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| import gradio as gr | |
| import json | |
| import base64 | |
| from PIL import Image | |
| import io | |
| import requests | |
| import os | |
| # Get token from environment variable | |
| HF_TOKEN = os.environ.get("HF_CV_ROBOT_TOKEN") | |
| MODEL = "Qwen/Qwen3-VL-32B-Instruct" | |
| # Check if the token is available when the script starts | |
| if not HF_TOKEN: | |
| print("ERROR: HF_CV_ROBOT_TOKEN environment variable not set.") | |
| # In a real app, you might want to stop execution or handle this more gracefully | |
| # For a Gradio app in a Space, it might just fail upon the first request. | |
| # ------------------------------- | |
| # 主處理函數 (Main Processing Function) | |
| # ------------------------------- | |
| def process(payload: dict): | |
| try: | |
| if not HF_TOKEN: | |
| return {"error": "Hugging Face token is missing. Please check Space secrets."} | |
| robot_id = payload.get("robot_id", "unknown") | |
| image_b64 = payload["image_b64"] | |
| # Base64 解碼成圖片，用 PIL 開啟 (Decode base64 to image, open with PIL) | |
| img_bytes = base64.b64decode(image_b64) | |
| # We don't actually use the PIL image object in the rest of the code, | |
| # so this part is technically unnecessary for the API call, but harmless. | |
| # img = Image.open(io.BytesIO(img_bytes)).convert("RGB") | |
| # Router API payload | |
| headers = {"Authorization": f"Bearer {HF_TOKEN}"} | |
| data = { | |
| "model": MODEL, | |
| "messages": [ | |
| { | |
| "role": "user", | |
| "content": [ | |
| {"type": "text", "text": "Describe this image in detail."}, | |
| {"type": "image_data", "image_data": {"b64": image_b64}} | |
| ] | |
| } | |
| ] | |
| } | |
| resp = requests.post( | |
| "https://router.huggingface.co/v1/chat/completions", | |
| headers=headers, | |
| json=data, | |
| timeout=60 | |
| ) | |
| if resp.status_code != 200: | |
| # Added more detail to error logging | |
| print(f"VLM API error: {resp.status_code}, {resp.text}") | |
| return {"error": f"VLM API error: {resp.status_code}, {resp.text}"} | |
| # Check if the expected response structure exists before accessing it | |
| try: | |
| vlm_text = resp.json()["choices"][0]["message"]["content"][0]["text"] | |
| except (KeyError, IndexError, json.JSONDecodeError) as e: | |
| return {"error": f"Failed to parse VLM response: {e}, Response text: {resp.text}"} | |
| return { | |
| "received": True, | |
| "robot_id": robot_id, | |
| "vllm_analysis": vlm_text | |
| } | |
| except Exception as e: | |
| # Added logging for general exceptions | |
| print(f"An unexpected error occurred: {e}") | |
| return {"error": str(e)} | |
| # ------------------------------- | |
| # Gradio MCP Server | |
| # ------------------------------- | |
| demo = gr.Interface( | |
| fn=process, | |
| inputs=gr.JSON(label="Input Payload (Dict format)"), | |
| outputs=gr.JSON(label="Reply to Jetson"), | |
| api_name="predict" | |
| ) | |
| if __name__ == "__main__": | |
| demo.launch(mcp_server=True) | |