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Claude c8078d4652 Implement complete AI Photo Edit tool with mask-scoped regeneration

This commit implements a full-stack AI photo editing application that
allows users to regenerate only selected areas of images using AI.

Features implemented:
- Frontend (React + Fabric.js):
  * Interactive canvas with selection tools (rectangle, ellipse, lasso)
  * Real-time selection preview and editing
  * Mode toggle (A: patch only, B: patch + context)
  * Feather slider for edge blending (0-50px)
  * Prompt input for AI instructions
  * Edit history viewer with revert capability
  * Responsive UI with dark theme

- Backend (FastAPI):
  * RESTful API for projects and edits
  * SQLite database for metadata storage
  * Image processing pipeline with PIL/OpenCV
  * AI provider interface (pluggable)
  * Support for OpenAI, Stability AI, and mock providers
  * Feathered alpha blending for smooth compositing
  * Complete edit history tracking
  * File-based storage for images and edits

- Image Processing:
  * Patch extraction from bounding boxes
  * Mask generation for all selection types
  * Feathered edge blending
  * Patch compositing back to full image
  * No pixels modified outside selection
  * All edits reversible

- Infrastructure:
  * Docker Compose orchestration
  * Production and development configurations
  * Nginx reverse proxy for frontend
  * Hot-reload support for development
  * Volume persistence for data

Architecture follows specification exactly:
- Only selected regions are regenerated
- Full image pixels preserved outside mask
- Two-mode operation (cost vs quality)
- Complete edit history and reversibility
- Self-hosted with external AI API calls

All components are fully functional and ready for deployment.

2026-01-24 02:58:41 +00:00

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Contributing to AI Photo Edit

Thank you for your interest in contributing to AI Photo Edit!

Development Setup

Fork the repository
Clone your fork
Create a feature branch
Make your changes
Test your changes
Submit a pull request

Development Environment

Using Docker (Recommended)

# Start dev environment with hot-reload
docker-compose -f docker-compose.dev.yml up

Local Development

Backend

cd backend
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate
pip install -r requirements.txt
uvicorn app.main:app --reload

Frontend

cd frontend
npm install
npm run dev

Code Style

Python (Backend)

Follow PEP 8
Use type hints where appropriate
Add docstrings to functions and classes

JavaScript/React (Frontend)

Use functional components with hooks
Follow React best practices
Use meaningful variable names

Pull Request Process

Update the README.md with details of changes if needed
Ensure all tests pass
Update documentation as needed
Get approval from maintainers
Squash commits if requested

Reporting Bugs

When reporting bugs, please include:

Description of the issue
Steps to reproduce
Expected behavior
Actual behavior
Screenshots if applicable
Environment details (OS, Docker version, etc.)

Feature Requests

We welcome feature requests! Please:

Check if the feature already exists
Explain the use case
Describe the expected behavior
Consider if it aligns with project goals

Code of Conduct

Be respectful and inclusive
Welcome newcomers
Focus on constructive feedback
Respect differing opinions

Thank you for contributing!

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