app.py

# import gradio as gr
# import tensorflow as tf
# from tensorflow.keras.preprocessing import image
# import numpy as np

# # Load the trained model
# model = tf.keras.models.load_model('./model12_acc99_kera.h5')

# # Define the class names
# classes = ['Colon Adenocarcinoma', 'Colon Benign Tissue', 'Lung Adenocarcinoma', 'Lung Benign Tissue', 'Lung Squamous Cell Carcinoma']

# # Function to preprocess the uploaded image and make predictions
# def predict(img):
#     img = img.resize((224, 224))
#     img_array = image.img_to_array(img)
#     img_array = np.expand_dims(img_array, axis=0)
#     img_array = img_array / 255.0  # Normalize the image

#     predictions = model.predict(img_array)
#     predicted_class = classes[np.argmax(predictions[0])]
    
#     return predicted_class

# # Create a Gradio interface
# iface = gr.Interface(
#     fn=predict, 
#     inputs=gr.Image(type='pil'), 
#     outputs=gr.Textbox(label="Prediction"), 
#     title="Lung and Colon Cancer Detection",
#     description="Upload an image of histopathological tissue to detect if it is a type of lung or colon cancer."
# )

# # Launch the interface
# iface.launch()

# import gradio as gr
# import tensorflow as tf
# from tensorflow.keras.preprocessing import image
# import numpy as np
# import logging

# # Set up logging
# logging.basicConfig(level=logging.DEBUG)

# # Initialize the model variable
# model = None

# # Load the trained model
# try:
#     model_path = './model1_kera.h5'
#     logging.info(f"Loading model from: {model_path}")
#     model = tf.keras.models.load_model(model_path)
#     logging.info("Model loaded successfully.")
# except Exception as e:
#     logging.error(f"Error loading model: {e}")

# # Define the class names
# classes = ['Colon Adenocarcinoma', 'Colon Benign Tissue', 'Lung Adenocarcinoma', 'Lung Benign Tissue', 'Lung Squamous Cell Carcinoma']

# # Function to preprocess the uploaded image and make predictions
# def predict(img):
#     global model
#     try:
#         logging.debug("Received image for prediction.")
        
#         # Resize and preprocess the image
#         img = img.resize((224, 224))
#         img_array = image.img_to_array(img)
#         img_array = np.expand_dims(img_array, axis=0)
        
#         logging.debug("Image preprocessed successfully.")
        
#         # Ensure the model is loaded
#         if model is None:
#             raise ValueError("Model is not loaded properly.")
        
#         # Make predictions
#         predictions = model.predict(img_array)
#         predicted_class_index = np.argmax(predictions[0])
#         predicted_class = classes[predicted_class_index]
        
#         logging.debug(f"Prediction successful: {predicted_class}")
        
#         # Return the predicted class and the raw predictions
#         return predicted_class, predictions[0].tolist()
#     except Exception as e:
#         logging.error(f"Error during prediction: {e}")
#         # Print the error message in the output
#         return str(e), str(e)

# # Create a Gradio interface
# iface = gr.Interface(
#     fn=predict, 
#     inputs=gr.Image(type='pil'), 
#     outputs=[
#         gr.Textbox(label="Prediction"),
#         gr.Label(label="Raw Predictions")
#     ], 
#     title="Lung and Colon Cancer Detection",
#     description="Upload an image of histopathological tissue to detect if it is a type of lung or colon cancer."
# )

# # Launch the interface
# iface.launch()
import gradio as gr
import tensorflow as tf
from tensorflow.keras.preprocessing import image as keras_image
from PIL import Image
import numpy as np
import logging
import os

# Set up logging
logging.basicConfig(level=logging.DEBUG)

# Initialize the model variable
model = None

# Function to load the model
def load_model():
    global model
    model_path = r'Model1_kera.h5'  # Replace with the actual path to your model file
    if not os.path.exists(model_path):
        logging.error(f"Model file does not exist at path: {model_path}")
        return False
    try:
        logging.info(f"Loading model from: {model_path}")
        model = tf.keras.models.load_model(model_path)
        logging.info("Model loaded successfully.")
        return True
    except Exception as e:
        logging.error(f"Error loading model: {e}")
        return False

# Load the model when the script starts
model_loaded = load_model()

# Define the class names
classes = ['Colon Adenocarcinoma', 'Colon Benign Tissue', 'Lung Adenocarcinoma', 'Lung Benign Tissue', 'Lung Squamous Cell Carcinoma']

# Function to preprocess the uploaded image and make predictions
def predict(img):
    global model
    try:
        logging.debug("Received image for prediction.")
        
        # Resize and preprocess the image
        img = img.resize((224, 224))
        img_array = keras_image.img_to_array(img)
        img_array = tf.expand_dims(img_array, 0)  # Add batch dimension
        
        logging.debug("Image preprocessed successfully.")
        
        # Ensure the model is loaded
        if model is None:
            raise ValueError("Model is not loaded properly.")
        
        # Make predictions
        predictions = model.predict(img_array)
        score = tf.nn.softmax(predictions[0])
        predicted_class_index = tf.argmax(score).numpy()
        predicted_class = classes[predicted_class_index]
        
        logging.debug(f"Prediction successful: {predicted_class}")
        
        # Return the predicted class and the raw predictions
        return predicted_class, predictions[0].tolist()
    except Exception as e:
        logging.error(f"Error during prediction: {e}")
        # Print the error message in the output
        return str(e), str(e)

# Create a Gradio interface
iface = gr.Interface(
    fn=predict, 
    inputs=gr.Image(type='pil'), 
    outputs=[
        gr.Textbox(label="Prediction"),
        gr.Label(label="Raw Predictions")
    ], 
    title="Lung and Colon Cancer Detection",
    description="Upload an image of histopathological tissue to detect if it is a type of lung or colon cancer."
)

# Launch the interface
iface.launch()