Fact-Checking Platform

A modern web-based fact-checking platform designed to help users verify information and combat misinformation. This platform provides tools for researching and analyzing content to determine its accuracy and reliability.

Project Goal

The primary goal of this project is to create a comprehensive fact-checking platform that enables users to:

• Submit content for fact-checking and verification
• Research information using automated tools and services
• Access reliable sources and cross-reference claims
• View detailed analysis and verification results
• Navigate through video content with fact-checking capabilities

Architecture

This project follows a microservices architecture with separate frontend and backend services that can be deployed independently or together using Docker Compose.

Folder Structure

/frontend

Technology Stack: React 19, Vite, TailwindCSS, React Router DOM

The frontend service provides the user interface for the fact-checking platform.

Key Components:

• React Router Setup: Multi-page application with routing capabilities
• CedarCopilot Integration: AI-powered assistance with LLM provider configuration
• Responsive UI: Modern interface using TailwindCSS and Framer Motion

Main Routes:

• / - Home Route: Main landing page for the platform
• /v/:id - Video Route: Video content analysis and fact-checking interface

Key Files:

• src/App.jsx - Main application component with router configuration
• src/routes/Home.jsx - Home page component
• src/routes/Video.jsx - Video analysis interface with parameter-based routing
• src/layout.jsx - Shared layout component
• package.json - Dependencies and build configuration

Development Scripts:

• npm run dev - Start development server
• npm run build - Build for production
• npm run lint - Code linting

/backend-research

Technology Stack: FastAPI, Python, Uvicorn

The backend research service handles API requests, data processing, and research operations for fact-checking.

Key Endpoints:

• GET / - API information and version details
• GET /health - Health check endpoint for monitoring service status

Key Files:

• main.py - FastAPI application with core endpoints
• requirements.txt - Python dependencies (FastAPI, Uvicorn)
• Dockerfile - Container configuration for deployment
• .env - Environment configuration

Service Features:

• RESTful API architecture
• Health monitoring
• Docker containerization support
• Development environment configuration

Root Configuration

Key Files:

• docker-compose.yml - Multi-service orchestration configuration
  • Backend service on port 8000
  • Health checks and auto-restart policies
  • Development environment setup
• .gitignore - Version control exclusions

Getting Started

Using Docker Compose (Recommended)

docker-compose up --build

Manual Development Setup

Backend Research Service:

cd backend-research
pip install -r requirements.txt
uvicorn main:app --reload --host 0.0.0.0 --port 8000

Frontend Service:

cd frontend
npm install
npm run dev

Service Communication

• Frontend connects to backend via configured API endpoints
• CedarCopilot integration points to http://localhost:3000/api/llm
• Backend research service runs on port 8000
• Health monitoring available at /health endpoint

Development Status

This is an early-stage project with basic service architecture in place. Current implementation includes:

• ✅ Basic frontend routing and UI framework
• ✅ Backend API foundation with health checks
• ✅ Docker containerization setup
• ✅ Development environment configuration

Next Steps

The platform is ready for feature development including:

• Fact-checking algorithms and research tools
• Content analysis and verification systems
• Database integration for storing research results
• User authentication and management
• Enhanced video analysis capabilities