UFML - Un Fuck My Life Credit Repair Platform

🚀 Quick Start (Windows)

Option 1: One-Click Start (Recommended)

1. Double-click start-ufml.bat
2. Wait for both servers to start
3. Open http://localhost:3000 in your browser

Option 2: Manual Start

1. Backend: Double-click start-backend.bat
2. Frontend: Double-click start-frontend.bat
3. Open http://localhost:3000 in your browser

📋 Requirements

• Python 3.8+ (for backend)
• Node.js 16+ (for frontend)
• Git (for cloning)

🔧 Installation

First Time Setup

# Clone the repository
git clone <repository-url>
cd credit-platform-secure-scaffold-with-policies

# Install backend dependencies
cd apps/api
pip install -r requirements.txt

# Install frontend dependencies
cd ../web
npm install

🌐 URLs

• Frontend: http://localhost:3000
• Backend API: http://127.0.0.1:8000
• API Documentation: http://127.0.0.1:8000/docs

🛠️ Troubleshooting

Backend Won't Start

1. Check Python is installed: python --version
2. Install dependencies: pip install -r apps/api/requirements.txt
3. Run: start-backend.bat

Frontend Won't Start

1. Check Node.js is installed: node --version
2. Install dependencies: npm install (in apps/web folder)
3. Run: start-frontend.bat

Port Already in Use

• Backend (port 8000): Kill Python processes or change port
• Frontend (port 3000): Kill Node processes or change port

📱 Features

• Real AI Credit Analysis - Uses Ollama for local AI processing
• PDF Credit Report Parsing - Extracts real data from uploaded PDFs
• Advanced Dispute Strategies - E Oscar bypass, factual disputes, consumer law violations
• Specialty Bureau Targeting - LexisNexis, LCI, Innovis, ARS, Clarity, DataX, etc.
• Legal Compliance - FCRA, FDCPA, TILA violation detection
• Police Report Strategies - Identity theft and fraud dispute techniques
• Metro2 Compliance - Advanced dispute validation bypass

🔒 Security

• All AI processing happens locally (Ollama)
• No data sent to external services
• Secure file uploads and processing
• GDPR compliant data handling

📞 Support

If you encounter issues:

1. Check the troubleshooting section above
2. Ensure all dependencies are installed
3. Verify both servers are running
4. Check browser console for errors

⚠️ Important Notes

• Backend must be running for real AI analysis
• Mock data warnings appear when backend is down
• Never use mock data for real credit disputes
• Real AI analysis required for legal compliance

🐳 Run with Docker

# Build and start all services
docker compose build
docker compose up

# Access the application
# Frontend: http://127.0.0.1:3000
# Backend: http://127.0.0.1:8000

Docker includes:

• Tesseract OCR for PDF processing
• All dependencies pre-installed
• Consistent environment across platforms

🖥️ Run on Windows (no Docker)

Backend Setup

cd apps/api
pip install -r requirements.txt
set DATABASE_URL=sqlite:///./dev.db
set OLLAMA_HOST=http://127.0.0.1:11434
set OLLAMA_MODEL=llama3.1:8b-instruct
uvicorn main:app --host 127.0.0.1 --port 8000 --reload

Test: http://127.0.0.1:8000/healthz

Frontend Setup

cd apps/web
set NEXT_PUBLIC_API_URL=http://127.0.0.1:8000
set NEXT_PUBLIC_USE_MOCKS=false
npm install
npm run dev

Open: http://127.0.0.1:3000

🧪 Testing

Smoke Tests

Run the PowerShell smoke tests to verify functionality:

.\scripts\smoke.ps1

Tests include:

• Health check
• AI health check
• PDF upload (if sample.pdf exists)
• Report listing
• AI analysis
• Report deletion

Quick Verification

# Test backend health
curl http://127.0.0.1:8000/healthz

# Test AI health
curl http://127.0.0.1:8000/ai/health

📊 Enhanced Features

• PDF Upload & Parsing: Upload credit reports with OCR fallback
• AI Analysis: Intelligent analysis using Ollama (llama3.1:8b-instruct)
• Dispute Generation: Automated dispute letter creation
• Client Management: Track clients and their reports
• Real-time Updates: Live status updates and notifications
• Structured Logging: Request tracking and performance monitoring
• Error Handling: Specific error messages for different failure types
• No Mock Data: Real API calls only (configurable via USE_MOCKS flag)