app.py

import os
import requests
import tensorflow as tf
import gradio as gr
import numpy as np

MODEL_URL = "https://drive.google.com/uc?export=download&id=1ECjloRVUkgnKACeBZA06UU_-JddZQ5Z5"
MODEL_PATH = "my_mnist_model.keras"

def download_file_from_google_drive(url, destination):
    if not os.path.exists(destination):
        print("Downloading model from Google Drive...")
        response = requests.get(url, stream=True)
        with open(destination, "wb") as f:
            for chunk in response.iter_content(chunk_size=8192):
                if chunk:
                    f.write(chunk)
        print("Download complete.")

download_file_from_google_drive(MODEL_URL, MODEL_PATH)

model = tf.keras.models.load_model(MODEL_PATH)

def predict_digit(image):
    # If the input is a file path, open and process the image
    if isinstance(image, str):
        from PIL import Image
        image = Image.open(image).convert('L')
        image = np.array(image)
    if image.ndim == 3:
        image = image[..., 0]
    image = np.array(image).astype("float32")
    image = image.reshape(1, 28, 28)
    image = image / 255.0
    prediction = model.predict(image)
    return str(np.argmax(prediction))

iface = gr.Interface(
    fn=predict_digit,
    inputs=gr.Image(image_mode='L', type='filepath', height=28, width=28),
    outputs=gr.Label(num_top_classes=1),
    title="MNIST Digit Classifier",
    description="Upload or draw a digit (0-9) and the model will predict it."
)

if __name__ == "__main__":
    iface.launch()