✍️ AI Blog Generator

A production-ready AI application that generates SEO-optimized blog posts in multiple languages (English, Hindi, French) using LangGraph agents and Groq's LLaMA 3 models.

This project features a decoupled architecture with a FastAPI backend for agentic workflow orchestration and a Streamlit frontend for user interaction.

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🚀 Features

• Multi-Language Support: Generate content in English, Hindi, or French.
• Agentic Workflow: Uses LangGraph to orchestrate stateful AI agents.
• High-Performance LLM: Powered by Groq (LLaMA 3) for ultra-fast inference.
• Decoupled Architecture:
  • Backend: FastAPI service running LangGraph workflows.
  • Frontend: Streamlit UI for easy topic input and markdown rendering.
• Dockerized: Fully containerized deployment for platforms like Hugging Face Spaces.

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🛠️ Tech Stack

• Orchestration: LangGraph & LangChain
• LLM Inference: Groq API (LLaMA 3)
• Backend: FastAPI, Uvicorn
• Frontend: Streamlit
• Language: Python 3.9+

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⚙️ Setup & Installation

1. Clone the Repository

git clone https://github.com/Abhishek2634/AgenticAi_Blog_Generator.git
cd AgenticAi_Blog_Generator

2. Environment Variables

Create a .env file in the root directory with at least:

GROQ_API_KEY=gsk_...
BACKEND_URL="http://localhost:8000"
LANGCHAIN_API_KEY=lsv2_...          # Optional, for tracing
LANGCHAIN_TRACING_V2=true

3. Run Locally (Docker)

docker build -t blog-generator .
docker run -p 7860:7860 --env-file .env blog-generator

4. Run Locally (Manual)

Backend:

pip install -r requirements.txt
uvicorn app:app --host 0.0.0.0 --port 8000 --reload

Frontend (in a separate terminal):

    streamlit run streamlit_app.py

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🔌 API Documentation

Once running, access the Swagger UI at /docs on the backend server (e.g., http://localhost:8000/docs).

• POST /blogs: Triggers the blog generation workflow.
  • Payload example:

  {
    "topic": "Machine Learning",
    "language": "hindi"
  }

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☁️ Deployment

This project is designed to be deployed on Hugging Face Spaces using the Docker SDK.

1. Create a new Space with the Docker SDK.
2. Upload the repository files (or push via Git).
3. Add GROQ_API_KEY in the Space Settings → Variables and secrets.
4. The provided Dockerfile should start the FastAPI backend on port 7860 by default.

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Security & Notes

• Keep your API keys secret. Use environment variables or the platform's secret manager.
• Groq LLaMA 3 usage may incur costs; monitor usage and quotas.
• If you enable LangChain tracing or other telemetry, confirm whether you want to store traces externally.

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Contributing

Contributions, issues, and feature requests are welcome. For larger changes, please open a PR with a clear description and tests/examples where relevant.

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License & Contact

Include your preferred license and contact information here (e.g., MIT License, email, or GitHub handle).

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Built with ❤️ using LangGraph & Groq