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.

CONTRIBUTING.md

@@ -0,0 +1,86 @@
+# Contributing to AI Photo Edit
+
+Thank you for your interest in contributing to AI Photo Edit!
+
+## Development Setup
+
+1. Fork the repository
+2. Clone your fork
+3. Create a feature branch
+4. Make your changes
+5. Test your changes
+6. Submit a pull request
+
+## Development Environment
+
+### Using Docker (Recommended)
+
+```bash
+# Start dev environment with hot-reload
+docker-compose -f docker-compose.dev.yml up
+```
+
+### Local Development
+
+**Backend**
+```bash
+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**
+```bash
+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
+
+1. Update the README.md with details of changes if needed
+2. Ensure all tests pass
+3. Update documentation as needed
+4. Get approval from maintainers
+5. 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!