Claude
c8078d4652
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.
76 lines
1.4 KiB
Python
76 lines
1.4 KiB
Python
from pydantic import BaseModel, EmailStr
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from typing import Optional, List, Dict, Any
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from datetime import datetime
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# User schemas
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class UserCreate(BaseModel):
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email: EmailStr
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password: str
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class UserResponse(BaseModel):
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id: int
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email: str
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created_at: datetime
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class Config:
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from_attributes = True
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# Project schemas
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class ProjectCreate(BaseModel):
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name: str
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class ProjectResponse(BaseModel):
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id: int
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user_id: int
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name: str
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created_at: datetime
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updated_at: datetime
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class Config:
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from_attributes = True
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# Edit schemas
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class EditRequest(BaseModel):
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prompt: str
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mode: str = "A" # "A" or "B"
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selection_type: str # "rectangle", "ellipse", "lasso"
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bbox: Dict[str, int] # {x, y, width, height}
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feather_px: int = 0
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selection_data: Optional[Dict[str, Any]] = None
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class EditResponse(BaseModel):
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id: int
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project_id: int
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created_at: datetime
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mode: str
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prompt: str
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selection_type: str
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bbox_json: str
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feather_px: int
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ai_provider: str
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status: str
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error_message: Optional[str] = None
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class Config:
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from_attributes = True
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# Image upload
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class UploadResponse(BaseModel):
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project_id: int
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original_url: str
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current_url: str
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# Generic responses
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class StatusResponse(BaseModel):
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status: str
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message: Optional[str] = None
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data: Optional[Any] = None
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