Update app.py

app.py

@@ -37,9 +37,17 @@ 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)
 
 # Load the trained model
-model = tf.keras.models.load_model('./model12_acc99_kera.h5')
+try:
+    model = tf.keras.models.load_model('path/to/your/model.h5')
+    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']
@@ -47,20 +55,27 @@ classes = ['Colon Adenocarcinoma', 'Colon Benign Tissue', 'Lung Adenocarcinoma',
 # Function to preprocess the uploaded image and make predictions
 def predict(img):
     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.")
+        
         # 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]
+        return predicted_class, predictions[0].tolist()
     except Exception as e:
-        # Print the error message
+        logging.error(f"Error during prediction: {e}")
+        # Print the error message in the output
         return str(e), str(e)
 
 # Create a Gradio interface
@@ -76,4 +91,4 @@ iface = gr.Interface(
 )
 
 # Launch the interface
-iface.launch()
+iface.launch()