# c.py

from tensorflow import keras

from keras.models import load_model

from keras.preprocessing.image import load_img, img_to_array

import numpy as np

def predict_image(file_path):

    try:

        # Load the pre-trained model

        model_path = r'model\CNN_model.h5'

        print(f"Loading model from: {model_path}")

        loaded_model = load_model(model_path)  # Load the model

        print("Model loaded successfully!")

        # Preprocess the image

        img = load_img(file_path, target_size=(224, 224))  # Resize to target size

        img_array = img_to_array(img)  # Convert to numpy array

        img_array = np.expand_dims(img_array, axis=0)  # Add batch dimension

        img_array = img_array / 255.0  # Normalize pixel values

        # Debugging: Check the shape and content of the image array

        print(f"Image shape after preprocessing: {img_array.shape}")

        # Predict the class

        predictions = loaded_model.predict(img_array)

        print(f"Raw predictions: {predictions}")

        # Get the predicted class

        predicted_class = np.argmax(predictions, axis=1)[0]

        print(f"Predicted class: {predicted_class}")

        return predicted_class  # Return only the predicted class

    except Exception as e:

        print(f"Error processing image or predicting: {e}")

        return None