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.

backend/Dockerfile

@@ -0,0 +1,27 @@
+FROM python:3.11-slim
+
+WORKDIR /app
+
+# Install system dependencies
+RUN apt-get update && apt-get install -y \
+    libgl1-mesa-glx \
+    libglib2.0-0 \
+    && rm -rf /var/lib/apt/lists/*
+
+# Copy requirements
+COPY requirements.txt .
+
+# Install Python dependencies
+RUN pip install --no-cache-dir -r requirements.txt
+
+# Copy application
+COPY . .
+
+# Create data directory
+RUN mkdir -p /app/data/projects
+
+# Expose port
+EXPOSE 8000
+
+# Run the application
+CMD ["uvicorn", "app.main:app", "--host", "0.0.0.0", "--port", "8000"]