From 765821d26b74ce4dc211364cb151296a6baa5e1a Mon Sep 17 00:00:00 2001 From: Ramforth Date: Thu, 30 Oct 2025 17:17:10 +0100 Subject: [PATCH] Update README.md with gRPC implementation plan --- README.md | 71 ++++++++++++++++++++++++++++++++----------------------- 1 file changed, 42 insertions(+), 29 deletions(-) diff --git a/README.md b/README.md index 7093122..4c76092 100644 --- a/README.md +++ b/README.md @@ -1,54 +1,67 @@ -# YouTube Chat Webhook Listener (Version 2) - Research Phase +# YouTube Chat Webhook Listener (Version 2) - gRPC Implementation -This project is currently in a research phase to identify the most robust, quota-friendly, open-source, and Linux-compatible solutions for monitoring YouTube Live Chat. The goal is to gather comprehensive data to inform the development of a long-term solution that avoids the limitations of continuous API polling. +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 conduct deep research into various methods for obtaining YouTube Live Chat data, prioritizing open-source and Linux-compatible solutions, with a focus on minimizing or eliminating YouTube Data API v3 quota consumption for sustained live stream monitoring. The gathered information will be used by a separate AI product to inform future development. +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. -## Deep Research Plan +## Implementation Plan: gRPC Client -The research will focus on the following key areas: +### Phase 1: gRPC Client Setup -### **1. YouTube Data API v3 - Deeper Dive** +1. **Install Dependencies:** + * **Action:** Install `grpcio` and `grpcio-tools` Python packages. -* **Focus:** Exhaustive search for official, quota-friendly API endpoints or features. -* **Key Questions:** Are there any less-documented ways to get live chat data? What are the exact quota costs for *all* relevant live streaming API calls? Are there any official recommendations from YouTube for long-term live chat monitoring? Can quota increases be requested? +2. **Obtain `.proto` File:** + * **Action:** Locate and download the official Protocol Buffers (`.proto`) file that defines the `liveChatMessages.streamList` service. -### **2. Third-Party Webhook/Event-Driven Solutions** +3. **Generate Python Client Code:** + * **Action:** Use `grpc_tools.protoc` to generate the Python client-side libraries from the `.proto` file. -* **Focus:** Identify and analyze third-party services that provide webhook notifications for YouTube Live Chat. -* **Key Questions:** Which platforms offer YouTube Live Chat webhooks? How do they work, what are their limitations, and what are their costs? How do they bypass or manage YouTube's API quota? +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. -### **3. Unofficial/Scraping Methods** +### Phase 2: Integration and Enhancements -* **Focus:** Investigate methods to obtain live chat data outside of the official API. -* **Key Questions:** Are there any known unofficial APIs or methods to scrape live chat data? What are the technical challenges, ethical considerations, and legality (YouTube's ToS)? +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. -### **4. Client-Side/Browser-Based Solutions** +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. -* **Focus:** Explore methods involving browser automation or extensions. -* **Key Questions:** Can headless browsers be used to extract live chat data? Are there existing browser extensions? What are the resource requirements and reliability? +3. **Configuration:** + * **Action:** Externalize API keys, default video ID, and display preferences into a configuration file (e.g., `config.ini` or `config.json`). -### **5. Advanced Quota Management & Optimization** +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). -* **Focus:** Strategies to minimize quota usage within a polling model, if no event-driven solution is found. -* **Key Questions:** What API parameters can reduce quota cost? Can intelligent polling be implemented? Are there any caching strategies? +### 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. -**Prioritization:** All research will prioritize **open-source and Linux-compatible solutions**. - -**Next Steps:** The findings from this research will be compiled into a structured document and utilized by a separate AI product to inform the design and implementation of a robust YouTube Live Chat monitoring solution. +2. **Update Documentation:** + * **Action:** Update the project's `README.md` with detailed usage instructions, setup guides, and explanations of the gRPC implementation. ## Dependencies -* `Flask` (for potential webhook server research) -* `rich` (for terminal display research) -* `ngrok` (for local webhook testing research) +* `grpcio` +* `grpcio-tools` +* `google-auth-oauthlib` +* `google-api-python-client` +* `rich` ## Future Enhancements -* Interactive message sending via webhook (if supported by the third-party service). +* Interactive message sending via gRPC (if supported by the API). * More advanced terminal UI (e.g., `prompt_toolkit` for input). -* Web overlay integration. \ No newline at end of file +* Web overlay integration.