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import gradio as gr
import tensorflow as tf
import numpy as np
from PIL import Image
# =========================
# Load trained model
# =========================
# Make sure you've trained and saved it as best_model.h5 in your notebook
model = tf.keras.models.load_model("mnist_model.h5")
# =========================
# Prediction function
# =========================
def predict(image):
"""
Takes a PIL image, preprocesses it (grayscale + resize),
runs prediction using trained model, and returns predicted digit.
"""
# Convert to grayscale + resize
image = image.convert("L").resize((28, 28))
# Convert to numpy and normalize
img_array = np.array(image) / 255.0
img_array = img_array.reshape(1, 28, 28, 1) # batch shape
# Predict
prediction = model.predict(img_array)
predicted_class = np.argmax(prediction, axis=1)[0]
# Also return top-3 predictions with probabilities
top3_indices = prediction[0].argsort()[-3:][::-1]
top3_probs = prediction[0][top3_indices]
result = {str(d): float(p) for d, p in zip(top3_indices, top3_probs)}
return result
# =========================
# Gradio interface
# =========================
iface = gr.Interface(
fn=predict,
inputs=gr.Image(type="pil", image_mode="L"),
outputs=gr.Label(num_top_classes=3), # show top 3 predictions
title="MNIST Digit Classifier",
description="Upload a handwritten digit (0–9) image. The model will predict the digit."
)
if __name__ == "__main__":
iface.launch()
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