base.css' +import './styles/utilities.css' + +createApp(App).mount('#app') diff --git a/catpred/web/frontend/src/styles/base.css b/catpred/web/frontend/src/styles/base.css new file mode 100644 index 0000000..53766b1 --- /dev/null +++ b/catpred/web/frontend/src/styles/base.css @@ -0,0 +1,41 @@ +body { + background: var(--bg); + color: var(--text); + font-family: var(--font-sans); + font-size: 15px; + line-height: 1.6; +} + +::selection { + background: var(--accent-light); + color: var(--text); +} + +:focus-visible { + outline: none; + box-shadow: 0 0 0 2px var(--focus-ring); + border-radius: var(--radius-sm); +} + +.container { + width: min(960px, calc(100% - 2rem)); + margin-inline: auto; +} + +code { + font-family: var(--font-mono); + font-size: 0.875em; + padding: 0.125em 0.35em; + border-radius: var(--radius-sm); + background: var(--bg-muted); + border: 1px solid var(--border); +} + +@media (prefers-reduced-motion: reduce) { + *, *::before, *::after { + animation-duration: 0.01ms !important; + animation-iteration-count: 1 !important; + transition-duration: 0.01ms !important; + scroll-behavior: auto !important; + } +} diff --git a/catpred/web/frontend/src/styles/reset.css b/catpred/web/frontend/src/styles/reset.css new file mode 100644 index 0000000..4fa50c8 --- /dev/null +++ b/catpred/web/frontend/src/styles/reset.css @@ -0,0 +1,61 @@ +*, +*::before, +*::after { + margin: 0; + padding: 0; + box-sizing: border-box; +} + +html { + height: 100%; + -webkit-text-size-adjust: 100%; + text-size-adjust: 100%; +} + +body { + min-height: 100%; + -webkit-font-smoothing: antialiased; + -moz-osx-font-smoothing: grayscale; +} + +img, picture, video, canvas, svg { + display: block; + max-width: 100%; +} + +input, button, textarea, select { + font: inherit; + color: inherit; +} + +button { + cursor: pointer; + background: none; + border: none; +} + +a { + color: inherit; + text-decoration: none; +} + +table { + border-collapse: collapse; +} + +h1, h2, h3, h4, h5, h6 { + overflow-wrap: break-word; + font-weight: inherit; +} + +p { + overflow-wrap: break-word; +} + +ul, ol { + list-style: none; +} + +[hidden] { + display: none !important; +} diff --git a/catpred/web/frontend/src/styles/tokens.css b/catpred/web/frontend/src/styles/tokens.css new file mode 100644 index 0000000..a201f4a --- /dev/null +++ b/catpred/web/frontend/src/styles/tokens.css @@ -0,0 +1,43 @@ +:root { + /* Background */ + --bg: #FAFAF9; + --bg-surface: #FFFFFF; + --bg-muted: #F5F5F4; + + /* Text */ + --text: #1A1A1A; + --text-secondary: #57534E; + --text-tertiary: #A8A29E; + + /* Accent */ + --accent: #059669; + --accent-hover: #047857; + --accent-light: #D1FAE5; + + /* Parameter colors */ + --color-kcat: #059669; + --color-km: #DC7B6A; + --color-ki: #8B7FC7; + + /* Semantic */ + --danger: #DC2626; + --ok: #059669; + + /* Borders & shadows */ + --border: #E7E5E4; + --focus-ring: rgba(5, 150, 105, 0.4); + + /* Radius */ + --radius-sm: 6px; + --radius-md: 10px; + --radius-lg: 16px; + + /* Shadows */ + --shadow-sm: 0 1px 2px rgba(0, 0, 0, 0.04); + --shadow-md: 0 4px 12px rgba(0, 0, 0, 0.06); + + /* Typography */ + --font-sans: 'Inter', -apple-system, BlinkMacSystemFont, 'Segoe UI', sans-serif; + --font-serif: 'Newsreader', Georgia, serif; + --font-mono: 'IBM Plex Mono', 'SF Mono', 'Fira Code', monospace; +} diff --git a/catpred/web/frontend/src/styles/utilities.css b/catpred/web/frontend/src/styles/utilities.css new file mode 100644 index 0000000..44241b9 --- /dev/null +++ b/catpred/web/frontend/src/styles/utilities.css @@ -0,0 +1,11 @@ +.sr-only { + position: absolute; + width: 1px; + height: 1px; + padding: 0; + margin: -1px; + overflow: hidden; + clip: rect(0, 0, 0, 0); + white-space: nowrap; + border: 0; +} diff --git a/catpred/web/frontend/tsconfig.app.json b/catpred/web/frontend/tsconfig.app.json new file mode 100644 index 0000000..fab93e9 --- /dev/null +++ b/catpred/web/frontend/tsconfig.app.json @@ -0,0 +1,20 @@ +{ + "compilerOptions": { + "target": "ES2020", + "module": "ESNext", + "lib": ["ES2020", "DOM", "DOM.Iterable"], + "skipLibCheck": true, + "moduleResolution": "bundler", + "allowImportingTsExtensions": true, + "isolatedModules": true, + "moduleDetection": "force", + "noEmit": true, + "jsx": "preserve", + "strict": true, + "noUnusedLocals": true, + "noUnusedParameters": true, + "noFallthroughCasesInSwitch": true, + "noUncheckedSideEffectImports": true + }, + "include": ["src/**/*.ts", "src/**/*.tsx", "src/**/*.vue", "src/env.d.ts"] +} diff --git a/catpred/web/frontend/tsconfig.app.tsbuildinfo b/catpred/web/frontend/tsconfig.app.tsbuildinfo new file mode 100644 index 0000000..7da64e3 --- /dev/null +++ b/catpred/web/frontend/tsconfig.app.tsbuildinfo @@ -0,0 +1 @@ +{"root":["./src/env.d.ts","./src/main.ts","./src/api/client.ts","./src/api/types.ts","./src/composables/useinputrows.ts","./src/composables/useprediction.ts","./src/composables/usereadiness.ts","./src/app.vue","./src/components/appfooter.vue","./src/components/appheader.vue","./src/components/batchupload.vue","./src/components/inputpanel.vue","./src/components/inputrow.vue","./src/components/parameterselector.vue","./src/components/resultpanel.vue","./src/components/skiplink.vue","./src/components/statusbar.vue","./src/components/substrateinputs.vue"],"version":"5.7.3"} \ No newline at end of file diff --git a/catpred/web/frontend/tsconfig.json b/catpred/web/frontend/tsconfig.json new file mode 100644 index 0000000..e4e65e5 --- /dev/null +++ b/catpred/web/frontend/tsconfig.json @@ -0,0 +1,6 @@ +{ + "files": [], + "references": [ + { "path": "./tsconfig.app.json" } + ] +} diff --git a/catpred/web/frontend/vite.config.ts b/catpred/web/frontend/vite.config.ts new file mode 100644 index 0000000..0c1fa7f --- /dev/null +++ b/catpred/web/frontend/vite.config.ts @@ -0,0 +1,18 @@ +import { defineConfig } from 'vite' +import vue from '@vitejs/plugin-vue' + +export default defineConfig({ + plugins: [vue()], + base: '/static/dist/', + build: { + outDir: '../static/dist', + emptyOutDir: true, + }, + server: { + proxy: { + '/ready': 'http://localhost:8000', + '/predict': 'http://localhost:8000', + '/health': 'http://localhost:8000', + }, + }, +}) diff --git a/catpred/web/static/dist/assets/index-BmkWAspG.css b/catpred/web/static/dist/assets/index-BmkWAspG.css new file mode 100644 index 0000000..48da9a2 --- /dev/null +++ b/catpred/web/static/dist/assets/index-BmkWAspG.css @@ -0,0 +1 @@ +.skip-link[data-v-b3acbf9d]{position:absolute;top:-100%;left:1rem;z-index:999;padding:.5rem 1rem;background:var(--accent);color:#fff;border-radius:var(--radius-sm);font-size:.875rem;font-weight:500}.skip-link[data-v-b3acbf9d]:focus{top:.5rem}.footer[data-v-37d50c2f]{border-top:1px solid var(--border);padding:1.5rem 0}.footer-inner[data-v-37d50c2f]{text-align:center}.footer-text[data-v-37d50c2f]{color:var(--text-tertiary);font-size:.8rem}.footer-brand[data-v-37d50c2f]{font-family:var(--font-serif);color:var(--text-secondary)}.footer-sep[data-v-37d50c2f]{margin:0 .25rem;opacity:.5}.mode-selector[data-v-06223ab0]{display:flex;align-items:center;gap:.75rem;flex-wrap:wrap}.mode-pills[data-v-06223ab0]{display:flex;gap:.375rem}.mode-pill[data-v-06223ab0]{height:34px;padding:0 .875rem;border:1px solid var(--border);border-radius:999px;background:var(--bg-surface);font-family:var(--font-mono);font-size:.75rem;font-weight:500;letter-spacing:.03em;color:var(--text-secondary);transition:all .15s}.mode-pill[data-v-06223ab0]:hover:not(:disabled){border-color:var(--text-tertiary)}.mode-pill[data-v-06223ab0]:disabled{opacity:.4;cursor:not-allowed}.mode-substrate.active[data-v-06223ab0]{color:var(--color-kcat);border-color:var(--color-kcat);background:#05966914}.mode-inhibition.active[data-v-06223ab0]{color:var(--color-ki);border-color:var(--color-ki);background:#8b7fc714}.param-checks[data-v-06223ab0]{display:flex;gap:.5rem;align-items:center}.check-label[data-v-06223ab0]{display:flex;align-items:center;gap:.25rem;font-family:var(--font-mono);font-size:.75rem;font-weight:500;color:var(--text-secondary);cursor:pointer;-webkit-user-select:none;user-select:none}.check-label.disabled[data-v-06223ab0]{opacity:.4;cursor:not-allowed}.check-label input[type=checkbox][data-v-06223ab0]{width:14px;height:14px;accent-color:var(--accent);cursor:inherit}.field-label[data-v-812d7d46]{display:block;margin-bottom:.25rem;font-family:var(--font-mono);font-size:.68rem;font-weight:500;letter-spacing:.06em;text-transform:uppercase;color:var(--text-tertiary)}.substrate-list[data-v-812d7d46]{display:grid;gap:.375rem}.substrate-row[data-v-812d7d46]{display:flex;align-items:flex-start;gap:.375rem}.primary-radio[data-v-812d7d46]{display:flex;align-items:center;gap:.25rem;flex-shrink:0;min-width:70px;padding-top:.5rem;cursor:pointer}.primary-radio input[type=radio][data-v-812d7d46]{width:14px;height:14px;accent-color:var(--accent);cursor:pointer}.primary-label[data-v-812d7d46]{font-family:var(--font-mono);font-size:.6rem;font-weight:500;color:var(--text-tertiary);letter-spacing:.04em}.sub-field[data-v-812d7d46]{flex:1;min-width:0}.sub-field input[data-v-812d7d46]{width:100%;padding:.5rem .625rem;border:1px solid var(--border);border-radius:var(--radius-sm);background:var(--bg);font-size:.875rem;line-height:1.5;transition:border-color .15s}.sub-field input[data-v-812d7d46]:focus{outline:none;border-color:var(--accent);box-shadow:0 0 0 2px var(--focus-ring)}.sub-field input[aria-invalid=true][data-v-812d7d46]{border-color:var(--danger)}.remove-sub-btn[data-v-812d7d46]{flex-shrink:0;width:28px;height:34px;margin-top:.125rem;border:1px solid var(--border);border-radius:var(--radius-sm);font-size:1rem;color:var(--text-tertiary);transition:all .15s;display:flex;align-items:center;justify-content:center}.remove-sub-btn[data-v-812d7d46]:hover{color:var(--danger);border-color:#dc26264d;background:#dc26260a}.add-sub-btn[data-v-812d7d46]{margin-top:.25rem;padding:.25rem .5rem;border:1px dashed var(--border);border-radius:var(--radius-sm);font-family:var(--font-mono);font-size:.68rem;font-weight:500;color:var(--text-tertiary);transition:all .15s}.add-sub-btn[data-v-812d7d46]:hover{border-color:var(--accent);color:var(--accent);background:#0596690a}.field-error[data-v-812d7d46]{margin-top:.25rem;font-size:.75rem;color:var(--danger)}.row-item[data-v-d4696ebf]{border:1px solid var(--border);border-radius:var(--radius-md);background:var(--bg-surface);padding:.75rem}.row-head[data-v-d4696ebf]{display:flex;align-items:center;justify-content:space-between;margin-bottom:.5rem}.row-title[data-v-d4696ebf]{font-family:var(--font-mono);font-size:.7rem;font-weight:500;color:var(--text-tertiary);letter-spacing:.02em}.remove-btn[data-v-d4696ebf]{height:28px;padding:0 .6rem;border:1px solid var(--border);border-radius:999px;font-family:var(--font-mono);font-size:.65rem;font-weight:500;color:var(--danger);transition:background .15s,border-color .15s}.remove-btn[data-v-d4696ebf]:hover{background:#dc26260f;border-color:#dc26264d}.row-fields[data-v-d4696ebf]{display:grid;gap:.5rem}.field-label[data-v-d4696ebf]{display:block;margin-bottom:.25rem;font-family:var(--font-mono);font-size:.68rem;font-weight:500;letter-spacing:.06em;text-transform:uppercase;color:var(--text-tertiary)}input[data-v-d4696ebf],textarea[data-v-d4696ebf]{width:100%;padding:.5rem .625rem;border:1px solid var(--border);border-radius:var(--radius-sm);background:var(--bg);font-size:.875rem;line-height:1.5;transition:border-color .15s}input[data-v-d4696ebf]:focus,textarea[data-v-d4696ebf]:focus{outline:none;border-color:var(--accent);box-shadow:0 0 0 2px var(--focus-ring)}input[aria-invalid=true][data-v-d4696ebf],textarea[aria-invalid=true][data-v-d4696ebf]{border-color:var(--danger)}input[aria-invalid=true][data-v-d4696ebf]:focus,textarea[aria-invalid=true][data-v-d4696ebf]:focus{box-shadow:0 0 0 2px #dc26264d}textarea[data-v-d4696ebf]{resize:vertical;min-height:48px}.field-error[data-v-d4696ebf]{margin-top:.25rem;font-size:.75rem;color:var(--danger)}.upload-zone[data-v-bd35de81]{border:1px dashed var(--border);border-radius:var(--radius-md);padding:1rem;text-align:center;cursor:pointer;transition:border-color .15s,background .15s}.upload-zone[data-v-bd35de81]:hover,.upload-zone.dragging[data-v-bd35de81]{border-color:var(--accent);background:#0596690a}.upload-text[data-v-bd35de81]{font-size:.8rem;color:var(--text-secondary)}.upload-text strong[data-v-bd35de81]{font-weight:500;color:var(--text)}.upload-hint[data-v-bd35de81]{margin-top:.25rem;font-family:var(--font-mono);font-size:.68rem;color:var(--text-tertiary)}.sr-only[data-v-bd35de81]{position:absolute;width:1px;height:1px;padding:0;margin:-1px;overflow:hidden;clip:rect(0,0,0,0);white-space:nowrap;border:0}.input-panel[data-v-fe6ff3ab]{background:var(--bg-surface);border:1px solid var(--border);border-radius:var(--radius-lg);padding:1rem;box-shadow:var(--shadow-sm)}.panel-title[data-v-fe6ff3ab]{font-family:var(--font-serif);font-size:1.25rem;font-weight:400;color:var(--text);margin-bottom:.75rem}.rows-list[data-v-fe6ff3ab]{display:grid;gap:.5rem;margin-bottom:.75rem}.actions[data-v-fe6ff3ab]{display:flex;flex-wrap:wrap;gap:.5rem;margin-top:.75rem}.btn[data-v-fe6ff3ab]{display:inline-flex;align-items:center;justify-content:center;gap:.375rem;height:40px;padding:0 1rem;border:1px solid var(--border);border-radius:999px;font-size:.8rem;font-weight:500;transition:all .15s;min-width:44px}.btn[data-v-fe6ff3ab]:disabled{opacity:.5;cursor:not-allowed}.btn-ghost[data-v-fe6ff3ab]{background:transparent;color:var(--text-secondary)}.btn-ghost[data-v-fe6ff3ab]:hover:not(:disabled){background:var(--bg-muted);color:var(--text)}.btn-primary[data-v-fe6ff3ab]{background:var(--accent);border-color:var(--accent);color:#fff;box-shadow:0 1px 3px #05966933}.btn-primary[data-v-fe6ff3ab]:hover:not(:disabled){background:var(--accent-hover);border-color:var(--accent-hover)}.spinner[data-v-fe6ff3ab]{width:14px;height:14px;border:2px solid rgba(255,255,255,.3);border-top-color:#fff;border-radius:50%;animation:spin-fe6ff3ab .6s linear infinite}@keyframes spin-fe6ff3ab{to{transform:rotate(360deg)}}.helper-text[data-v-fe6ff3ab]{margin-top:.75rem;font-size:.8rem;color:var(--text-tertiary);line-height:1.5}.helper-text strong[data-v-fe6ff3ab]{font-weight:600;color:var(--text-secondary)}.status-bar[data-v-d5b2b2e6]{border:1px solid var(--border);border-radius:var(--radius-md);background:var(--bg-surface);padding:.5rem .75rem;display:grid;gap:.375rem}.status-row[data-v-d5b2b2e6]{display:flex;align-items:center;gap:.5rem;flex-wrap:wrap;min-height:28px}.status-label[data-v-d5b2b2e6]{font-family:var(--font-mono);font-size:.65rem;font-weight:500;letter-spacing:.06em;text-transform:uppercase;color:var(--text-tertiary)}.badge[data-v-d5b2b2e6]{display:inline-flex;align-items:center;height:22px;padding:0 .5rem;border:1px solid var(--border);border-radius:999px;font-family:var(--font-mono);font-size:.65rem;font-weight:500}.badge.online[data-v-d5b2b2e6]{color:var(--ok);border-color:#0596694d;background:#0596690f}.badge.offline[data-v-d5b2b2e6],.badge.limited[data-v-d5b2b2e6]{color:var(--danger);border-color:#dc26264d;background:#dc26260f}.badge.checking[data-v-d5b2b2e6]{color:var(--text-tertiary)}.status-hint[data-v-d5b2b2e6]{font-family:var(--font-mono);font-size:.7rem;color:var(--text-tertiary)}.prediction-row[data-v-d5b2b2e6]{border-top:1px solid 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CatPred

',1)),le(bc,{"service-status":V(t).status.value,"service-hint":V(t).statusHint.value,"prediction-status":V(s).status.value,"prediction-error":V(s).error.value,elapsed:V(s).elapsedSeconds.value,"can-retry":V(s).lastJobs.value!==null&&V(s).status.value==="error",onRetry:h[0]||(h[0]=b=>V(s).retry())},null,8,["service-status","service-hint","prediction-status","prediction-error","elapsed","can-retry"])]),E("div",kc,[le(ga,{mode:r.value,"predict-kcat":i.value,"predict-km":o.value,available:V(t).availableCheckpoints.value,"onUpdate:mode":h[1]||(h[1]=b=>r.value=b),"onUpdate:predictKcat":h[2]||(h[2]=b=>i.value=b),"onUpdate:predictKm":h[3]||(h[3]=b=>o.value=b)},null,8,["mode","predict-kcat","predict-km","available"])]),l.value?(O(),$("p",Nc,Y(l.value),1)):he("",!0),E("div",Gc,[le(lc,{rows:V(n).rows.value,mode:r.value,disabled:V(s).isRunning.value,onAddRow:h[4]||(h[4]=b=>V(n).addRow()),onRemoveRow:h[5]||(h[5]=b=>V(n).removeRow(b)),onUpdateField:h[6]||(h[6]=(b,T,P)=>V(n).updateField(b,T,P)),onAddSubstrate:h[7]||(h[7]=b=>V(n).addSubstrate(b)),onRemoveSubstrate:h[8]||(h[8]=(b,T)=>V(n).removeSubstrate(b,T)),onUpdateSubstrateSmiles:h[9]||(h[9]=(b,T,P)=>V(n).updateSubstrateSmiles(b,T,P)),onSetPrimary:h[10]||(h[10]=(b,T)=>V(n).setPrimary(b,T)),onLoadSample:h[11]||(h[11]=b=>V(n).loadSample(r.value)),onImportCsv:u,onSubmit:d},null,8,["rows","mode","disabled"]),le(Vc,{results:V(s).results.value},null,8,["results"])])])]),le(aa)],64))}}),jc=He(Hc,[["__scopeId","data-v-1443df81"]]);Ol(jc).mount("#app"); diff --git a/catpred/web/static/dist/index.html b/catpred/web/static/dist/index.html new file mode 100644 index 0000000..e49d413 --- /dev/null +++ b/catpred/web/static/dist/index.html @@ -0,0 +1,24 @@ + + + + + + CatPred | Enzyme Kinetics Prediction + + + + + + + + + +
+ + From 2447ef795efbcb0f215606f4e2351e0a24205588 Mon Sep 17 00:00:00 2001 From: theproteinbot Date: Sat, 14 Mar 2026 21:55:27 -0400 Subject: [PATCH 23/34] fix(web): remove legacy index.html fallback MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Stop silently falling back to static/index.html when the Vue dist build is missing โ€” surface a clear 404 instead so deployment issues are visible rather than hidden behind the old page. Co-Authored-By: Claude Opus 4.6 (1M context) --- catpred/web/app.py | 11 +++++++---- 1 file changed, 7 insertions(+), 4 deletions(-) diff --git a/catpred/web/app.py b/catpred/web/app.py index 8158f24..a45de62 100644 --- a/catpred/web/app.py +++ b/catpred/web/app.py @@ -321,10 +321,13 @@ def create_app( @app.get("/", include_in_schema=False) def root() -> FileResponse: - index_path = static_root / "index.html" - if not index_path.exists(): - raise HTTPException(status_code=404, detail="Landing page not found.") - return FileResponse(index_path) + dist_index = static_root / "dist" / "index.html" + if not dist_index.exists(): + raise HTTPException( + status_code=404, + detail="Frontend build not found. Run `npm run build` in catpred/web/frontend/.", + ) + return FileResponse(dist_index) @app.get("/health") def health() -> dict[str, str]: From c6962167758e6728f0302882bbc5d8b4ba986698 Mon Sep 17 00:00:00 2001 From: theproteinbot Date: Sat, 14 Mar 2026 22:00:38 -0400 Subject: [PATCH 24/34] style(web): move Maranas Group attribution to footer Move "Developed in Maranas Group at Penn State" with link to maranasgroup.com into the footer. Keep only Paper and GitHub links in the header. Co-Authored-By: Claude Opus 4.6 (1M context) --- catpred/web/frontend/src/App.vue | 14 ------------- .../web/frontend/src/components/AppFooter.vue | 20 ++++++++++++++----- .../{index-DWuYjQ6d.js => index--vkmdqN7.js} | 14 ++++++------- .../web/static/dist/assets/index-BmkWAspG.css | 1 - .../web/static/dist/assets/index-C3Gjkhgi.css | 1 + catpred/web/static/dist/index.html | 4 ++-- 6 files changed, 25 insertions(+), 29 deletions(-) rename catpred/web/static/dist/assets/{index-DWuYjQ6d.js => index--vkmdqN7.js} (70%) delete mode 100644 catpred/web/static/dist/assets/index-BmkWAspG.css create mode 100644 catpred/web/static/dist/assets/index-C3Gjkhgi.css diff --git a/catpred/web/frontend/src/App.vue b/catpred/web/frontend/src/App.vue index f193aab..de1464e 100644 --- a/catpred/web/frontend/src/App.vue +++ b/catpred/web/frontend/src/App.vue @@ -94,15 +94,6 @@ function onImportCsv(text: string) {

CatPred

@@ -38,4 +36,16 @@ margin: 0 0.25rem; opacity: 0.5; } + +.footer-link { + color: var(--text-secondary); + text-decoration: none; + border-bottom: 1px solid transparent; + transition: color 0.15s, border-color 0.15s; +} + +.footer-link:hover { + color: var(--accent); + border-bottom-color: var(--accent); +} diff --git a/catpred/web/static/dist/assets/index-DWuYjQ6d.js b/catpred/web/static/dist/assets/index--vkmdqN7.js similarity index 70% rename from catpred/web/static/dist/assets/index-DWuYjQ6d.js rename to catpred/web/static/dist/assets/index--vkmdqN7.js index d8ca87b..80d05e2 100644 --- a/catpred/web/static/dist/assets/index-DWuYjQ6d.js +++ b/catpred/web/static/dist/assets/index--vkmdqN7.js @@ -2,19 +2,19 @@ * @vue/shared v3.5.30 * (c) 2018-present Yuxi (Evan) You and Vue contributors * @license MIT -**/function Hs(e){const t=Object.create(null);for(const s of e.split(","))t[s]=1;return s=>s in t}const 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CatPredยท Maranas Group ยท Penn State University ยท 2026

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From 4863d26794cecb1c6d453e2b37187a3d8fb62784 Mon Sep 17 00:00:00 2001 From: theproteinbot Date: Sat, 14 Mar 2026 22:14:44 -0400 Subject: [PATCH 25/34] feat(web): add GoatCounter visit tracking with footer counter Add GoatCounter analytics script (privacy-friendly, no cookies) and display visit count in the footer via GoatCounter's public JSON API. Counter gracefully hides if the API is unreachable. Co-Authored-By: Claude Opus 4.6 (1M context) --- catpred/web/frontend/index.html | 1 + .../web/frontend/src/components/AppFooter.vue | 27 +++++++++++++++++++ .../web/static/dist/assets/index--vkmdqN7.js | 20 -------------- .../web/static/dist/assets/index-B_EBo3Xr.js | 20 ++++++++++++++ .../web/static/dist/assets/index-C3Gjkhgi.css | 1 - .../web/static/dist/assets/index-DOG0cTDK.css | 1 + catpred/web/static/dist/index.html | 5 ++-- 7 files changed, 52 insertions(+), 23 deletions(-) delete mode 100644 catpred/web/static/dist/assets/index--vkmdqN7.js create mode 100644 catpred/web/static/dist/assets/index-B_EBo3Xr.js delete mode 100644 catpred/web/static/dist/assets/index-C3Gjkhgi.css create mode 100644 catpred/web/static/dist/assets/index-DOG0cTDK.css diff --git a/catpred/web/frontend/index.html b/catpred/web/frontend/index.html index 42c4030..f3f370f 100644 --- a/catpred/web/frontend/index.html +++ b/catpred/web/frontend/index.html @@ -19,5 +19,6 @@
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+ From b4d7b3b7b3b655469d5b41852a5a69177b706537 Mon Sep 17 00:00:00 2001 From: theproteinbot Date: Sat, 14 Mar 2026 22:22:32 -0400 Subject: [PATCH 26/34] style(web): remove favicon from browser tab Co-Authored-By: Claude Opus 4.6 (1M context) --- catpred/web/frontend/index.html | 3 +-- catpred/web/static/dist/index.html | 3 +-- 2 files changed, 2 insertions(+), 4 deletions(-) diff --git a/catpred/web/frontend/index.html b/catpred/web/frontend/index.html index f3f370f..75a4818 100644 --- a/catpred/web/frontend/index.html +++ b/catpred/web/frontend/index.html @@ -8,8 +8,7 @@ name="description" content="Predict enzyme kinetic parameters (kcat, Km, Ki) from substrate and enzyme inputs with CatPred." /> - - + - - + Date: Sat, 14 Mar 2026 22:36:52 -0400 Subject: [PATCH 27/34] docs(readme): add web app section, frontend dev info, and update citation Add www.catpred.com badge and Web App section highlighting the live prediction tool. Document Vue 3 frontend location and dev/build commands in the Web API section. Update citation from bioRxiv preprint to published Nature Communications reference (2025, vol 16, art 2072). Co-Authored-By: Claude Opus 4.6 (1M context) --- README.md | 47 ++++++++++++++++++++++++++++++++++------------- 1 file changed, 34 insertions(+), 13 deletions(-) diff --git a/README.md b/README.md index 080ba25..04e8a87 100644 --- a/README.md +++ b/README.md @@ -1,6 +1,7 @@ # CatPred: A Comprehensive Framework for Deep Learning In Vitro Enzyme Kinetic Parameters -[![DOI](https://img.shields.io/badge/DOI-10.1101/2024.03.10.584340-blue)](https://www.nature.com/articles/s41467-025-57215-9) +[![Web App](https://img.shields.io/badge/Web_App-www.catpred.com-059669)](https://www.catpred.com) +[![DOI](https://img.shields.io/badge/DOI-10.1038/s41467--025--57215--9-blue)](https://www.nature.com/articles/s41467-025-57215-9) [![Colab](https://img.shields.io/badge/GoogleColab-tiny.cc/catpred-red)](https://tiny.cc/catpred) [![License](https://img.shields.io/badge/License-MIT-green)](LICENSE) @@ -18,6 +19,7 @@ ## ๐Ÿ“š Table of Contents +- [Web App](#web-app) - [Google Colab Interface](#colab-interface) - [Local Installation](#local-installation) - [System Requirements](#requirements) @@ -32,6 +34,17 @@ --- +## ๐ŸŒ Web App + +CatPred is live at **[www.catpred.com](https://www.catpred.com)** โ€” no installation needed. + +- **Two prediction modes:** Substrate kinetics (kcat/Km) and Inhibition (Ki) +- **Multi-substrate input** with primary substrate marker +- **CSV import/export** for batch workflows +- Powered by a [Modal](https://modal.com) serverless backend + +--- + ## ๐ŸŒ Google Colab Interface For ease of use without any hardware requirements, a Google Colab interface is available here: [tiny.cc/catpred](http://tiny.cc/catpred). @@ -104,7 +117,7 @@ For released benchmark datasets, the number of entries with 3D structure can be ### ๐ŸŒ Web API (Optional) -CatPred also provides an optional FastAPI service for prediction workflows. +CatPred also provides an optional FastAPI service for prediction workflows. The Vue 3 frontend lives in `catpred/web/frontend/` and is served by the API at `/`. Install web dependencies: @@ -112,12 +125,21 @@ Install web dependencies: pip install -e ".[web]" ``` -Run the API: +Run the API (serves the built frontend at `/`): ```bash catpred_web --host 0.0.0.0 --port 8000 ``` +To develop the frontend: + +```bash +cd catpred/web/frontend +npm install +npm run dev # Vite dev server with HMR +npm run build # Production build (vue-tsc + vite) +``` + Endpoints: - `GET /health` โ€” liveness check. - `GET /ready` โ€” backend configuration/readiness. @@ -367,15 +389,14 @@ This source code is licensed under the MIT license found in the `LICENSE` file i If you find the models useful in your research, we ask that you cite the relevant paper: ```bibtex -@article {Boorla2024.03.10.584340, - author = {Veda Sheersh Boorla and Costas D. Maranas}, - title = {CatPred: A comprehensive framework for deep learning in vitro enzyme kinetic parameters kcat, Km and Ki}, - elocation-id = {2024.03.10.584340}, - year = {2024}, - doi = {10.1101/2024.03.10.584340}, - publisher = {Cold Spring Harbor Laboratory}, - URL = {https://www.biorxiv.org/content/early/2024/03/26/2024.03.10.584340}, - eprint = {https://www.biorxiv.org/content/early/2024/03/26/2024.03.10.584340.full.pdf}, - journal = {bioRxiv} +@article{Boorla2025CatPred, + author = {Boorla, Veda Sheersh and Maranas, Costas D.}, + title = {CatPred: a comprehensive framework for deep learning in vitro enzyme kinetic parameters}, + journal = {Nature Communications}, + year = {2025}, + volume = {16}, + number = {2072}, + doi = {10.1038/s41467-025-57215-9}, + URL = {https://www.nature.com/articles/s41467-025-57215-9} } ``` From 8223db54ffad5404fa3fac879517e4a0ce91134c Mon Sep 17 00:00:00 2001 From: theproteinbot Date: Sat, 14 Mar 2026 22:39:06 -0400 Subject: [PATCH 28/34] docs(readme): add web app announcement Co-Authored-By: Claude Opus 4.6 (1M context) --- README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/README.md b/README.md index 04e8a87..5e886ee 100644 --- a/README.md +++ b/README.md @@ -9,6 +9,7 @@ ## ๐Ÿšจ Announcements ๐Ÿ“ข +- โœ… **NEW** - Web app live at [www.catpred.com](https://www.catpred.com) โ€” predict kcat, Km, and Ki directly in your browser! - โœ… **28th Feb 2025** - Published in [_Nature Communications_](https://www.nature.com/articles/s41467-025-57215-9) - โœ… **27th Dec 2024** - Updated repository with scripts to reproduce results from the manuscript. - ๐Ÿšง **TODO** From bbcc77ad24f2fb418c16796eb5589d4b5ca5668a Mon Sep 17 00:00:00 2001 From: theproteinbot Date: Sat, 14 Mar 2026 22:40:31 -0400 Subject: [PATCH 29/34] docs(readme): use date instead of NEW for web app announcement Co-Authored-By: Claude Opus 4.6 (1M context) --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 5e886ee..502b4b4 100644 --- a/README.md +++ b/README.md @@ -9,7 +9,7 @@ ## ๐Ÿšจ Announcements ๐Ÿ“ข -- โœ… **NEW** - Web app live at [www.catpred.com](https://www.catpred.com) โ€” predict kcat, Km, and Ki directly in your browser! +- โœ… **14th Mar 2026** - Web app live at [www.catpred.com](https://www.catpred.com) โ€” predict kcat, Km, and Ki directly in your browser! - โœ… **28th Feb 2025** - Published in [_Nature Communications_](https://www.nature.com/articles/s41467-025-57215-9) - โœ… **27th Dec 2024** - Updated repository with scripts to reproduce results from the manuscript. - ๐Ÿšง **TODO** From daa3e61248baa8cb78dc886db5324751e93eef84 Mon Sep 17 00:00:00 2001 From: theproteinbot Date: Sat, 14 Mar 2026 22:43:08 -0400 Subject: [PATCH 30/34] docs(readme): remove TODO from announcements Co-Authored-By: Claude Opus 4.6 (1M context) --- README.md | 3 --- 1 file changed, 3 deletions(-) diff --git a/README.md b/README.md index 502b4b4..4350a24 100644 --- a/README.md +++ b/README.md @@ -12,9 +12,6 @@ - โœ… **14th Mar 2026** - Web app live at [www.catpred.com](https://www.catpred.com) โ€” predict kcat, Km, and Ki directly in your browser! - โœ… **28th Feb 2025** - Published in [_Nature Communications_](https://www.nature.com/articles/s41467-025-57215-9) - โœ… **27th Dec 2024** - Updated repository with scripts to reproduce results from the manuscript. -- ๐Ÿšง **TODO** - - Add prediction codes for models using 3D-structural features. - - Add instructions to install CatPred using a Docker image. --- From f9c59c746bd39a451346629c5819348256f97374 Mon Sep 17 00:00:00 2001 From: theproteinbot Date: Sat, 14 Mar 2026 22:47:12 -0400 Subject: [PATCH 31/34] docs(readme): update POST example with real glucokinase + glucose sample Replace placeholder SMILES/sequence with the same human glucokinase (GCK, UniProt P35557) + D-glucose example used on www.catpred.com. Co-Authored-By: Claude Opus 4.6 (1M context) --- README.md | 9 ++++++--- 1 file changed, 6 insertions(+), 3 deletions(-) diff --git a/README.md b/README.md index 4350a24..b66ede2 100644 --- a/README.md +++ b/README.md @@ -149,7 +149,7 @@ By default, the API is hardened for service use: - `results_dir` is constrained under `CATPRED_API_RESULTS_ROOT`. - for local backend (and modal requests with fallback enabled), `checkpoint_dir` must resolve under `CATPRED_API_CHECKPOINT_ROOT`. -Minimal `POST /predict` example for local inference using `input_rows`: +Minimal `POST /predict` example for local inference using `input_rows` (human glucokinase + D-glucose): ```bash curl -X POST http://127.0.0.1:8000/predict \ @@ -158,8 +158,11 @@ curl -X POST http://127.0.0.1:8000/predict \ "parameter": "kcat", "checkpoint_dir": "kcat", "input_rows": [ - {"SMILES": "CCO", "sequence": "ACDEFGHIK", "pdbpath": "seq_a"}, - {"SMILES": "CCN", "sequence": "LMNPQRSTV", "pdbpath": "seq_b"} + { + "SMILES": "C(C1C(C(C(C(O1)O)O)O)O)O", + "sequence": "MLDDRARMEAAKKEKVEQILAEFQLQEEDLKKVMRRMQKEMDRGLRLETHEEASVKMLPTYVRSTPEGSEVGDFLSLDLGGTNFRVMLVKVGEGEEGQWSVKTKHQMYSIPEDAMTGTAEMLFDYISECISDFLDKHQMKHKKLPLGFTFSFPVRHEDIDKGILLNWTKGFKASGAEGNNVVGLLRDAIKRRGDFEMDVVAMVNDTVATMISCYYEDHQCEVGMIVGTGCNACYMEEMQNVELVEGDEGRMCVNTEWGAFGDSGELDEFLLEYDRLVDESSANPGQQLYEKLIGGKYMGELVRLVLLRLVDENLLFHGEASEQLRTRGAFETRFVSQVESDTGDRKQIYNILSTLGLRPSTTDCDIVRRACESVSTRAAHMCSAGLAGVINRMRESRSEDVMRITVGVDGSVYKLHPSFKERFHASVRRLTPSCEITFIESEEGSGRGAALVSAVACKKACMLGQ", + "pdbpath": "GCK_HUMAN" + } ], "results_dir": "batch1", "backend": "local" From e854d0a8ec9f5cd43985fd3ec9b118f48b69a767 Mon Sep 17 00:00:00 2001 From: theproteinbot Date: Sat, 14 Mar 2026 22:55:21 -0400 Subject: [PATCH 32/34] docs(readme): fix backtick typo, URL casing, add ToC entries & Docker section; remove favicon MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit - Fix 4-backtick code block closing to 3 - Fix git clone URL casing (catpred โ†’ CatPred) - Add Fine-Tuning and Docker entries to Table of Contents - Add Docker subsection under Local Installation - Remove favicon SVG and its link tag from index.html Co-Authored-By: Claude Opus 4.6 (1M context) --- README.md | 15 ++++- catpred/web/static/catpred-favicon.svg | 11 ---- catpred/web/static/index.html | 89 ++++++++++++++++++++++++-- 3 files changed, 97 insertions(+), 18 deletions(-) delete mode 100644 catpred/web/static/catpred-favicon.svg diff --git a/README.md b/README.md index b66ede2..b8a3676 100644 --- a/README.md +++ b/README.md @@ -26,6 +26,8 @@ - [Web API (Optional)](#web-api-optional) - [Vercel Deployment (Optional)](#vercel-deployment-optional) - [Reproducibility](#reproduce) + - [Fine-Tuning On Custom Data](#-fine-tuning-on-custom-data) + - [Docker](#-docker) - [Acknowledgements](#acknw) - [License](#license) - [Citations](#citations) @@ -81,12 +83,12 @@ cd catpred_pipeline wget -c --tries=5 --timeout=30 https://catpred.s3.us-east-1.amazonaws.com/capsule_data_update.tar.gz || \ wget -c --tries=5 --timeout=30 https://catpred.s3.amazonaws.com/capsule_data_update.tar.gz tar -xzf capsule_data_update.tar.gz -git clone https://github.com/maranasgroup/catpred.git +git clone https://github.com/maranasgroup/CatPred.git cd catpred conda env create -f environment.yml conda activate catpred pip install -e . -```` +``` `stride` is Linux-only and optional for the default demos. If needed for your workflow, install it separately on Linux: @@ -94,6 +96,15 @@ pip install -e . conda install -c kimlab stride ``` +### ๐Ÿณ Docker + +A `Dockerfile` is included for containerized usage (PyTorch 2.4, CUDA 12.4, Python 3.12.4 via Mambaforge). + +```bash +docker build -t catpred . +docker run --gpus all -it catpred +``` + ### ๐Ÿ”ฎ Prediction The Jupyter Notebook `batch_demo.ipynb` and the Python script `demo_run.py` show the usage of pre-trained models for prediction. diff --git a/catpred/web/static/catpred-favicon.svg b/catpred/web/static/catpred-favicon.svg deleted file mode 100644 index 3c33710..0000000 --- a/catpred/web/static/catpred-favicon.svg +++ /dev/null @@ -1,11 +0,0 @@ - - - - - - - - - - - diff --git a/catpred/web/static/index.html b/catpred/web/static/index.html index 30e3c9a..954c827 100644 --- a/catpred/web/static/index.html +++ b/catpred/web/static/index.html @@ -14,8 +14,8 @@ href="https://fonts.googleapis.com/css2?family=Newsreader:ital,opsz,wght@0,6..72,300;0,6..72,400;1,6..72,300;1,6..72,400&family=Outfit:wght@200;300;400;500&family=IBM+Plex+Mono:wght@300;400&display=swap" rel="stylesheet" /> - - + + @@ -40,10 +40,19 @@
-
+
+
+ Published in Nature Communications +

Predict Enzyme
Kinetic Parameters

+

Deep learning ensemble predictions for kcat, Km, and Ki with built-in uncertainty quantification.

+ Start predicting โ†’ +
+
+ +
-

Enzyme Kinetics Prediction

+

Enzyme Kinetics Prediction

kcat, Km, and Ki with uncertainty estimates

@@ -107,7 +116,14 @@

Results

-

Run a prediction to see kinetic estimates here.

+ +

Run a prediction to see kinetic
estimates here.

@@ -121,8 +137,71 @@

Results

+
+
+
+

How It Works

+

Three steps to enzyme kinetics predictions

+
+
+
+ 01 +

Enter Data

+

Provide substrate SMILES notation and enzyme amino acid sequence for each entry.

+
+
+ 02 +

Run Prediction

+

Our deep learning ensemble processes inputs through multiple model checkpoints.

+
+
+ 03 +

Get Results

+

Receive predicted kinetic parameters with uncertainty estimates and detailed metrics.

+
+
+
+
+ +
+
+
+

Peer-Reviewed Research

+

CatPred is backed by rigorous scientific methodology

+
+
+ +
+

CatPred: A comprehensive framework for deep learning in vitro enzyme kinetic parameters kcat, Km and Ki

+

Nature Communications, 2025 — Veda Sheersh Boorla, Costas D. Maranas

+ Read the paper โ†’ +
+
+
+
+
+
+
+
+
+ CatPred +

Maranas Group · Penn State University

+
+ +
+

© 2025 Maranas Group, Penn State University. All rights reserved.

+
+
+ Run prediction From 4c7149e256f5acc9abec9f5db9ed15c739cace49 Mon Sep 17 00:00:00 2001 From: Veda Sheersh Boorla Date: Mon, 16 Mar 2026 14:41:15 -0400 Subject: [PATCH 33/34] Remove CI/CD section and incorrect fine-tuning section from README --- README.md | 59 ------------------------------------------------------- 1 file changed, 59 deletions(-) diff --git a/README.md b/README.md index b8a3676..1b9094a 100644 --- a/README.md +++ b/README.md @@ -282,65 +282,6 @@ CATPRED_MODAL_TOKEN= CATPRED_MODAL_FALLBACK_TO_LOCAL=0 ``` -#### CI/CD (GitHub Actions + Vercel + Modal) - -This repo includes two GitHub Actions workflows: - -- `.github/workflows/ci.yml` - - Runs on every PR and push to `main`. - - Installs minimal API dependencies, compiles all Python files, and smoke-tests API entrypoints. -- `.github/workflows/deploy-modal.yml` - - Runs on push to `main` when backend files change (and manually via `workflow_dispatch`). - - Deploys `modal_app.py` automatically. - -To enable automatic Modal deploys from GitHub Actions, add repository secrets: - -- `MODAL_TOKEN_ID` -- `MODAL_TOKEN_SECRET` - -Create these from Modal: - -1. Go to [https://modal.com/settings/tokens](https://modal.com/settings/tokens). -2. Create a token with deploy permissions for your workspace. -3. Copy token ID and secret into GitHub repo settings: - `Settings -> Secrets and variables -> Actions -> New repository secret`. - -Vercel deployment remains automatic from the connected GitHub branch (`main`). - -### ๐Ÿงช Fine-Tuning On Custom Data - -You can fine-tune CatPred on your own regression targets using `train.py`. - -1. Prepare train/val/test CSVs with at least: -- `SMILES` -- `sequence` -- `pdbpath` (unique per unique sequence) -- one numeric target column (for example: `log10kcat_max`) - -2. Build a protein-records file that covers all `pdbpath` values in your splits: - -```bash -python ./scripts/create_pdbrecords.py --data_file --out_file -``` - -3. Train: - -```bash -python train.py \ - --protein_records_path \ - --data_path \ - --separate_val_path \ - --separate_test_path \ - --dataset_type regression \ - --smiles_columns SMILES \ - --target_columns \ - --add_esm_feats \ - --loss_function mve \ - --save_dir -``` - -For working end-to-end examples, see the training commands in scripts such as `scripts/reproduce_figS10_catpred.sh`. - ### ๐Ÿ”„ Reproducing Publication Results We provide three separate ways for reproducing the results of the publication. From 1786e0f94be40ec2cd1b20ff96479c26b565a2cf Mon Sep 17 00:00:00 2001 From: theproteinbot Date: Tue, 7 Apr 2026 08:40:53 -0400 Subject: [PATCH 34/34] fix(frontend): patch vite security advisory --- catpred/web/frontend/package-lock.json | 24 ++++++++++-------------- catpred/web/frontend/package.json | 14 +++++++------- 2 files changed, 17 insertions(+), 21 deletions(-) diff --git a/catpred/web/frontend/package-lock.json b/catpred/web/frontend/package-lock.json index e780d52..0444aa6 100644 --- a/catpred/web/frontend/package-lock.json +++ b/catpred/web/frontend/package-lock.json @@ -13,7 +13,7 @@ "devDependencies": { "@vitejs/plugin-vue": "^5.2.3", "typescript": "~5.7.3", - "vite": "^6.3.5", + "vite": "^6.4.2", "vue-tsc": "^2.2.8" } }, @@ -1062,9 +1062,9 @@ "license": "MIT" }, "node_modules/brace-expansion": { - "version": "2.0.2", - "resolved": "https://registry.npmjs.org/brace-expansion/-/brace-expansion-2.0.2.tgz", - "integrity": "sha512-Jt0vHyM+jmUBqojB7E1NIYadt0vI0Qxjxd2TErW94wDz+E2LAm5vKMXXwg6ZZBTHPuUlDgQHKXvjGBdfcF1ZDQ==", + "version": "2.0.3", + "resolved": "https://registry.npmjs.org/brace-expansion/-/brace-expansion-2.0.3.tgz", + "integrity": "sha512-MCV/fYJEbqx68aE58kv2cA/kiky1G8vux3OR6/jbS+jIMe/6fJWa0DTzJU7dqijOWYwHi1t29FlfYI9uytqlpA==", "dev": true, "license": "MIT", "dependencies": { @@ -1251,12 +1251,11 @@ "license": "ISC" }, "node_modules/picomatch": { - "version": "4.0.3", - "resolved": "https://registry.npmjs.org/picomatch/-/picomatch-4.0.3.tgz", - "integrity": "sha512-5gTmgEY/sqK6gFXLIsQNH19lWb4ebPDLA4SdLP7dsWkIXHWlG66oPuVvXSGFPppYZz8ZDZq0dYYrbHfBCVUb1Q==", + "version": "4.0.4", + "resolved": "https://registry.npmjs.org/picomatch/-/picomatch-4.0.4.tgz", + "integrity": "sha512-QP88BAKvMam/3NxH6vj2o21R6MjxZUAd6nlwAS/pnGvN9IVLocLHxGYIzFhg6fUQ+5th6P4dv4eW9jX3DSIj7A==", "dev": true, "license": "MIT", - "peer": true, "engines": { "node": ">=12" }, @@ -1369,7 +1368,6 @@ "integrity": "sha512-84MVSjMEHP+FQRPy3pX9sTVV/INIex71s9TL2Gm5FG/WG1SqXeKyZ0k7/blY/4FdOzI12CBy1vGc4og/eus0fw==", "devOptional": true, "license": "Apache-2.0", - "peer": true, "bin": { "tsc": "bin/tsc", "tsserver": "bin/tsserver" @@ -1379,12 +1377,11 @@ } }, "node_modules/vite": { - "version": "6.4.1", - "resolved": "https://registry.npmjs.org/vite/-/vite-6.4.1.tgz", - "integrity": "sha512-+Oxm7q9hDoLMyJOYfUYBuHQo+dkAloi33apOPP56pzj+vsdJDzr+j1NISE5pyaAuKL4A3UD34qd0lx5+kfKp2g==", + "version": "6.4.2", + "resolved": "https://registry.npmjs.org/vite/-/vite-6.4.2.tgz", + "integrity": "sha512-2N/55r4JDJ4gdrCvGgINMy+HH3iRpNIz8K6SFwVsA+JbQScLiC+clmAxBgwiSPgcG9U15QmvqCGWzMbqda5zGQ==", "dev": true, "license": "MIT", - "peer": true, "dependencies": { "esbuild": "^0.25.0", "fdir": "^6.4.4", @@ -1466,7 +1463,6 @@ "resolved": "https://registry.npmjs.org/vue/-/vue-3.5.30.tgz", "integrity": "sha512-hTHLc6VNZyzzEH/l7PFGjpcTvUgiaPK5mdLkbjrTeWSRcEfxFrv56g/XckIYlE9ckuobsdwqd5mk2g1sBkMewg==", "license": "MIT", - "peer": true, "dependencies": { "@vue/compiler-dom": "3.5.30", "@vue/compiler-sfc": "3.5.30", diff --git a/catpred/web/frontend/package.json b/catpred/web/frontend/package.json index c67dc21..d00b5d5 100644 --- a/catpred/web/frontend/package.json +++ b/catpred/web/frontend/package.json @@ -11,10 +11,10 @@ "dependencies": { "vue": "^3.5.13" }, - "devDependencies": { - "@vitejs/plugin-vue": "^5.2.3", - "typescript": "~5.7.3", - "vite": "^6.3.5", - "vue-tsc": "^2.2.8" - } -} + "devDependencies": { + "@vitejs/plugin-vue": "^5.2.3", + "typescript": "~5.7.3", + "vite": "^6.4.2", + "vue-tsc": "^2.2.8" + } +}