Upload app.py

app.py

@@ -8,23 +8,30 @@ from PIL import Image
 # Define the path to your model file
 model_path = "best_model_InceptionV2.keras"
 
-# Load the trained InceptionV2 model
-model = tf.keras.models.load_model(model_path)
+# Try to load the trained InceptionV2 model
+try:
+    model = tf.keras.models.load_model(model_path)
+    print("Model loaded successfully.")
+except Exception as e:
+    print(f"Error loading the model: {e}")
 
 # Function for prediction
 def predict(img):
-    img_resized = img.resize((224, 224))  # Resize image to the target size
-    img_array = image.img_to_array(img_resized)  # Convert image to array
-    img_array = np.expand_dims(img_array, axis=0)  # Add batch dimension
-    img_array = preprocess_input(img_array)  # Preprocess image according to model requirements
-
-    predictions = model.predict(img_array)
-    class_idx = np.argmax(predictions, axis=1)[0]
-    class_labels = ['Benign', 'Malignant']  # Update according to your class labels
-    class_label = class_labels[class_idx]
-    confidence = float(predictions[0][class_idx])
-
-    return f"Class: {class_label}, Confidence: {confidence:.2f}"
+    try:
+        img_resized = img.resize((224, 224))  # Resize image to the target size
+        img_array = image.img_to_array(img_resized)  # Convert image to array
+        img_array = np.expand_dims(img_array, axis=0)  # Add batch dimension
+        img_array = preprocess_input(img_array)  # Preprocess image according to model requirements
+
+        predictions = model.predict(img_array)
+        class_idx = np.argmax(predictions, axis=1)[0]
+        class_labels = ['Benign', 'Malignant']  # Update according to your class labels
+        class_label = class_labels[class_idx]
+        confidence = float(predictions[0][class_idx])
+
+        return f"Class: {class_label}, Confidence: {confidence:.2f}"
+    except Exception as e:
+        return f"Error in prediction: {e}"
 
 # Define the Gradio app
 with gr.Blocks() as demo: