nickviljoen 3fec052c12 Create frontend and backend folder structure for deployment

Organized the application into separate frontend and backend directories for cleaner deployment and better separation of concerns.

Frontend Directory (frontend/):
- index.html: Single-page web interface (renamed from web_ui.html)
- README.md: Frontend deployment guide
- Total size: ~113 KB (self-contained)
- Smart base path detection (works at / or /ai_qc/)
- No configuration changes required

Backend Directory (backend/):
- All Python files (api_server.py, llm_config.py, etc.)
- visual_qc_apps/: 33 QC check modules
- profiles/: 6 QC profile configurations
- brand_guidelines/: Reference asset storage
- config/: Environment configurations
- scripts/: Deployment automation
- uploads/, output/: Data directories
- requirements.txt, ai_qc.service, apache_config.conf
- Complete documentation

New Documentation:
- FOLDER_STRUCTURE.md: Comprehensive guide to new structure
- frontend/README.md: Frontend deployment instructions
- backend/BACKEND_README.md: Backend deployment guide

Deployment Mapping:
- frontend/ → /var/www/html/ai_qc/ (web root)
- backend/ → /opt/ai_qc/ (application directory)

Benefits:
- Clear separation of concerns
- Backend code not in web-accessible directory
- Independent frontend/backend updates
- Matches server's existing patterns (/opt/veo3, /opt/voice2text)
- Industry-standard architecture
- Easy to deploy and maintain

Original files preserved in root directory for reference.
Ready for production deployment following MIGRATION_GUIDE.md.

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <noreply@anthropic.com>

2025-11-06 11:55:53 +02:00

2.6 KiB

Raw Permalink Blame History

Visual AI QC - Backend

This directory contains the complete backend application for the Visual AI QC platform.

Core Application Files

api_server.py - Main Flask application with REST API endpoints
run_api_server.py - Production server wrapper using Waitress WSGI
llm_config.py - LLM (OpenAI/Gemini) configuration and API interaction
profile_config.py - QC profile loading and management system

Authentication & Security

jwt_validator.py - Azure AD JWT token validation
auth_middleware.py - Flask authentication middleware with httpOnly cookies

Data & Storage

brand_guidelines_db.py - Brand guidelines database management
visual_qc_apps/ - 33+ QC check modules with standardized interface
profiles/ - 6 QC profile configurations (JSON)
brand_guidelines/ - Reference asset storage and metadata
config/ - Environment configuration files

Upload & Output Directories

uploads/ - Production file uploads (temporary storage)
uploads-dev/ - Development file uploads
output/ - Production generated reports (HTML/JSON)
output-dev/ - Development generated reports

Deployment & Configuration

requirements.txt - Python dependencies
ai_qc.service - Systemd service configuration
apache_config.conf - Apache virtual host template
scripts/ - Deployment and testing scripts

Deployment Location

On Server: /opt/ai_qc/

This backend should be deployed to /opt/ai_qc/ on the production server, following the standard pattern used by other applications on the server.

Quick Start

Production Deployment

Copy to Server

rsync -av backend/ user@server:/opt/ai_qc/

Install Dependencies

cd /opt/ai_qc
python3 -m venv venv
source venv/bin/activate
pip install -r requirements.txt

Configure Production

nano config/production.env
# Update paths to absolute:
# UPLOAD_FOLDER=/opt/ai_qc/uploads
# OUTPUT_FOLDER=/opt/ai_qc/output

Set Permissions

sudo chown -R www-data:www-data /opt/ai_qc
sudo chmod -R 750 /opt/ai_qc
sudo chmod 770 /opt/ai_qc/uploads /opt/ai_qc/output

Install Service

sudo cp ai_qc.service /etc/systemd/system/
sudo systemctl daemon-reload
sudo systemctl enable ai_qc
sudo systemctl start ai_qc

Frontend Setup: See ../frontend/README.md
Complete Migration Guide: See ../MIGRATION_GUIDE.md
Full README: See README.md in this directory

Deploy to: /opt/ai_qc/ 🚀

2.6 KiB Raw Permalink Blame History