Add application file

app.py

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+import torch
+import json
+import gradio as gr
+
+# Load YOLOv5s pretrained on COCO
+model = torch.hub.load('ultralytics/yolov5', 'yolov5s')
+
+def detect(image):
+    results = model(image)
+    df = results.pandas().xyxy[0]  # xmin, ymin, xmax, ymax, confidence, class, name
+    return json.loads(df.to_json(orient="records"))
+
+iface = gr.Interface(
+    fn=detect,
+    inputs=gr.Image(type="pil"),
+    outputs="json",
+    title="COCO Object Detection (YOLOv5)",
+    description="Upload an image to detect objects (COCO classes). Output is JSON."
+)
+
+iface.launch()

readme.md

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+# COCO Object Detection API (Free on Hugging Face Spaces)
+
+Deploys a YOLOv5s model pretrained on MS COCO.
+
+## Features
+- Upload image via web UI or API
+- Returns JSON (bounding boxes, class, confidence)
+
+## Example API Call
+```bash
+curl -X POST \
+  -F "data=@your_image.jpg" \
+  https://YOUR_SPACE_URL/run/predict

requirements.txt

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+torch
+torchvision
+pillow
+gradio