app.py

import gradio as gr
import requests
from PIL import Image

def load_model(model_name):
    # Use the Hugging Face API to load the model
    api_url = f"https://huggingface.co/{model_name}"
    response = requests.get(api_url)
    if response.status_code == 200:
        # Assume the model is an image processing model
        return lambda image: image  # Replace with actual model processing code
    else:
        raise ValueError("Model not found")

# Load the model once during initialization
model = load_model("amjadfqs/finalProject")

def predict(image):
    # Use the model to make a prediction
    return model(image)

# Set up the Gradio interface
image_cp = gr.Image(type="pil", label='Brain')
interface = gr.Interface(fn=predict, inputs=image_cp, outputs="text")
interface.launch()