Merge new changes as now is stable version

.gitignore

@@ -4,7 +4,3 @@ __pycache__/
 .DS_Store
 Notebooks/
 config/
-
-BAFU_e_CMYK_pos_hoch.pdf
-my_figure.pdf
-my_figure.png

backend/Dockerfile

@@ -1,7 +1,11 @@
-FROM python:3.9
+FROM python:3.11
 COPY requirements.txt app/requirements.txt
 WORKDIR /app
 RUN pip install -r requirements.txt
+RUN apt-get update && \
+    apt-get install -y openjdk-17-jre && \
+    apt-get clean && \
+    update-ca-certificates -f \
 COPY . /app
 EXPOSE 8000
 CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "8000" ,"--reload"]

backend/app.py

@@ -1,10 +1,5 @@
 from fastapi import FastAPI
-import joblib
-import numpy as np
-
-from descriptors import get_maccs_fingerprints
-
-model_pipeline = joblib.load('pepper_pipeline_model.pkl')
+from predict_target_endpoint import predict
 app = FastAPI()
 
 @app.get("/")
@@ -13,11 +8,4 @@ async def read_root():
 
 @app.get('/predict/')
 async def serve_foo(smiles: str):
-    smiles_list = smiles.split(',')
-
-    # Calculate the MACCS fingerprints for the input data
-    X = get_maccs_fingerprints(smiles_list)
-
-    # Use the pipeline to make predictions
-    predicted_logB = model_pipeline.predict(X)
-    return np.round((10**predicted_logB ) *100).tolist()
+    return predict(smiles.split(','))

backend/predict_target_endpoint.py

@@ -0,0 +1,26 @@
+from pepper_lab.predict import Predict
+import pandas as pd
+
+def predict(input_smiles):
+    data = pd.DataFrame(input_smiles, columns=["SMILES"])
+    pepper_predict = Predict(renku=True)
+    predictions_df = pepper_predict.predict_endpoint('pepper_object_wwtp_optimized_trained_model.pkl',
+                                    input_model_format='pickle', input_smiles=data,
+                                    input_smiles_type='dataframe') # The backend accepts single molecules
+
+    # Select what to show in the app
+    logb = predictions_df['logB_predicted']
+    breakthrough_perc = (10**logb)*100
+    rounded_b_perc = round(breakthrough_perc, 1)
+    predictions_df['Breakthrough (%)'] = rounded_b_perc
+
+    confidence = predictions_df['{}_predicted'.format(pepper_predict.model.target_variable_std_name)]
+    rounded_confidence = round(confidence, 2)
+    predictions_df['Confidence 0-1'] = rounded_confidence
+
+    predictions_df = predictions_df[[pepper_predict.model.compound_name,
+                                     pepper_predict.model.smiles_name,
+                                     'Breakthrough (%)',
+                                     'Confidence 0-1']]
+    predictions_df.fillna("", inplace=True)
+    return predictions_df.to_dict(orient="list")

backend/requirements.txt

@@ -1,6 +1,5 @@
-fastapi==0.115.2
-joblib==1.4.2
-molfeat==0.10.0
-numpy==1.26.0
-scikit-learn==1.3.2
-uvicorn==0.32.0
+fastapi>=0.115.2,<1.0.0
+pandas>=2.2.3,<3.0.0
+pepper-lab>=1.1.0
+rdkit>=2024.9.5,<2025.0.0
+uvicorn>=0.32.0,<1.0.0

docker-compose.yml

@@ -1,4 +1,4 @@
-version: '3.9'
+version: '3.12'
 services:
   frontend:
     build: streamlit

streamlit/Dockerfile

@@ -1,13 +1,12 @@
-FROM python:3.9
-
+FROM python:3.12
 COPY requirements.txt app/requirements.txt
 WORKDIR /app
-
 RUN pip install -r requirements.txt
+RUN apt-get update && \
+    apt-get install -y openjdk-17-jre && \
+    apt-get clean && \
+    update-ca-certificates -f
 COPY . /app
-
 EXPOSE 8501
-
 ENTRYPOINT ["streamlit","run"]
-
 CMD ["app.py"]

streamlit/app.py

@@ -1,39 +1,69 @@
-import pandas as pd
 import streamlit as st
-import requests
+import pandas as pd
 from rdkit.Chem import PandasTools
+import requests
+
+from utils import image_from_mol
 
+@st.cache_data
+def convert_df(df):
+    # IMPORTANT: Cache the conversion to prevent computation on every rerun
+    return df.to_csv().encode("utf-8")
+example_csv = pd.read_csv('test_pepper_app.csv')
 
 def main():
 
     # Streamlit app title
     st.title("PEPPER: an app to Predict Environmental Pollutant PERsistence ")
 
+    st.markdown("""
+    Currently we support the prediction of the expected percentage breakthrough of micropollutants from
+    conventional wastewater treatment, that is, the percentage that potentially escapes the plant 
+    without being successfully removed. Visit section [Learn more](https://pepper-app.streamlit.app/Learn_more) 
+    for further details.  
+    """)
+
     # Upload CSV file
     uploaded_file = st.file_uploader("Upload a CSV file with chemical substance data", type="csv")
 
+
+    csv = convert_df(example_csv)
+
+    st.sidebar.download_button(
+        label="Download example file",
+        data=csv,
+        file_name="pepper_example.csv",
+        mime="text/csv",
+    )
+
     if uploaded_file is not None:
         # Load the uploaded data
-        input_data = pd.read_csv(uploaded_file)
+        df = pd.read_csv(uploaded_file)
 
         # Show the input data
-        st.write("Uploaded data:", input_data)
+        st.write(" ### Uploaded data:", df)
 
-        response = requests.request("get", "http://backend:8000/predict/",
-        params={"smiles": ",".join(input_data.SMILES)})
-
-        # Show it as a dataframe
-        predictions_df = pd.DataFrame(input_data)
-        predictions_df['Breakthrough (%)'] = response.json()
+        print('Start predictions')
+        # Calculate using pepper-lab
+        response = requests.request("get", "http://backend:8000/predict/", params={"smiles": ",".join(df.SMILES)}).json()
+        df = pd.DataFrame.from_dict(response)
+
+        PandasTools.AddMoleculeColumnToFrame(df, 'SMILES', 'Structure')
+        df["Structure"] = df["Structure"].apply(image_from_mol)
+        df.drop(columns='SMILES', inplace=True)
 
         # Show the predictions
-        st.write("Predictions:", predictions_df)
-
-        # PandasTools.AddMoleculeColumnToFrame(predictions_df, smilesCol='SMILES')
-        # predictions_df.rename(columns={'ROMol': 'Structure'})
-        # predictions_df.drop(columns='SMILES', inplace=True)
+        st.markdown(""" ### Predictions: """)
+        config = {
+            "Structure": st.column_config.ImageColumn(width="medium"),
+        }
+        st.dataframe(df, column_config=config, row_height=100)
 
-        st.markdown(predictions_df.to_html(escape=False), unsafe_allow_html=True)
+        # st.write("""
+        # 📢⚠️ The frame below shows the predictions along chemical structures.
+        # We are working to give you the chemical structures as part of the file to be downloaded.  """)
+        #
+        # st.markdown(predictions_df.to_html(escape=False), unsafe_allow_html=True)
 
 
 if __name__ == '__main__':

streamlit/requirements.txt

@@ -1,9 +1,4 @@
-joblib==1.4.2
-molfeat==0.10.0
-pandas==2.2.3
-streamlit==1.38.0
-scipy==1.12.0
-matplotlib==3.6.2
-numpy==1.26.0
-scikit-learn==1.3.0
-rdkit==2024.03.5
+pandas>=2.2.3,<3.0.0
+requests
+rdkit>=2024.9.5,<2025.0.0
+streamlit>=1.41.1

streamlit/test_pepper_app.csv

@@ -0,0 +1,125 @@
+SMILES,Compound
+O=c1[nH]cnc2c1ncn2C1CC(O)C(CO)O1,2'-Deoxyinosine
+CC(C)C(O)(CC(=O)O)C(=O)O,2-Isopropylmalic acid
+OCC1OC(OC2C(CO)OC(O)C(O)C2O)C(O)C(O)C1O,alpha-Lactose
+O=P(c1ccccc1)(c1ccccc1)c1ccccc1,triphenylphosphineoxide
+CCCCC(=O)N(Cc1ccc(-c2ccccc2-c2nn[nH]n2)cc1)C(C(=O)O)C(C)C,Valsartan
+CC(C)(CC(=O)O)CC(=O)O,"3,3-Dimethylglutaric acid"
+COCCc1ccc(OCC(O)CNC(C)C)cc1,Metoprolol
+NC(Cc1ccc(O)cc1)C(=O)O,L-Tyrosine
+O=c1[nH]sc2ccccc12,"1,2-Benzisothiazolin-3-one"
+CC(CCC(=O)O)C1CCC2C3CCC4CC(O)CCC4(C)C3CC(O)C12C,Deoxycholic acid
+O=C(O)C1CC(=O)N(Cc2ccco2)C1,1-(2-Furylmethyl)-5-oxopyrrolidine-3-carboxylic acid
+Cc1ccc(C(N)=O)cn1,6-Methylnicotinamide
+CNCCCC(C#N)(c1ccc(OC)c(OC)c1)C(C)C,"2-(3,4-Dimethoxyphenyl)-5-methylamino-2-isopropylvaleronitrile"
+O=C(O)C(O)Cc1ccccc1,3-Phenyllactic acid
+CC(C)CC(N)C(=O)NC(C(=O)O)C(C)C,Leucylvaline
+CCOP(=O)(OCC)OCC,Triethyl phosphate
+Nc1c2c(=O)[nH]c(O)cc2nn1-c1ccccc1,"3-amino-2-phenyl-2H-pyrazolo[4,3-c]pyridine-4,6-diol"
+CC1CN2CC(C)OB(O1)OC(C)C2,Triisopropanolamine cyclic borate
+CC(O)(CC(=O)O)C(=O)O,citramalic acid
+N#Cc1cc(Br)c(O)c(Br)c1,Bromoxynil
+CCCCNS(=O)(=O)c1ccccc1,N-Butylbenzenesulfonamide
+O=c1cc[nH]c2ccccc12,4-Hydroxyquinoline
+O=C1CC2(CCCCC2)CN1,"3,3-pentamethylene-4-butyrolactam"
+CC(O)COC(C)COC(C)COC(C)COC(C)COC(C)COC(C)COC(C)COC(C)COC(C)COC(C)CO,Undecapropylene glycol
+COc1cc(CC(N)C(=O)O)ccc1O,3-Methoxytyrosine
+CC(Cc1ccc(C(C)C(=O)O)cc1)C(=O)O,Carboxyibuprofen
+NC(=O)c1ccccc1O,Salicylamide
+CC(C)CC(NC(=O)CN)C(=O)O,Glycyl-L-leucine
+CCCCCC(O)C(O)CC=CCCCCCCCC(=O)O,(+/-)12(13)-DiHOME
+CC(CC(=O)O)C(=O)O,Methylsuccinic acid
+COc1cc(C=O)cc(OC)c1O,Syringaldehyde
+CCC=CCC(O)C(O)C=CC(O)CCCCCCCC(=O)O,"(10e,15z)-9,12,13-Trihydroxyoctadeca-10,15-dienoic acid"
+CC(CCl)OP(=O)(OC(C)CCl)OC(C)CCl,Tris(1-chloro-2-propyl)phosphate
+CC(N)C12CC3CC(CC(C3)C1)C2,Rimantadine
+CC1CC2OC2C=CC(=O)CC(=O)O1,Decarestrictine F
+NC(Cc1ccccc1O)C(=O)O,2-Hydroxyphenylalanine
+S=c1[nH]c2ccccc2s1,2-Mercaptobenzothiazole
+Oc1ccc2ncccc2c1,6-Quinolinol
+O=C(O)C1(O)CC(O)C(O)C(O)C1,D-(-)-Quinic acid
+O=C(O)c1cc(O)c(O)c(O)c1,Gallic acid
+COc1ccc(-c2coc3cc(O)cc(O)c3c2=O)cc1,Biochanin A
+Nc1cccc(O)c1,3-Aminophenol
+COc1cc(C(=O)O)ccc1O,Vanillic acid
+CC12C=CC(=O)C=C1CCC1C2C(O)CC2(C)C1CCC2(O)C(=O)CO,Prednisolone
+COc1cc(O)c(C(=O)c2ccccc2)cc1S(=O)(=O)O,Sulisobenzone
+c1ccc2n[nH]nc2c1,Benzotriazole
+Cc1nc(-c2ccc(OCC(C)C)c(C#N)c2)sc1C(=O)O,Febuxostat
+Cc1cc(O)cc(C)c1S(C)(=O)=O,Methiocarb-TP methiocarb sulfone phenol (M05)
+O=CNCc1ccccc1,N-Benzylformamide
+OCCOCCOCCOCCOCCOCCOCCOCCOCCOCCOCCOCCOCCOCCOCCO,"3,6,9,12,15,18,21,24,27,30,33,36,39,42-Tetradecaoxatetratetracontane-1,44-diol"
+NC(=O)N1c2ccccc2C(O)C(O)c2ccccc21,"10,11-Dihydro-10,11-dihydroxycarbamazepine"
+CC12CCC3c4ccc(OS(=O)(=O)O)cc4CCC3C1CCC2=O,Estrone sulfate
+CCC=CCC=CCC=CCC=CCC=CCCCCCC(=O)O,Docosapentaenoic acid
+O=C(O)CC1(C(=O)O)CCCCC1,1-(Carboxymethyl)cyclohexanecarboxylic acid
+CC(=O)Nc1ccc(C)cc1C,"N-(2,4-Dimethylphenyl)acetamide"
+NC(CC(=O)O)C(=O)NC(Cc1ccccc1)C(=O)O,L-Aspartyl-L-phenylalanine
+Cc1cc2nc3c(=O)[nH]c(=O)nc-3n(CC(O)C(O)C(O)CO)c2cc1C,Riboflavin
+Cc1oc(C(=O)O)cc(=O)c1C,"5,6-dimethyl-4-oxo-4H-pyran-2-carboxylic acid"
+CC(=O)NC(CC(C)C)C(=O)O,N-Acetyl-L-leucine
+O=C(O)Cc1ccccc1Nc1c(Cl)cccc1Cl,Diclofenac
+OCCN(CCO)CCO,Triethanolamine
+Cc1ccncc1,4-Picoline
+O=C(NC1CCCCC1)NC1CCCCC1,Dicyclohexylurea
+CC(C)(Oc1ccc(Cl)cc1)C(=O)O,Clofibric acid
+NCC(=O)N1CCCC1C(=O)O,Glycylproline
+OCCOCCOCCOCCOCCOCCOCCOCCOCCOCCO,"3,6,9,12,15,18,21,24,27-Nonaoxanonacosane-1,29-diol"
+NC(N)=NCCCC(N)C(=O)O,DL-Arginine
+CN(C)CC1CCCCC1(O)c1cccc(O)c1,O-Desmethyl-cis-tramadol
+CC(=O)Nc1ccc(O)c(C(=O)O)c1,N-Acetyl-5-aminosalicylic acid
+CC(C)(C)NC(=O)NCCO,1-(2-Hydroxyethyl)-3-t-butylurea
+O=C(O)c1ccc([O-])c(O)c1,"3,4-Dihydroxybenzoate"
+Cc1cccc(C(=O)NCC(=O)O)c1,3-Methylhippuric acid
+CC(C)CC(NC(=O)OC(C)(C)C)C(N)=O,tert-Butyl N-[1-(aminocarbonyl)-3-methylbutyl]carbamate
+CCCCOP(=O)(O)OCCCC,Dibutyl phosphate
+Cc1nc(C)c(C)nc1C,"2,3,5,6-Tetramethylpyrazine"
+CN(C)CCC=C1c2ccccc2CCc2ccccc21,Amitriptyline
+NCC(O)c1ccccc1,Phenylethanolamine
+CC(C)NCC(O)COc1ccc(COCCOC(C)C)cc1,Bisoprolol
+NC(CCC(=O)O)C(=O)NC(Cc1ccc(O)cc1)C(=O)O,Glutamyltyrosine
+COc1ccc(C=O)cc1,4-Methoxybenzaldehyde
+COc1cccc(CC(=O)O)c1,3-Methoxyphenylacetic acid
+Cc1cc(=O)oc2cc(O)c(O)cc12,"6,7-Dihydroxy-4-methylcoumarin"
+NCC1(CC(=O)O)CCCCC1,Gabapentin
+O=C(O)c1ccc([N+](=O)[O-])cc1,4-Nitrobenzoic acid
+Cc1cc(=O)oc2cc(N)ccc12,7-Amino-4-methylcoumarin
+CC=C(C)C(=O)O,Tiglic acid
+CCCc1nc(C(C)(C)O)c(C(=O)O)n1Cc1ccc(-c2ccccc2-c2nn[nH]n2)cc1,Olmesartan
+OCCOCCOCCOCCOCCOCCOCCOCCOCCOCCOCCOCCO,Dodecaethylene glycol
+Nc1ccccc1C(=O)O,Anthranilic acid
+CN(C)C(=O)Oc1ccc[n+](C)c1,Pyridostigmine
+O=S(=O)(O)c1ccccc1,Benzenesulfonic acid
+COc1ccc(C2Sc3ccccc3N(CCN(C)C)C(=O)C2O)cc1,Desacetyl diltiazem
+O=C(O)C(c1ccccc1Cl)N1CCc2sccc2C1,Clopidogrel carboxylic acid
+Cn1cnc2[nH]c(=O)[nH]c(=O)c21,7-Methylxanthine
+CN(CC(=O)O)C(=N)N,Creatine
+CCCCCCC=CCCCCCCCC(=O)O,Palmitoleic acid
+NC1CCCCC1,Cyclohexylamine
+CC1NC(=O)NC1CCCCCC(=O)O,Desthiobiotin
+O=C1CCCCN1,2-Piperidone
+CC(C)(Oc1ccc(CCNC(=O)c2ccc(Cl)cc2)cc1)C(=O)O,Bezafibrate
+CN1C(=O)CC(C)(c2ccccc2)C1=O,Methsuximide
+O=S(=O)(O)c1ccc2nc(-c3ccccc3)[nH]c2c1,Phenylbenzimidazole sulfonic acid
+O=C1OC(C(O)CO)C(O)=C1O,Ascorbic acid
+CCN(CC)CC(=O)Nc1c(C)cccc1C,Lidocaine
+O=C1NS(=O)(=O)c2ccccc21,Saccharin
+O=C(O)CCc1ccc(O)cc1,3-(4-Hydroxyphenyl)propionic acid
+CCC=CCC(O)C(O)CCC(O)CCCCCCCC(=O)O,"(15Z)-9,12,13-Trihydroxy-15-octadecenoic acid"
+CCOP(=O)(Sc1ccccc1)Sc1ccccc1,EDDP
+c1ccc2c(c1)[nH]c1cnccc12,Norharman
+CCCCCCC(=O)O,Heptanoic acid
+NC(CCC(=O)O)C(=O)O,L-Glutamic acid
+CN1CC(=O)NC1=O,N-Methylhydantoin
+CC(=O)CCc1ccc(O)cc1,4-(4-Hydroxyphenyl)butan-2-one
+CCCCCCCC(O)CC(=O)O,3-Hydroxydecanoic acid
+c1ccc(-c2cnc[nH]2)cc1,4-Phenylimidazole
+CN1CCC23c4c5ccc(O)c4OC2C(O)C=CC3C1C5,Morphine
+O=C(O)c1c[nH]c2ccccc12,3-Indolylcarboxylic Acid
+CC(C)C1NC(=O)C2CCCN2C1=O,XLUAWXQORJEMBD-UHFFFAOYSA-N
+O=C(O)C=Cc1ccc(O)cc1,trans-4-hydroxycinnamic acid
+O=S(=O)(c1ccc(O)cc1)c1ccc(O)cc1,"4,4'-Sulfonyldiphenol"
+O=C(O)CCCCCCCCCCCC(=O)O,Tridecanedioic acid
+CC(N)C(=O)N1CCCC1C(=O)O,L-Alanyl-L-proline
+CC(C)N(C(=O)C(=O)O)c1ccccc1,Propachlor OXA
+CC1(C)CC(O)CC(C)(C)N1CCO,"4-Hydroxy-1-(2-hydroxyethyl)-2,2,6,6-tetramethylpiperidine"

streamlit/utils.py

@@ -0,0 +1,12 @@
+import io
+import base64
+from rdkit.Chem import Draw
+
+
+def image_from_mol(mol):
+    img = Draw.MolToImage(mol)
+    bio = io.BytesIO()
+    img.save(bio, format="PNG")
+    img_bytes = bio.getvalue()
+    base64_str = base64.b64encode(img_bytes).decode("utf-8")
+    return f"data:image/png;base64,{base64_str}"