68 lines
3.5 KiB
Markdown
68 lines
3.5 KiB
Markdown
# YouTube Chat Webhook Listener (Version 2) - gRPC Implementation
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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.
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## Project Goal
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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.
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## Implementation Plan: gRPC Client
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### Phase 1: gRPC Client Setup
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1. **Install Dependencies:**
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* **Action:** Install `grpcio` and `grpcio-tools` Python packages.
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2. **Obtain `.proto` File:**
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* **Action:** Locate and download the official Protocol Buffers (`.proto`) file that defines the `liveChatMessages.streamList` service.
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3. **Generate Python Client Code:**
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* **Action:** Use `grpc_tools.protoc` to generate the Python client-side libraries from the `.proto` file.
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4. **Develop gRPC Client:**
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* **Action:** Write a Python script to:
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* Establish a secure gRPC channel to the YouTube API endpoint.
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* Create a client stub for the `liveChatMessages.streamList` service.
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* Initiate the `StreamList` request to begin receiving messages.
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* Implement a loop to continuously process messages as they are pushed from the server.
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### Phase 2: Integration and Enhancements
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1. **Integrate with Display Logic:**
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* **Action:** Adapt the existing `rich` display logic from `main.py` to consume messages received from the gRPC client instead of the polling mechanism.
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2. **Error Handling & Resilience:**
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* **Action:** Implement robust error handling for gRPC connections, including automatic reconnection logic.
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* **Action:** Utilize `nextPageToken` (if provided by the gRPC stream) to resume receiving messages from where the connection was interrupted, preventing data loss.
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3. **Configuration:**
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* **Action:** Externalize API keys, default video ID, and display preferences into a configuration file (e.g., `config.ini` or `config.json`).
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4. **Enhance Display Features:**
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* **Action:** Implement a more sophisticated system to assign consistent, unique colors to each user.
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* **Action:** Improve emote rendering and potentially integrate with external emote services (e.g., BTTV, FrankerFaceZ) if feasible and compliant.
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* **Action:** Add options for message filtering (e.g., by user, keywords, message type).
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### Phase 3: Testing and Documentation
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1. **Unit and Integration Tests:**
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* **Action:** Write comprehensive unit tests for the gRPC client, message processing, and display logic.
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* **Action:** Develop integration tests to ensure the end-to-end flow works correctly.
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2. **Update Documentation:**
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* **Action:** Update the project's `README.md` with detailed usage instructions, setup guides, and explanations of the gRPC implementation.
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## Dependencies
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* `grpcio`
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* `grpcio-tools`
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* `google-auth-oauthlib`
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* `google-api-python-client`
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* `rich`
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## Future Enhancements
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* Interactive message sending via gRPC (if supported by the API).
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* More advanced terminal UI (e.g., `prompt_toolkit` for input).
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* Web overlay integration.
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