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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.

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📢 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 or GROK_API_KEY configured.

You can configure your API keys using one of the following methods:

Option 1: Passing it via the UI (Recommended)

Paste your API key directly into the designated input field, Enter your API key (Optional) in the user interface.

Option 2: 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 3: Use a .streamlit/secrets.toml File

Create a file named secrets.toml inside the .streamlit (please create the directory if it does not exist) folder with the following content:

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

Note: If no API key is provided, the app will automatically attempt to retrieve it from the environment variables.

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

# Generate a podcast summary using the GroqCloud API key.
python main.py  --input episode.md --output episode_summary_groq.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

🚀 Let's build something great together! 🎙️📄

About

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.

Resources

Stars

9 stars

Watchers

1 watching

Forks

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