Update app.py

app.py

@@ -1,186 +1,34 @@
-# 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}")
+model = tf.keras.models.load_model('Model1_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):
-    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)
+    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"),
-        gr.Label(label="Raw Predictions")
-    ], 
+    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 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()