PagePod: Multi-Agent Web content Podcast generator

This project uses a multi-agent framework to automatically generate podcasts from website content. It leverages the power of AI to scrape web content, refine it, create a script, and convert it to speech.

Features

• Web scraping of content
• Content refinement and validation
• Automatic podcast script generation
• Script validation and improvement
• Text-to-speech conversion
• Multi-agent system using CrewAI

Feautres to contribute

• More Sophisticated Web Content Scrapping using Vision and OCR content Extraction
• UI/Interface
• Support to more LLM integrations and TTS models
• Multi-Turn Podcast generation with multiple people
• Addition of Fallback scrapping tools like Firecrawl , Craw4AI and more

Prerequisites

• Python 3.7+
• OpenAI API key or Groq API key
• Deepgram API key

Installation

1. Clone the repository:

   git clone https://github.com/AjayK47/PagePod.git
   cd PagePod
   

2. Install the required packages:

   pip install -r requirements.txt
   

3. Set up environment variables: Create a .env file in the project root and add your API keys:

   OPENAI_API_KEY=your_openai_api_key
   DG_API_KEY=your_deepgram_api_key
   

Usage

Run the main script:

python main.py

You will be prompted to enter a URL. The system will then:

1. Scrape the content from the provided URL
2. Refine and validate the content
3. Generate a podcast script
4. Convert the script to speech
5. Save the resulting audio file

Project Structure

• main.py: Entry point of the application
• agents.py: Defines the AI agents used in the process
• tasks.py: Defines the tasks for each agent
• tools/tts.py: Contains the text-to-speech tool
• requirements.txt: Lists all Python dependencies

Customization

• You can customize the behavior of the agents by modifying their roles, goals, and backstories in the agents.py file. You can also adjust the task descriptions in tasks.py to fine-tune the process.
• Best Part you change your LLM model as well in agents.py file

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

Acknowledgments

• CrewAI for the multi-agent framework
• OpenAI for the language model
• Deepgram for the text-to-speech API