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/app/database.py

@@ -0,0 +1,30 @@
+from sqlalchemy import create_engine
+from sqlalchemy.ext.declarative import declarative_base
+from sqlalchemy.orm import sessionmaker
+from app.config import settings
+import os
+
+# Ensure data directory exists
+os.makedirs("./data", exist_ok=True)
+
+engine = create_engine(
+    settings.database_url,
+    connect_args={"check_same_thread": False}  # Needed for SQLite
+)
+
+SessionLocal = sessionmaker(autocommit=False, autoflush=False, bind=engine)
+
+Base = declarative_base()
+
+
+def get_db():
+    db = SessionLocal()
+    try:
+        yield db
+    finally:
+        db.close()
+
+
+def init_db():
+    """Initialize database tables"""
+    Base.metadata.create_all(bind=engine)