📢 Podcast Summaries Project

🚀 Overview

This project aims to generate structured PDF reports from podcast interviews, highlighting key takeaways, quotes, and insights. The goal is to create shareable and accessible summaries for a broader audience.

🔹 Features (Planned)

• Summarization using LLMs
• Search & Retrieval
• PDF Report Generation
• Web UI (Streamlit) for user interaction

📌 Getting Started

1️⃣ Setup the Project

1. Clone the repository:

   git clone https://github.com/DataTalksClub/podcast-summary-generation.git
   cd podcast-summaries

2. Install dependencies:

   pip install -r requirements.txt

Before running the application, ensure you have your OPEN_API_KEY and GROK_API_KEY configured.

You can do this using either of the following methods:

Option 1: Use a .env File

Create a .env file in the root of your project with the following content:

OPEN_API_KEY = sample-value-here
GROK_API_KEY = sample-value-here

Option 2: Use a secrets.toml File

Create a file named secrets.toml inside the .streamlit folder with the following content:

OPEN_API_KEY = sample-value-here
GROK_API_KEY = sample-value-here

2️⃣ Run the Project (Development Mode)

To run the application, do the following:

# Start the backend services (if needed)
docker-compose up -d

# Generate a podcast summary using the OpenAI API key.
 python main_openai.py  --input episode.md --output episode_summary_openai.md

# Run the Streamlit application
python run_streamlit_app.py

🔄 Workflow Pipeline

1. LLM Processing → Summarization, Extracting Key Insights
2. Storage & Retrieval → Search Engine (ElasticSearch/In-memory DB)
3. PDF Generation → Formatted Report
4. Web UI → User Interaction & Downloads

🏗️ Contribution Guidelines

• Open an issue before working on any feature.
• Use feature branches for development.
• Submit PRs with at least 2 approvals before merging.

📚 Resources

• Project Documentation
• GitHub Issues

🚀 Let's build something great together! 🎙️📄