Traffic E-Challan System - AI-Powered

🚦 AI-Powered Traffic E-Challan System

YOLOv8 • EasyOCR • ChromaDB • Llama 3 • React • Flask

An intelligent, autonomous traffic violation detection and management system utilizing Computer Vision (YOLOv8), OCR, and a completely localized, RAG-powered AI Analytics engine.

🚀 Features

• Computer Vision: YOLOv8-based detection of helmet and red-light violations.
• OCR: Automatic license plate extraction using EasyOCR.
• Dashboard: Real-time monitoring with a perfectly integrated React UI.
• Local RAG AI Assistant: Natural language querying, legal explanation generation, and automated reporting powered by a Local LLM (Ollama) and ChromaDB.
• Advanced Analytics: Scikit-Learn based predictive forecasting and high-risk hotspot clustering.
• Enterprise Infrastructure: Integrated Redis caching, Flask-Limiter rate limiting, and Celery background task processing.
• Database Migrations: Fully tracked database schema using Flask-Migrate (Alembic).

⚡ Quick Start (Recommended)

The entire backend infrastructure is Dockerized.

Prerequisites

• Docker & Docker Compose
• Ollama (For local AI features)
• Node.js 18+ (For frontend)

1. Start the Backend Infrastructure

# Spins up Flask, Redis, and the Celery worker
docker-compose up --build

2. Configure Local AI

To utilize the AI features, ensure Ollama is running on your host machine and pull the LLM:

ollama run llama3

3. Start the Frontend Dashboard

cd frontend
npm install
npm run dev             

Navigate to http://localhost:5173

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🛠️ Manual Backend Setup (Without Docker)

cd backend
python -m venv venv


pip install -r requirements.txt


flask db upgrade
python seed_data.py


python scripts/ingest_data.py


python app.py

(Note: To use caching and async tasks natively without Docker, you must run a Redis server on localhost:6379 and spin up a celery worker: celery -A tasks.tasks.celery_app worker)

📡 API Endpoints

Method	Endpoint	Description
GET	/api/violations	Fetch all violations (paginated)
GET	/api/violations/filter	Filter by type, date, vehicle
POST	/api/violation	Create new violation
POST	/api/ai/query	Ask natural language questions via Local RAG
POST	/api/ai/explain/:id	Get AI legal explanations for a violation
GET	/api/analytics/report	Fetch cached automated daily summaries
GET	/api/analytics/hotspots	Fetch high-risk clustered locations
GET	/api/analytics/forecast	Fetch 7-day predictive violation forecasts
POST	/api/cv/process-video	Upload and process CCTV video

🧠 Tech Stack

• Backend: Flask, SQLAlchemy, Alembic
• AI & RAG: LangChain, ChromaDB, HuggingFace (all-MiniLM-L6-v2), Ollama
• Infrastructure: Redis, Celery, Docker, Flask-Limiter
• Frontend: React 18, Vite, Recharts, TailwindCSS
• CV: YOLOv8 (ultralytics), OpenCV, EasyOCR

⚙️ Environment Variables

Copy backend/.env.example to backend/.env and configure:

• LOCAL_LLM_MODEL: e.g. llama3 (defaults to llama3)
• VECTOR_DB_PATH: ./chroma_db
• REDIS_URL: redis://localhost:6379/0
• CELERY_BROKER_URL: redis://localhost:6379/0