I dont have money to deploy this, so might be deployed later. Main core functionality is implemeneted at this moment.

Superchat Tracking System

This project is a Dockerized Flask-based web application for tracking YouTube live chat superchats, with data stored in a PostgreSQL database. The application allows you to track live chat messages, aggregate superchat amounts by author, and send the data as a CSV file via email.

Features

• Livechat Tracking: Automatically tracks live chat messages from a YouTube live stream.
• Superchat Aggregation: Calculates the total superchat amount for each user.
• Database Storage: Stores live chat data and aggregated results in a PostgreSQL database.
• CSV Export and Email: Generates a CSV summary of the tracked data and emails it as an attachment.
• Web Interface: Simple Flask web interface to interact with the database and view results.

Prerequisites

• Docker
• Docker Compose
• Python 3.x
• PostgreSQL

Getting Started

1. Clone the repository

git clone https://github.com/your-repo/superchat-tracker.git
cd superchat-tracker

2. Set up an environment variable

Create a .env file in the project root directory with the following variables:

FLASK_APP=server.py
FLASK_ENV=development
FLASK_RUN_PORT=8000

API_KEY='YOUR_YOUTUBE_API_KEY'
API_URL='https://www.googleapis.com/youtube/v3/videos'
HUB_URL=https://pubsubhubbub.appspot.com/subscribe
TOPIC_URL='https://www.youtube.com/xml/feeds/videos.xml?channel_id=YOUR_FAVORITE_CHANNEL_ID'

NGROK_AUTHTOKEN=YOUR_NGROK_AUTHTOKEN
APP_PASSWORD="YOUR_EMAIL_APP_PASSWORD"
SENDER_EMAIL="YOUR_SENDER_EMAIL"
RECEIVER_EMAIL="YOUR_RECEIVER_EMAIL"

DATABASE=your_database_name
USER=your_database_user
PASSWORD=your_database_password
HOST=your_database_host
PORT=your_database_port

3. Build and run docker

docker-compose up --build

4. Access to the service

Once the containers are running, you can access the Flask application at: http://localhost:8000

Usage

API Endpoints

• GET /: Initialize the database and summarize data.
• POST /create: Create the necessary database tables.
• DELETE /drop: Drop the existing database tables.
• POST /add: Insert sample data into the database.
• GET /view: View the contents of the livechat_data and author_total tables.
• POST /send_mail: Send an email with the CSV file attached.
• POST /youtube-callback: Handle YouTube PubSubHubbub notifications for live chat tracking.

CSV Export

The system generates a CSV file containing the live chat data and sends it via email to the specified recipient.

Development

Running Locally

To run the application locally without Docker, ensure you have Python and PostgreSQL installed, then follow these steps:

1. Create a virtual environment and install dependencies:

   python3 -m venv venv
   source venv/bin/activate
   pip install -r requirements.txt

2. Set up the PostgreSQL database and configure environment variables in the .env file.

3. Start the Flask application:

   flask run
   or
   python server.py

Database Setup

The database is initialized using SQL scripts located in the ./db/queries/ directory. The primary tables include:

• livechat_data: Stores live chat messages with author names and superchat amounts.
• author_total: Stores the aggregated superchat amounts for each author.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments

• Flask
• PostgreSQL
• pytchat (YouTube Live Chat Library)
• Docker