app.py

import os
import base64
import time
import io
import gradio as gr
from gradio_client import Client

# Load environment variables
ROBOT_ID = os.environ.get("ROBOT_ID")
HF_TOKEN = os.environ.get("HF_CV_ROBOT_TOKEN")
HF_SPACE = "OppaAI/Robot_MCP_Server"
API_NAME = "/predict"


def process_webcam_stream(image):
    """Send webcam image to HF MCP Server and get result"""
    if image is None:
        return "", "", "", ""

    # Convert Image to base64
    buffered = io.BytesIO()
    image.save(buffered, format="JPEG")
    b64_img = base64.b64encode(buffered.getvalue()).decode("utf-8")

    # Prepare payload
    payload = {
        "image_b64": b64_img,
        "robot_id": ROBOT_ID,
        "timestamp": time.time(),
        "hf_token": HF_TOKEN
    }

    # Send to HF Space using streaming-friendly predict
    client = Client(HF_SPACE)
    try:
        resp = client.predict(payload, api_name=API_NAME)
        objects_list = resp.get("objects", [])
        objects_str = ", ".join(objects_list) if isinstance(objects_list, list) else str(objects_list)

        return (
            resp.get("description", ""),
            resp.get("human", ""),
            objects_str,
            resp.get("environment", "")
        )
    except Exception as e:
        return f"Error: {e}", "", "", ""


with gr.Blocks() as demo:
    gr.Markdown("## 🎥 Robot Vision Webcam Stream")

    with gr.Row():
        webcam_input = gr.Image(
            label="Captured from Web-Cam",
            sources=["upload", "webcam"],
            type="pil"
        )
        description_out = gr.Textbox(label="Description")
        human_out = gr.Textbox(label="Human")
        objects_out = gr.Textbox(label="Objects")
        environment_out = gr.Textbox(label="Environment")

    webcam_input.stream(
        process_webcam_stream,
        inputs=[webcam_input],
        outputs=[description_out, human_out, objects_out, environment_out],
        stream_every=0.5
    )

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