app.py

import gradio as gr
from transformers import pipeline
from PIL import Image
import torch

# Load your model
device = 0 if torch.cuda.is_available() else -1
pipe = pipeline("image-classification", model="beingamit99/car_damage_detection", device=device)

def predict_damage(image):
    if image.mode != "RGB":
        image = image.convert("RGB")
    results = pipe(image)
    return results

# Create the Gradio interface
iface = gr.Interface(
    fn=predict_damage,
    inputs=gr.Image(type="pil"),
    outputs=gr.JSON(),
    title="Car Damage Detection API",
    description="Upload an image of a car to detect damages."
)

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