app.py

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()