testScript.py

import tensorflow as tf
import numpy as np
import os
import pandas as pd
from PIL import Image

IMG_SIZE = (224, 224)
test_dir = ""
output_csv = "./Submissions/ResNet_submission.csv"

IMAGENET_MEAN = tf.constant([103.939, 116.779, 123.68], dtype=tf.float32)


def preprocess_input_resnet(x):
    x = tf.cast(x, tf.float32) 
    x = x[..., ::-1]       
    x = x - IMAGENET_MEAN   
    return x


model_path = "Models\my_model.keras"  
model = tf.keras.models.load_model(model_path)
print("Model loaded successfully!")


results = []
image_files = sorted(os.listdir(test_dir))
pred_classes_so_far = []

for img_name in image_files:
    img_path = os.path.join(test_dir, img_name)

    try:
        img = Image.open(img_path).convert("RGB")
        img = img.resize(IMG_SIZE)

        x = np.array(img, dtype=np.float32)
        x = np.expand_dims(x, axis=0)

        x = preprocess_input_resnet(x)

        pred = model.predict(x, verbose=0)
        pred_class = int(np.argmax(pred))
        pred_classes_so_far.append(pred_class)

    except Exception as e:
        print(f"Skipping {img_name}: {e}")
        if pred_classes_so_far:
            pred_class = int(pred_classes_so_far[-1])
        else:
            pred_class = 0

    results.append([img_name, pred_class])


df = pd.DataFrame(results, columns=["ImageName", "ClassLabel"])
df.to_csv(output_csv, index=False)
print(f"Saved predictions to {output_csv}")