Update app.py

app.py

@@ -13,25 +13,31 @@ model = tf.keras.models.load_model("best_model_weights.h5")  # Replace with the
 
 # Define the image classification function
 def classify_image(input_image):
-    # Preprocess the input image
-    input_image = Image.open(BytesIO(input_image))
-    input_image = input_image.resize((img_width, img_height))
-    input_image = np.array(input_image) / 255.0  # Normalize pixel values
+    try:
+        # Preprocess the input image
+        input_image = Image.open(BytesIO(input_image))
+        input_image = input_image.resize((img_width, img_height))
+        input_image = np.array(input_image) / 255.0  # Normalize pixel values
 
-    # Make a prediction using the model
-    predictions = model.predict(np.expand_dims(input_image, axis=0))
+        # Make a prediction using the model
+        predictions = model.predict(np.expand_dims(input_image, axis=0))
 
-    # Get the class label with the highest probability
-    class_index = np.argmax(predictions)
-    class_prob = predictions[0][class_index]
+        # Get the class label with the highest probability
+        class_index = np.argmax(predictions)
+        class_prob = predictions[0][class_index]
 
-    # Define class labels (you can replace these with your actual class labels)
-    class_labels = ["Normal", "Cataract"]
+        # Define class labels (you can replace these with your actual class labels)
+        class_labels = ["Normal", "Cataract"]
 
-    # Get the class label
-    class_label = class_labels[class_index]
+        # Get the class label
+        class_label = class_labels[class_index]
+
+        return f"Predicted Class: {class_label} (Probability: {class_prob:.2f})"
+    except Exception as e:
+        return "Error: Invalid image file"
+
+# ...
 
-    return f"Predicted Class: {class_label} (Probability: {class_prob:.2f})"
 
 # Define the Gradio interface
 iface = gr.Interface(