Update app1.py

app1.py

@@ -6,27 +6,25 @@ from PIL import Image
 # Load the trained model
 model = load_model("mnist_model.h5")
 
-# Define prediction function
+# Prediction function
 def predict_digit(image):
-    # Resize and normalize
-    image = image.convert('L').resize((28, 28))  # convert to grayscale and resize
+    image = image.convert('L').resize((28, 28))
     img_array = np.array(image).astype("float32") / 255.0
     img_array = img_array.reshape(1, 28, 28)
     
-    # Predict
     prediction = model.predict(img_array)
     predicted_class = np.argmax(prediction)
     confidence = float(np.max(prediction))
     
     return f"Prediction: {predicted_class} (Confidence: {confidence:.2f})"
 
-# Define Gradio Interface
+# Gradio Interface (no shape argument)
 interface = gr.Interface(
     fn=predict_digit,
-    inputs=gr.Image(type="pil", shape=(200, 200), label="Upload a Digit Image"),
+    inputs=gr.Image(type="pil", label="Upload a Digit Image"),
     outputs=gr.Textbox(label="Prediction"),
     title="Handwritten Digit Recognition",
-    description="Upload a handwritten digit image (0–9) to classify it using a neural network trained on the MNIST dataset."
+    description="Upload a handwritten digit image (0–9) to classify it using a model trained on the MNIST dataset."
 )
 
 interface.launch()