https://github.com/darkdk123/ai-youtube-assistant
YouTube Assitant to summarize videos & Question-answering using LangChain and an LLM.
https://github.com/darkdk123/ai-youtube-assistant
hugging-face langchain llm python streamlit
Last synced: about 2 months ago
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YouTube Assitant to summarize videos & Question-answering using LangChain and an LLM.
- Host: GitHub
- URL: https://github.com/darkdk123/ai-youtube-assistant
- Owner: DarkDk123
- Created: 2024-09-04T08:31:14.000Z (almost 2 years ago)
- Default Branch: main
- Last Pushed: 2024-09-05T19:05:52.000Z (almost 2 years ago)
- Last Synced: 2025-03-28T07:13:18.468Z (over 1 year ago)
- Topics: hugging-face, langchain, llm, python, streamlit
- Language: Python
- Homepage:
- Size: 7.21 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: Readme.md
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README
# AI-YouTube-Assistant 🤖
**AI-YouTube-Assistant** is a ***LangChain*** 🦜🔗 application that helps to answer queries & summarize `YouTube Videos` to understand their crucial aspects in less time!
- **Question answering about YouTube videos 🤔**
- **Summarize any (almost) YouTube Video & Save time! ⏳**
## Table of Contents
- [AI-YouTube-Assistant 🤖](#ai-youtube-assistant-)
- [Table of Contents](#table-of-contents)
- [Introduction](#introduction)
- [Features](#features)
- [Getting Started](#getting-started)
- [Usage](#usage)
- [Credit \& Contributing](#credit--contributing)
## Introduction
This project aims to provide a simple and efficient way to answer questions and summarize YouTube videos using LangChain.
It also provides a beautiful UI with Streamlit.
## Features
- Question answering about YouTube videos
- Summarize any (almost) YouTube Video
- Save time by quickly understanding video content
## Getting Started
To get started, follow these steps:
1. Clone this repository: `https://github.com/DarkDk123/AI-YouTube-Assistant`
2. Install the required dependencies: `pip install -r requirements.txt`
3. Store environment variables in .env file like [example.env](./example.env)
4. Run the **Streamlit** application: `streamlit run main_streamlit.py`
## Usage
Once the application is running, you can interact with it by entering your queries on the web interface, default at - http://localhost:8501
## Credit & Contributing
Inspired by learnings from LangChain [Tutorial](https://www.youtube.com/watch?v=lG7Uxts9SXs&t=22s) by [Rishab Kumar](https://github.com/rishabkumar7).
Contributions are welcome! If you have any ideas or improvements, feel free to open an issue or submit a pull request.