About This Project
🚀 Overview
This is a modern, full-stack AI text analysis application that combines the power of LangGraph workflows with Ollama local language models to provide intelligent text summarization and sentiment analysis.
The application demonstrates a clean separation between frontend and backend, making it perfect for deployment on modern cloud platforms.
🏗️ Architecture
Frontend
- Flask - Lightweight Python web framework
- Modern CSS - Beautiful, responsive design with gradients and animations
- Vanilla JavaScript - No framework bloat, just clean code
- Deployed on Vercel - Fast, global CDN
Backend
- FastAPI - Modern, fast REST API framework
- LangGraph - Advanced workflow orchestration
- Ollama - Local LLM inference
- Deployed on Render.com - Reliable, scalable hosting
✨ Features
📝 Smart Summarization
AI-powered text summarization that captures key points in 2-3 sentences
💭 Sentiment Analysis
Detects positive, negative, neutral, or mixed emotions in your text
⚡ Multiple Models
Choose from various Ollama models based on your needs
🎨 Beautiful UI
Modern, responsive design with smooth animations
🔒 Privacy First
Your text is processed on-demand, not stored
📊 Real-time Stats
Instant word count, character count, and analysis results
🛠️ Technology Stack
Backend
- Python 3.11+
- FastAPI
- LangGraph
- LangChain
- Ollama
- Uvicorn
Frontend
- Python 3.11+
- Flask
- Modern CSS3
- Vanilla JavaScript
- Google Fonts
Deployment
- Vercel (Frontend)
- Render.com (Backend)
- Git/GitHub
- Environment Variables
⚙️ How It Works
- User Input: You enter text in the frontend interface
- API Request: Frontend sends a POST request to the FastAPI backend
-
LangGraph Processing: Backend runs a multi-node workflow:
- Node 1: Processes input and calculates word count
- Node 2: Generates summary and sentiment using Ollama LLM
- Response: Results are returned as JSON to the frontend
- Display: Beautiful UI displays the analysis results
👨💻 Developer Information
This project was built as a demonstration of modern full-stack development practices, combining AI/ML capabilities with clean architecture and beautiful design.
The codebase is structured for easy maintenance and scalability, with clear separation of concerns between frontend presentation and backend logic.