YouTube Chat Webhook Listener (Version 2) - gRPC Implementation

This project aims to create a robust, quota-friendly, open-source, and Linux-compatible solution for monitoring YouTube Live Chat by implementing the recommended liveChatMessages.streamList gRPC endpoint. This approach will provide an event-driven, server-push model for receiving live chat messages, effectively eliminating the limitations and quota consumption associated with continuous API polling.

Project Goal

To build a Python application that leverages the liveChatMessages.streamList gRPC endpoint to receive real-time YouTube Live Chat messages, process them, and display them in the terminal with rich formatting. This will ensure a highly efficient and compliant method for sustained live stream monitoring.

Implementation Plan: gRPC Client

Phase 1: gRPC Client Setup

1. Install Dependencies:

   • Action: Install grpcio and grpcio-tools Python packages.

2. Obtain .proto File:

   • Action: Locate and download the official Protocol Buffers (.proto) file that defines the liveChatMessages.streamList service.

3. Generate Python Client Code:

   • Action: Use grpc_tools.protoc to generate the Python client-side libraries from the .proto file.

4. Develop gRPC Client:

   • Action: Write a Python script to:
     • Establish a secure gRPC channel to the YouTube API endpoint.
     • Create a client stub for the liveChatMessages.streamList service.
     • Initiate the StreamList request to begin receiving messages.
     • Implement a loop to continuously process messages as they are pushed from the server.

Phase 2: Integration and Enhancements

1. Integrate with Display Logic:

   • Action: Adapt the existing rich display logic from main.py to consume messages received from the gRPC client instead of the polling mechanism.

2. Error Handling & Resilience:

   • Action: Implement robust error handling for gRPC connections, including automatic reconnection logic.
   • Action: Utilize nextPageToken (if provided by the gRPC stream) to resume receiving messages from where the connection was interrupted, preventing data loss.

3. Configuration:

   • Action: Externalize API keys, default video ID, and display preferences into a configuration file (e.g., config.ini or config.json).

4. Enhance Display Features:

   • Action: Implement a more sophisticated system to assign consistent, unique colors to each user.
   • Action: Improve emote rendering and potentially integrate with external emote services (e.g., BTTV, FrankerFaceZ) if feasible and compliant.
   • Action: Add options for message filtering (e.g., by user, keywords, message type).

Phase 3: Testing and Documentation

1. Unit and Integration Tests:

   • Action: Write comprehensive unit tests for the gRPC client, message processing, and display logic.
   • Action: Develop integration tests to ensure the end-to-end flow works correctly.

2. Update Documentation:

   • Action: Update the project's README.md with detailed usage instructions, setup guides, and explanations of the gRPC implementation.

Dependencies

• grpcio
• grpcio-tools
• google-auth-oauthlib
• google-api-python-client
• rich

Future Enhancements

• Interactive message sending via gRPC (if supported by the API).
• More advanced terminal UI (e.g., prompt_toolkit for input).
• Web overlay integration.