Added application file

app.py

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+import gradio as gr
+import pickle
+import numpy as np
+
+# Load your model
+with open('your_mnist_model.pkl', 'rb') as f:
+    model = pickle.load(f)
+
+def predict(image):
+    # Preprocess the image
+    image = image.reshape(1, 28, 28) / 255.0  # Normalize to 0-1
+    
+    # Make prediction
+    probabilities = model.predict_proba(image.reshape(1, -1))[0]
+    
+    # Format output
+    return {str(i): float(prob) for i, prob in enumerate(probabilities)}
+
+iface = gr.Interface(
+    fn=predict,
+    inputs=gr.Sketchpad(shape=(28, 28)),
+    outputs=gr.Label(num_top_classes=10),
+    title="MNIST Digit Classifier",
+    description="Draw a digit (0-9) in the canvas and the model will predict it."
+)
+
+iface.launch()

hindimnist.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:938ad51e94acf60cee5ddf39d228b6ac9a88cc6f441d0fe1c10b9f2349f0451f
+size 1690954

mnist_cnn.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:0d3706230da2426c2311152abe6d3cd5db973f229af51efc1a3b98bc4c028c31
+size 1689752