final no

app.py

@@ -31,9 +31,15 @@ model = ImageClassifier()
 model.load_state_dict(torch.hub.load_state_dict_from_url(model_path))
 model.eval()
 
+# Function to process sketchpad input
+def sketchToNumpy(image):
+    # Extract the 'composite' key from the sketchpad input dictionary
+    imArray = image['composite']  # 'composite' contains the drawn image
+    return imArray
+
 # Gradio preprocessing and prediction pipeline
 def predict_digit(image):
-    # Directly handle the numpy array from gr.Sketchpad
+    # Convert the sketchpad input into a PIL Image
     image = Image.fromarray(image).convert('L')  # Convert to grayscale
 
     # Preprocess: resize to 28x28 and normalize
@@ -54,8 +60,8 @@ def predict_digit(image):
 
 # Create Gradio Interface
 interface = gr.Interface(
-    fn=predict_digit,
-    inputs=gr.Sketchpad(),  # Sketchpad for users to draw
+    fn=lambda x: predict_digit(sketchToNumpy(x)),
+    inputs=gr.Sketchpad(crop_size=(256,256), type='numpy', image_mode='L'),
     outputs="text",
     title="Digit Recognizer",
     description="Draw a digit (0-9) and the model will predict the number!"