Doc2Podcast - Powered by Langflow

Doc2Podcast is an AI-powered tool that transforms PDF documents into engaging podcast-style audio content. This project leverages Next.js, React, and Langflow to generate audio content from text documents.

Features

• PDF document upload
• AI-powered text-to-speech conversion
• Multi-speaker audio generation
• Customizable voice selection
• Interactive audio player with waveform visualization

Getting Started

Prerequisites

• Node.js 14.x or later
• npm or yarn
• A Langflow server running locally
• Git (for cloning the repository)

Installation

1. Clone the repository:

   git clone https://github.com/yourusername/podcast-generator.git
   cd podcast-generator

2. Install dependencies:

   npm install
   # or
   yarn install

3. Set up Langflow:

   • Install and run the Langflow backend server
   • Navigate to the Langflow UI (usually at http://localhost:7860)
   • Import the flow provided in the repo at langflow_flow/Doc to Podcast (Langflow).json
   • If you need to install necessary dependecies for audio generation in Langflow, run the flow at 'langflow_flow/Doc to Podcast (Langflow) - Necessary Installs.json`
   • Note the Flow ID after importing (you'll need this for the .env.local file)

4. Create a .env.local file in the root directory and add the following:

   LANGFLOW_API_URL=http://127.0.0.1:7860
   FLOW_ID=your_flow_id_here
   UPLOAD_FOLDER="uploads"
   GENERATED_AUDIO_FOLDER="generated_audio"
   

   Replace your_flow_id_here with the actual Flow ID from step 3.

5. Run the development server:

   npm run dev
   # or
   yarn dev

6. Open http://localhost:3000 with your browser to see the application.

Usage

1. Navigate to the home page.
2. Upload a PDF document.
3. Select the number of speakers and provide any additional instructions.
4. Wait for the AI to process and generate the audio content.
5. Use the interactive audio player to listen to the generated podcast.

Project Structure

• app/: Contains the main application code
  • api/: API routes for server-side functionality
  • components/: Reusable React components
  • page.tsx: Home page component
• public/: Static assets
• langflow_flow/: Langflow configuration files
• uploads/: Temporary storage for uploaded files
• generated_audio/: Storage for AI-generated audio files

Technologies Used

• Next.js: React framework for building the web application
• React: JavaScript library for building user interfaces
• Langflow: For AI workflow management
• Tailwind CSS: Utility-first CSS framework
• Axios: Promise-based HTTP client
• WaveSurfer.js: Audio visualization library

Configuration

• The project uses environment variables for configuration. Ensure all necessary variables are set in your .env.local file.
• Tailwind CSS configuration can be found in tailwind.config.ts.
• TypeScript configuration is in tsconfig.json.

API Routes

• /api/upload: Handles file upload and podcast generation

Debugging

• Use the browser's developer tools to debug client-side issues.
• For server-side debugging, use console.log statements or attach a debugger to your Node.js process.

Performance Considerations

• Large PDF files may take longer to process. Consider implementing a progress indicator for better user experience.
• Optimize audio file handling for improved performance with larger files.

Contributing

Contributions are welcome! Please feel free to submit a Pull Request. Here are some ways you can contribute:

• Report bugs and issues
• Suggest new features
• Improve documentation
• Submit pull requests with bug fixes or new features

License

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

Support

If you encounter any problems or have questions, please open an issue on the GitHub repository.

Acknowledgements

• Thanks to the Langflow team for providing the AI workflow management tool.
• Special thanks to all contributors who have helped shape this project.