Create app.py

app.py

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+import gradio as gr
+import tensorflow as tf
+import numpy as np
+
+# Load the trained model
+model = tf.keras.models.load_model('model.h5')
+print("Model loaded successfully!")
+
+def preprocess_image(image):
+    """Process the input image to match MNIST format"""
+    # Convert to grayscale
+    image = image.convert('L')
+    # Resize to 28x28
+    image = image.resize((28, 28))
+    # Convert to numpy array and normalize
+    image_array = np.array(image)
+    image_array = image_array / 255.0
+    # Reshape to match model input
+    image_array = np.expand_dims(image_array, axis=0)
+    return image_array
+
+def predict_digit(image):
+    if image is None:
+        return None
+    
+    # Preprocess the image
+    processed_image = preprocess_image(image)
+    
+    # Make prediction
+    predictions = model.predict(processed_image)
+    pred_scores = tf.nn.softmax(predictions[0]).numpy()
+    pred_class = np.argmax(pred_scores)
+    
+    # Create result string
+    result = f"Prediction: {pred_class}"
+    
+    return result
+
+# Create Gradio interface
+demo = gr.Interface(
+    fn=predict_digit,
+    inputs=gr.Image(type="pil"),
+    outputs=gr.Textbox(label="Result"),
+    title="MNIST Digit Recognizer",
+    description="Upload a digit from 0-9 and the model will predict which digit it is.",
+    examples=None,
+)
+
+if __name__ == "__main__":
+    demo.launch()