🤖 Customer 360° Recommender System

A modular AI-powered system that analyzes customer profiles, product usage, and behavioral signals to generate cross-sell and upsell opportunities — powered by LangGraph, FastAPI, Streamlit, and Hugging Face Transformers.

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🔍 What This Project Does

This project uses a LangGraph DAG of AI agents to:

• 🧠 Understand customer context from a PostgreSQL database or CSV
• 📊 Analyze product usage and purchase history
• 🔁 Suggest related/co-purchased product opportunities
• 🎯 Score each opportunity based on business impact
• 📝 Generate a natural-language research report
• 🌐 Expose the entire pipeline via a FastAPI endpoint
• 💻 Provide a beautiful Streamlit dashboard for business users

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🧱 Project Architecture

flowchart TD
    A[📥 Input: customer_id] --> B[👤 Customer Context Agent]
    B --> C[📈 Purchase Pattern Agent]
    C --> D[🔗 Product Affinity Agent]
    D --> E[💡 Opportunity Scoring Agent]
    E --> F[📝 Report Generator Agent]
    F --> G[📤 Output: API + Streamlit]

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🚀 How to Run Locally

🔧 Setup

# 1. Clone repo and enter project
git clone https://github.com/yashdew3/langgraph-customer360-recommender.git
cd langgraph-customer360-recommender

# 2. Create virtual environment
python -m venv venv
source venv/bin/activate  # Windows: venv\Scripts\activate

# 3. Install dependencies
pip install -r requirements.txt

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🧬 Start Backend API

uvicorn app.api:app --reload

💻 Start Streamlit Frontend

streamlit run streamlit_app.py

📂 Sample API Endpoint

Test in browser or Swagger UI:

http://localhost:8000/recommendation?customer_id=C001

Open Swagger Docs:

http://localhost:8000/docs

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🧠 Features

• ✅ Modular agent system using LangGraph

• ✅ FastAPI-based REST interface

• ✅ PostgreSQL and CSV fallback support

• ✅ Real-time product affinity via Hugging Face

• ✅ Executive-ready research reports

• ✅ Interactive Streamlit UI for business teams

• ✅ Supports extension for new agents, LLMs, analytics

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📊 Technologies Used

Tech	Purpose
LangGraph	Agent-based DAG orchestration
FastAPI	RESTful API backend
Streamlit	Real-time frontend/dashboard
PostgreSQL	Structured customer database
Transformers	Embedding and NLP scoring
Uvicorn	ASGI web server for FastAPI

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🏗️ Folder Structure

├── app/
│   ├── agents/              # All 5 sub-agents
│   ├── db.py                # PostgreSQL interface
│   ├── dag.py               # LangGraph DAG pipeline
│   └── api.py               # FastAPI endpoint
│
├── data/                    # Sample CSV + SQL
│
├── dashboard/  
│   └── streamlit_app.py         # Streamlit frontend
│
├── requirements.txt
├── .gitignore
├── .env
└── LICENSCE

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✅ Example Output

{
  "executive_summary": "Edge Communications shows strong usage in Core Management Platform...",
  "recommendations": [
    {
      "product": "AI Insights Module",
      "score": 87,
      "rationale": "Related to Workflow Automation; High product usage..."
    }
  ]
}

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🧪 Future Enhancements

• 🧾 PDF report export (via WeasyPrint or PDFKit)

• 🗃️ Add dynamic product catalog & segments

• 🧬 Integrate LangChain agents for richer LLM outputs

• ☁️ Deploy with Docker, CI/CD, or Streamlit Sharing

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📄 License

MIT License © Yash Dewangan

💬 Let's Connect

Feel free to connect or suggest improvements!

• Built by Yash Dewangan
• 🐙Github: YashDewangan
• 📧Email: yashdew06@gmail.com
• 🔗Linkedin: YashDewangan

🤝 Contributing

Contributions, issues, and feature requests are welcome! Feel free to check the issues page (if you have one) or open a new issue to discuss changes. Pull requests are also appreciated.