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ShopnoBanerjee / repository
Real-time sentiment analysis tool for YouTube Live Chats, built with a Python FastAPI backend and a custom-trained machine learning model, and a TypeScript-based Chrome extension for the front end.
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This project is a real-time sentiment analysis tool for YouTube Live Chats, built with a Python FastAPI backend and a custom-trained machine learning model, and a TypeScript-based Chrome extension for the front end. It is designed to provide content creators and moderators with instant insights into the sentiment of their live chat audience.
The project is divided into two main components: a server and a chrome_extension.
The server directory contains the backend of the application.
The chrome_extension directory holds the user-facing part of the application.
pip install mlflow)Navigate to the server directory and install the required Python dependencies:
pip install -r requirements.txt
Navigate to the chrome_extension directory and install the Node.js packages:
npm install
After installation, build the extension:
npm run build
This will create a dist directory.
From the server directory, run the FastAPI application:
uvicorn main:app --reload
The server will start at http://127.0.0.1:8000.
chrome://extensions/.dist folder from your chrome_extension directory.The MLflow tracking server is used to manage and serve the machine learning models.
To run the MLflow UI and view your experiments:
mlflow server --host 127.0.0.1 --port 5000
You can then access the UI in your browser at: 👉 http://127.0.0.1:5000
This project is licensed under the MIT License. See the LICENSE file for details.