app.py

import gradio as gr
import torch
from PIL import Image

# Load a pretrained model (YOLOv5 for example)
model = torch.hub.load("ultralytics/yolov5", "yolov5s", pretrained=True)

def detect_objects(image):
    results = model(image)
    detections = results.pandas().xyxy[0].to_dict(orient="records")
    return detections  # JSON-friendly list of dicts

# Gradio interface
iface = gr.Interface(
    fn=detect_objects,
    inputs=gr.Image(type="pil"),
    outputs=gr.JSON(),
    examples=[]
)

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