🛡️ TrustNet - AI-Powered Misinformation Detection Platform

A comprehensive fullstack application that empowers users to identify misinformation and build digital immunity through AI-powered content analysis, educational insights, and real-time verification.

🚀 Quick Start

Prerequisites

• Node.js 18+ and npm
• Python 3.11+
• Modern web browser

1. Clone & Setup

git clone https://github.com/smirk-dev/TrustNet.git
cd TrustNet

2. Start Backend Server

cd services/api-python
pip install fastapi uvicorn pydantic
python basic_server.py

Backend will run on: http://localhost:8000

3. Start Frontend Server

cd mind-guard-toolkit-main
npm install
npm run dev

Frontend will run on: http://localhost:8080

4. Open Application

Navigate to http://localhost:8080 in your browser and start analyzing content!

🏗️ Architecture

Fullstack Application Structure:

TrustNet/
├── mind-guard-toolkit-main/          # React Frontend
│   ├── src/
│   │   ├── components/               # UI Components
│   │   ├── hooks/                    # React Hooks & API Integration
│   │   ├── lib/                     # API Client & Configuration
│   │   └── pages/                   # Application Pages
│   └── package.json                 # Frontend Dependencies
├── services/
│   ├── api-python/                  # FastAPI Backend
│   │   ├── app/                     # Application Logic
│   │   ├── basic_server.py          # Simplified API Server
│   │   └── requirements.txt         # Backend Dependencies
│   └── workers/                     # Background Processing
├── infra/                           # Infrastructure as Code
└── docs/                           # Documentation

Technology Stack:

• Frontend: React 18.3.1, TypeScript, Vite, TanStack Query, shadcn/ui
• Backend: Python FastAPI, Pydantic, Uvicorn
• Styling: Tailwind CSS with animations and dark mode
• State Management: TanStack Query for server state
• Build Tool: Vite for fast development and optimized builds

See ARCHITECTURE.md for detailed system design and deployment instructions.

✨ Features

🔍 Real-Time Content Analysis

• Instant Trust Scoring: Get immediate credibility assessment (0-100% scale)
• Manipulation Detection: Identifies emotional appeals, urgency tactics, and bias
• Educational Insights: Learn why content is flagged with detailed explanations
• Source Verification: Automatic checking of URLs and domain reputation

🎨 Modern User Interface

• Responsive Design: Works seamlessly on desktop, tablet, and mobile
• Dark/Light Mode: Automatic theme switching with user preference
• Real-time Feedback: Live analysis with progress indicators
• File Upload Support: Analyze documents, images, and text files

🧠 Digital Immunity Building

• Pattern Recognition: Learn to identify manipulation techniques
• Educational Tips: Context-aware guidance for each analysis
• Progressive Learning: Build critical thinking skills over time
• Safe Testing Environment: Practice with various content types

🔄 Seamless Integration

• RESTful API: Easy integration with external applications
• Real-time Processing: Immediate results with streaming updates
• Cross-platform: Works on all modern browsers and devices
• No Registration Required: Start analyzing content immediately

🔧 API Documentation

Endpoints

Health Check

GET http://localhost:8000/health
# Response: {"status": "healthy"}

Content Analysis

POST http://localhost:8000/v1/analysis
Content-Type: application/json

{
  "content": "Your content to analyze here",
  "content_type": "text",
  "user_id": "anonymous"
}

Response Format

{
  "analysis_id": "test_1234567890",
  "trust_score": {
    "overall_score": 0.75,
    "credibility": 0.78,
    "bias_score": 0.5,
    "emotional_manipulation": 0.25,
    "source_reliability": 0.75
  },
  "analysis_summary": "Content analysis results...",
  "manipulation_techniques": [],
  "educational_content": "Educational insights...",
  "metadata": {
    "word_count": 10,
    "sources": ["Source 1"]
  },
  "timestamp": 1234567890.123
}

Frontend API Integration

The frontend uses TanStack Query for efficient API state management:

import { useAnalysis } from '@/hooks/useApi';

const { analyzeContent, isAnalyzing, data, error } = useAnalysis();

// Analyze content
analyzeContent({
  content: "Text to analyze",
  content_type: "text",
  user_id: "user123"
});

💻 Development

Frontend Development

cd mind-guard-toolkit-main

# Install dependencies
npm install

# Start development server
npm run dev

# Build for production
npm run build

# Run linting
npm run lint

Backend Development

cd services/api-python

# Install dependencies
pip install fastapi uvicorn pydantic pydantic-settings

# Start development server
python basic_server.py

# Run with auto-reload
python -m uvicorn basic_server:app --reload --port 8000

Environment Configuration

Create environment files for different environments:

Frontend (.env.development)

VITE_API_BASE_URL=http://localhost:8000
VITE_NODE_ENV=development
VITE_ENABLE_DEBUG=true

Backend Configuration

The backend uses pydantic-settings for configuration management. Key settings include CORS origins, API endpoints, and feature flags.

Testing

# Frontend tests
cd mind-guard-toolkit-main
npm test

# Backend tests  
cd services/api-python
python -m pytest

# API testing
curl http://localhost:8000/health

🚀 Deployment

Production Build

# Build frontend
npm run build

# Serve static files or deploy to CDN

Environment Variables

• VITE_API_BASE_URL: Backend API URL
• VITE_ENABLE_ANALYTICS: Enable/disable analytics
• VITE_ENABLE_DEBUG: Debug mode toggle

🤝 Contributing

We welcome contributions to TrustNet! Here's how you can help:

Getting Started

1. Fork the repository

   git clone https://github.com/your-username/TrustNet.git
   cd TrustNet

2. Create a feature branch

   git checkout -b feature/amazing-feature

3. Set up development environment

   # Install frontend dependencies
   cd mind-guard-toolkit-main
   npm install
   
   # Install backend dependencies
   cd ../services/api-python
   pip install -r requirements.txt

4. Make your changes

   • Follow the existing code style
   • Add tests for new features
   • Update documentation as needed

5. Test your changes

   # Test frontend
   npm run build
   npm test
   
   # Test backend
   python basic_server.py

6. Commit and push

   git add .
   git commit -m 'Add amazing feature'
   git push origin feature/amazing-feature

7. Open a Pull Request

   • Describe your changes clearly
   • Reference any related issues
   • Wait for review and feedback

Development Guidelines

• Code Style: Follow TypeScript/Python best practices
• Testing: Add tests for new functionality
• Documentation: Update README and code comments
• Performance: Consider impact on load times and user experience
• Accessibility: Ensure UI components are accessible

Areas for Contribution

• 🎨 UI/UX Improvements: Better animations, responsive design
• 🔍 Analysis Features: New detection algorithms, better scoring
• 📱 Mobile Experience: PWA features, offline capability
• 🌍 Internationalization: Multi-language support
• 🧪 Testing: Unit tests, integration tests, E2E tests
• 📚 Documentation: Tutorials, API documentation, examples

📄 License

MIT License - see LICENSE file for details.

🙏 Acknowledgments

• React and FastAPI communities for excellent frameworks
• shadcn/ui for beautiful component library
• TanStack Query for efficient state management
• All contributors and users of TrustNet

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Built with ❤️ for digital literacy and misinformation resistance