Upload app.py

app.py

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+import json
+import os
+from pathlib import Path
+import cv2
+import gradio as gr
+from fastapi import FastAPI
+from fastapi.responses import HTMLResponse
+from huggingface_hub import hf_hub_download
+from pydantic import BaseModel, Field
+from livekit_token import generate_token
+
+try:
+    from demo.object_detection.inference import YOLOv10
+except (ImportError, ModuleNotFoundError):
+    from inference import YOLOv10
+
+cur_dir = Path(__file__).parent
+
+model_file = hf_hub_download(repo_id="onnx-community/yolov10n", filename="onnx/model.onnx")
+model = YOLOv10(model_file)
+
+def detection(image, conf_threshold=0.3):
+    image = cv2.resize(image, (model.input_width, model.input_height))
+    new_image = model.detect_objects(image, conf_threshold)
+    return cv2.resize(new_image, (500, 500))
+
+app = FastAPI()
+
+LIVEKIT_URL = os.getenv("LIVEKIT_URL")
+LIVEKIT_API_KEY = os.getenv("LIVEKIT_API_KEY")
+LIVEKIT_API_SECRET = os.getenv("LIVEKIT_API_SECRET")
+
+@app.get("/")
+async def _():
+    token = generate_token(LIVEKIT_API_KEY, LIVEKIT_API_SECRET, identity="user123")
+    html_content = open(cur_dir / "index.html").read()
+    html_content = html_content.replace("__LIVEKIT_URL__", LIVEKIT_URL)
+    html_content = html_content.replace("__LIVEKIT_TOKEN__", f'"{token}"')
+    return HTMLResponse(content=html_content)
+
+class InputData(BaseModel):
+    identity: str
+    conf_threshold: float = Field(ge=0, le=1)
+
+@app.post("/input_hook")
+async def _(data: InputData):
+    print(f"Received input for {data.identity} with threshold {data.conf_threshold}")
+    return {"status": "ok"}
+
+if __name__ == "__main__":
+    import uvicorn
+    uvicorn.run(app, host="0.0.0.0", port=7860)