Update app.py

app.py

@@ -6,9 +6,34 @@ import tensorflow as tf
 #Load the model 
 model = tf.keras.models.load_model("cnn_model.h5")
 
-def predict(input_data):
-    return(type(input_data))
-        
+def predict(image_array):
+    try:
+        print("Function called!")
+
+        # Check for blank or None input
+        if image_array is None or np.sum(image_array) == 0:
+            return "Please draw a digit."
+
+        # Convert to PIL image and process
+        image = Image.fromarray(image_array.astype("uint8"), mode="L")
+        image = ImageOps.invert(image).resize((28, 28))
+
+        # Normalize and reshape for model input
+        image_array = np.array(image).astype("float32") / 255.0
+        image_array = image_array.reshape(1, 28, 28, 1)
+
+        # Predict
+        logits = model.predict(image_array)
+        prediction = int(np.argmax(logits))
+        confidence = float(tf.nn.softmax(logits)[0][prediction])
+
+        return f"Digit: {prediction} (confidence: {confidence:.2%})"
+
+    except Exception as err:
+        return f"💥 Runtime error: {str(err)}"
+
+
+gr.Sketchpad(image_mode="L", canvas_size=(200, 200), type="numpy")
 gr.Interface(
     fn=predict,
     inputs=gr.Sketchpad(image_mode="L", canvas_size=(200, 200)),