Create app.py

app.py

@@ -0,0 +1,27 @@
+import pickle
+import numpy as np
+from PIL import Image
+import gradio as gr
+
+# 1) Load your pretrained model
+model = pickle.load(open("mnist_model.pkl", "rb"))
+
+# 2) Define a prediction function
+def classify_digit(img):
+    # convert to grayscale 28×28
+    img = img.convert("L").resize((28, 28))
+    arr = np.array(img).reshape(1, -1)
+    pred = model.predict(arr)[0]
+    return f"Predicted digit: {pred}"
+
+# 3) Wire up Gradio
+iface = gr.Interface(
+    fn=classify_digit,
+    inputs=gr.Image(type="pil", label="Upload a 28×28 digit"),
+    outputs=gr.Textbox(label="Prediction"),
+    title="MNIST Digit Classifier",
+    description="Upload a handwritten digit and get a prediction!"
+)
+
+if __name__ == "__main__":
+    iface.launch()