The Technical Architecture: Bridging React and Python
One of the biggest hurdles in this project was managing the data flow. Python is the king of Computer Vision, but React is the king of User Experience. To make them talk to each other efficiently, I built a decoupled system.

1. The Frontend (The Collector)
   Using React.js, I implemented a custom hook to handle the MediaDevices API. The challenge wasn't just showing the video; it was capturing frames at a consistent interval and shipping them to the backend without blocking the UI thread.

2. The Backend (The Processor)
   The heart of the app is a Python server. I chose Python specifically to leverage the open-rppg library and OpenCV.

Processing Pipeline: The backend receives the frame, applies skin-tone detection, and calculates the remote photoplethysmography (rPPG) signal.

API Design: I built a lightweight RESTful interface (or WebSocket for real-time) to handle the inference results.

1. The Deployment (The Reality Check) This is where the engineering got "scrappy." While deploying the backend on Render, I hit a 512MB memory limit—common when running heavy Python libraries.

The Solution: I containerized the backend using Docker. This allowed me to optimize the environment, prune unnecessary dependencies, and manage memory usage more deterministically.

# A snippet showing how we handle the incoming frame
def process_vital_signals(frame_data):
    # Decode base64 frame from React frontend
    frame = decode_base64_to_cv2(frame_data)

    # Run rPPG inference
    vitals = rppg_engine.estimate_heart_rate(frame)

    return {
        "bpm": vitals.bpm,
        "confidence": vitals.score,
        "timestamp": time.time()
    }