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https://github.com/emirhansilsupur/youtube-video-analyzer
YouTube Video Inspector is a multi-agent tool that analyzes YouTube videos by fetching metadata, analyzing comments, and generating performance reports, which can be exported as PDFs using CrewAI.
https://github.com/emirhansilsupur/youtube-video-analyzer
crewai llm multi-agent-systems streamlit youtube
Last synced: 3 months ago
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YouTube Video Inspector is a multi-agent tool that analyzes YouTube videos by fetching metadata, analyzing comments, and generating performance reports, which can be exported as PDFs using CrewAI.
- Host: GitHub
- URL: https://github.com/emirhansilsupur/youtube-video-analyzer
- Owner: emirhansilsupur
- License: mit
- Created: 2024-08-27T14:33:51.000Z (5 months ago)
- Default Branch: main
- Last Pushed: 2024-09-26T12:56:32.000Z (4 months ago)
- Last Synced: 2024-10-11T00:43:41.929Z (4 months ago)
- Topics: crewai, llm, multi-agent-systems, streamlit, youtube
- Language: Python
- Homepage:
- Size: 11.1 MB
- Stars: 2
- Watchers: 2
- Forks: 2
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# YouTube Video Inspector
YouTube Video Inspector is a multi-agent-based project that provides basic analysis of YouTube videos. It fetches video statistics, analyzes comments, generates detailed reports, and converts them to PDF format.
https://github.com/user-attachments/assets/bb9aeb3c-1166-4b66-97ad-aa86bd3dcec3
## Demo
You can try the YouTube Video Inspector application [here](https://huggingface.co/spaces/emirhansilsupur/Youtube-Video-Inspector).
1. Enter a YouTube video URL in the provided input field.
2. Select an LLM from the dropdown menu.
3. Click "Analyze Video" to start the analysis process.
4. The app will fetch video details, analyze comments, and generate a basic report.
5. Once complete, you can view the analysis results and download the [PDF report](output/Youtube_Video_Analysis_Report.pdf).
## Features
- Fetch and analyze YouTube video metadata
- Analyze video comments and viewer sentiment
- Generate detailed reports on video performance and audience engagement
- Convert reports to PDF format
- **Supports multiple LLMs**:
- Llama 3.2 90B Text Preview
- Llama 3.1 70B Versatile
- Llama 3 70B
- Mixtral 8x7B
## Installation
#### Using Docker
The easiest way to run YouTube Video Inspector is using Docker. Pull the latest image from Docker Hub:
```bash
docker pull emirhnslspr/youtube-video-analyzer:v0.1
```
#### Manual Installation
1. Clone the Repository:
```bash
git clone https://github.com/emirhansilsupur/youtube-video-analyzer.git
```
2. Install Poetry:
```bash
pip install poetry
```
3. Install dependencies:
```bash
poetry install
```
3. Run the Streamlit app:
```bash
streamlit run app.py
```
## Configuration
Create a .env file in the project root and add your API keys:
```
GROQ_API_KEY=your_groq_api_key
YOUTUBE_API_KEY=your_youtube_api_key
```
**Note :** You will need to replace **your_groq_api_key** and **your_youtube_api_key** with your own API keys. You can obtain these keys by signing up for the respective services:
- Groq API Key: Sign up for a [Groq](https://console.groq.com/keys) account and generate an API key.
- YouTube API Key: Follow the [YouTube Data API v3](https://developers.google.com/youtube/v3/getting-started) documentation to create a project and obtain an API key.
## Usage
```bash
docker run -p 8501:8501 -v $(pwd)/output:/app/output --env-file .env emirhnslspr/youtube-video-analyzer:v0.1
```
## Workflow
[Click for the interactive flowchart version.](https://miro.com/app/board/uXjVKjutOC8=/?share_link_id=151983055691)
## License
This project is licensed under the MIT License - see the [LICENSE](./LICENSE) file for details.