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https://github.com/lhiebert01/geminiyoutubeapp

A powerful YouTube video analysis tool that leverages Google's Gemini AI to provide comprehensive insights, summaries, and interactive Q&A for any YouTube video with available transcripts.
https://github.com/lhiebert01/geminiyoutubeapp

gemini-ai gemini-api gemini-flash gemini-flash-ai gemini-pro geminiapi google-youtube google-youtube-api lindsay-hiebert youtube-analytics youtube-analytics-api youtube-analytics-dashboard

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A powerful YouTube video analysis tool that leverages Google's Gemini AI to provide comprehensive insights, summaries, and interactive Q&A for any YouTube video with available transcripts.

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README 

        
# Gemini Flash YouTube Video Insights Pro



🎥 ⚡ 🤖 A powerful YouTube video analysis tool that leverages Google's Gemini AI to provide comprehensive insights, summaries, and interactive Q&A for any YouTube video with available transcripts.



[![Streamlit App](https://static.streamlit.io/badges/streamlit_badge_black_white.svg)](https://geminiyoutubeapp.streamlit.app)



## Features



- **Comprehensive Video Analysis**: Generate detailed summaries, key points, and insights from YouTube videos

- **Multiple Analysis Types**:

  - Summary & Key Points

  - Title Suggestions

  - Quotes with Timestamps

  - Key Terms & Definitions

  - Full Analysis (all of the above)

- **Interactive Q&A**: Ask questions about the video content and get AI-powered responses

- **Export Options**: Download analysis results in both Word and Text formats

- **Full Transcript Access**: View and download complete video transcripts

- **Large Context Window**: Utilizes Gemini's 1M token input capacity for comprehensive analysis



## Installation



1. Clone the repository:

```bash

git clone https://github.com/lhiebert01/GeminiYouTubeApp.git

cd GeminiYouTubeApp

```



2. Create and activate a virtual environment:

```bash

python -m venv venv

source venv/bin/activate  # On Windows: venv\Scripts\activate

```



3. Install dependencies:

```bash

pip install -r requirements.txt

```



## Configuration



1. Create a `.env` file in the project root with your API keys:

```env

GEMINI_API_KEY=your_gemini_api_key

YOUTUBE_API_KEY=your_youtube_api_key

```



2. For Streamlit deployment, configure secrets in your Streamlit dashboard:

   - Navigate to your app settings

   - Add the above environment variables in the Secrets management section



## API Keys Setup



1. **Gemini API Key**:

   - Visit [Google AI Studio](https://makersuite.google.com/app/apikey)

   - Create a new API key

   - Copy the key to your `.env` file or Streamlit secrets



2. **YouTube API Key**:

   - Go to [Google Cloud Console](https://console.cloud.google.com/)

   - Create a new project or select an existing one

   - Enable the YouTube Data API v3

   - Create credentials (API key)

   - Copy the key to your `.env` file or Streamlit secrets



## Usage



1. Run the application locally:

```bash

streamlit run app.py

```



2. Enter a YouTube URL in the sidebar

3. Select your desired analysis type

4. Click "Run Analysis" to generate insights

5. Use the Q&A interface to ask specific questions about the video

6. Download results in your preferred format



## Features in Detail



### Analysis Types



1. **Summary & Key Points**

   - Comprehensive summary (2-3 paragraphs)

   - Key points in table format



2. **Title Suggestions**

   - 3-5 alternative titles with explanations

   - Based on video content and themes



3. **Quotes with Timestamps**

   - 5-10 significant quotes

   - Context and significance explanations



4. **Key Terms & Definitions**

   - Important concepts and jargon

   - Clear, organized definitions



5. **All Analysis**

   - Complete analysis including all above features



### Export Options



- **Word Document**: Complete analysis with formatting

- **Text File**: Plain text version of analysis

- **Transcript**: Raw video transcript



## Development



- Built with Streamlit and Google's Gemini AI

- Uses YouTube Data API v3 for video information

- Implements smart text truncation for long videos

- Maintains session state for seamless user experience



## Deployment



The application is deployed on Streamlit Cloud. For deployment:



1. Push your changes to GitHub

2. Connect your repository to Streamlit Cloud

3. Configure your environment variables in Streamlit Cloud

4. Deploy!



## Security Notes



- Never commit `.env` file or secrets to the repository

- Use environment variables for all sensitive information

- Implement rate limiting and error handling for API calls



## Contributing



1. Fork the repository

2. Create a feature branch

3. Commit your changes

4. Push to the branch

5. Create a Pull Request



## License



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



## Documentation



- [Architecture Overview](docs/ARCHITECTURE.md) - Detailed application structure and flow

- [Technical Details](docs/TECHNICAL.md) - In-depth technical implementation details



For a complete understanding of the application architecture and flow, please see the [Architecture Documentation](docs/ARCHITECTURE.md).



## Credits



Developed by [Lindsay Hiebert](https://www.linkedin.com/in/lindsayhiebert/) using Google's Gemini AI and Streamlit.



## Contact



For questions or collaboration, please reach out through [LinkedIn](https://www.linkedin.com/in/lindsayhiebert/).