Update app.py

app.py

@@ -1,52 +1,44 @@
 import gradio as gr
 import numpy as np 
-from PIL import Image, ImageOps
 import tensorflow as tf
 
 # Load the model 
 model = tf.keras.models.load_model("cnn_model.h5")
 
 
-def predict(image_array):
-    try:
-        # Handle dictionary input format from newer Gradio versions
-        if isinstance(image_array, dict) and 'composite' in image_array:
-            # Extract the image data from the 'composite' key
-            image_array = image_array['composite']
-        
-        if image_array is None or np.sum(image_array) == 0:
-            return "Please draw a digit."
-            
-        # Process the image for EMNIST format
-        image = Image.fromarray(image_array.astype("uint8"), mode="L")
-        image = image.resize((28, 28), Image.LANCZOS)
-        image = ImageOps.invert(image)  # Invert colors
-        
-        # Convert to model input format
-        image_array = np.array(image).astype("float32") / 255.0
-        image_array = image_array.reshape(1, 28, 28, 1)
-        
-        # Make prediction
-        logits = model.predict(image_array, verbose=0)
-        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)}"
+def predict(input_dict):
+    if not input_dict or "composite" not in input_dict:
+        return {str(i): 0.0 for i in range(10)}
+
+    image = input_dict["composite"]
+
+    # Remove alpha channel (RGBA → RGB)
+    image = image[:, :, :3]
+
+    # Convert to grayscale
+    image = tf.image.rgb_to_grayscale(image)
+
+    # Resize to 28x28
+    image = tf.image.resize(image, [28, 28])
+
+    # Invert colors and normalize
+    image = 255 - image
+    image = image / 255.0
+    image = image.numpy().reshape(-1, 28, 28, 1)
+
+    # Predict
+    prediction = model_cnn.predict(image)
+    probs = tf.nn.softmax(prediction[0]).numpy()
+    return {str(i): float(probs[i]) for i in range(10)}
 
 # Create the Gradio interface with appropriate settings
-interface = gr.Interface(
+sketchpad = gr.Sketchpad()  # <- clean and version-safe
+label = gr.Label(num_top_classes=3)
+
+gr.Interface(
     fn=predict,
-    inputs=gr.Sketchpad(
-        image_mode="L", 
-        canvas_size=(280, 280),
-        type="numpy",
-        brush=gr.Brush()  # Using default brush settings
-    ),
-    outputs="text",
-    title="EMNIST Digit Classifier",
-    description="Draw a digit (0-9) in the center of the canvas. Make it large and clear for best results."
-)
-
-interface.launch()
+    inputs=sketchpad,
+    outputs=label,
+    title="MNIST Digit Sketch Pad",
+    description="Draw a digit (0–9) and this model will try to guess what number you wrote! It’s trained on the original MNIST dataset of handwritten digits.",
+).launch(debug = False)