import os
import shutil

# Define the directory structure and file contents
files = {
    # Backend files
    "backend/__init__.py": "",
    "backend/main.py": """import logging
from contextlib import asynccontextmanager
from fastapi import FastAPI, WebSocket, WebSocketDisconnect
from fastapi.middleware.cors import CORSMiddleware
import uvicorn
import asyncio
from datetime import datetime

from api.routes import router as api_router
from services.websocket_manager import WebSocketManager
from services.cache_manager import CacheManager
from config.settings import settings
from models.model_manager import ModelManager
from models.database import init_db

# Configure logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

websocket_manager = WebSocketManager()
cache_manager = CacheManager()
model_manager = ModelManager()

@asynccontextmanager
async def lifespan(app: FastAPI):
    logger.info("Starting AI Creative Suite...")
    await init_db()
    await model_manager.initialize()
    await cache_manager.initialize()
    asyncio.create_task(cleanup_old_jobs())
    yield
    await model_manager.cleanup()
    await cache_manager.cleanup()

app = FastAPI(title="AI Creative Suite API", version="1.0.0", lifespan=lifespan)

app.add_middleware(
    CORSMiddleware,
    allow_origins=settings.ALLOWED_ORIGINS,
    allow_credentials=True,
    allow_methods=["*"],
    allow_headers=["*"],
)

app.include_router(api_router, prefix="/api/v1")

@app.get("/")
async def root():
    return {"name": "AI Creative Suite API", "status": "operational"}

@app.get("/health")
async def health_check():
    return {"status": "healthy", "models_loaded": model_manager.is_initialized}

@app.websocket("/ws/{job_id}")
async def websocket_endpoint(websocket: WebSocket, job_id: str):
    await websocket_manager.connect(websocket, job_id)
    try:
        while True:
            data = await websocket.receive_text()
            await websocket_manager.handle_message(job_id, data)
    except WebSocketDisconnect:
        websocket_manager.disconnect(job_id)

async def cleanup_old_jobs():
    while True:
        await asyncio.sleep(3600)
        await cache_manager.cleanup_expired_jobs()

if __name__ == "__main__":
    uvicorn.run("main:app", host="0.0.0.0", port=8000, reload=settings.DEBUG)
""",
    "backend/requirements.txt": """fastapi==0.104.1
uvicorn[standard]==0.24.0
pydantic==2.4.2
pydantic-settings==2.0.3
sqlalchemy==2.0.23
psycopg2-binary==2.9.9
redis==5.0.1
celery==5.3.4
boto3==1.28.64
python-multipart==0.0.6
python-jose[cryptography]==3.3.0
passlib[bcrypt]==1.7.4
python-dotenv==1.0.0
alembic==1.12.1
httpx==0.25.1
torch==2.1.0
torchvision==0.16.0
diffusers==0.24.0
transformers==4.35.0
accelerate==0.24.1
xformers==0.0.22
opencv-python==4.8.1.78
pillow==10.1.0
numpy==1.24.3
decord==0.6.0
imageio[ffmpeg]==2.31.6
moviepy==1.0.3
prometheus-client==0.19.0
opentelemetry-api==1.21.0
opentelemetry-sdk==1.21.0
pytest==7.4.3
pytest-asyncio==0.21.1
""",
    "backend/config/__init__.py": "",
    "backend/config/settings.py": """from pydantic_settings import BaseSettings
from typing import List
import os

class Settings(BaseSettings):
    API_VERSION: str = "v1"
    DEBUG: bool = os.getenv("DEBUG", "False").lower() == "true"
    ALLOWED_ORIGINS: List[str] = ["http://localhost:3000", "http://localhost:5173"]
    DATABASE_URL: str = os.getenv("DATABASE_URL", "postgresql://user:pass@localhost:5432/ai_creative")
    REDIS_URL: str = os.getenv("REDIS_URL", "redis://localhost:6379/0")
    AWS_ACCESS_KEY_ID: str = os.getenv("AWS_ACCESS_KEY_ID", "")
    AWS_SECRET_ACCESS_KEY: str = os.getenv("AWS_SECRET_ACCESS_KEY", "")
    S3_BUCKET: str = os.getenv("S3_BUCKET", "ai-creative-outputs")
    S3_REGION: str = os.getenv("S3_REGION", "us-east-1")
    MODEL_CACHE_DIR: str = os.getenv("MODEL_CACHE_DIR", "/models")
    IMAGE_MODEL: str = "stabilityai/stable-diffusion-xl-base-1.0"
    VIDEO_MODEL: str = "stabilityai/stable-video-diffusion-img2vid"
    CUDA_VISIBLE_DEVICES: str = os.getenv("CUDA_VISIBLE_DEVICES", "0")
    TORCH_DTYPE: str = "float16"
    CELERY_BROKER_URL: str = os.getenv("CELERY_BROKER_URL", "redis://localhost:6379/1")
    CELERY_RESULT_BACKEND: str = os.getenv("CELERY_RESULT_BACKEND", "redis://localhost:6379/2")
    SECRET_KEY: str = os.getenv("SECRET_KEY", "your-secret-key-change-in-production")
    ALGORITHM: str = "HS256"
    ACCESS_TOKEN_EXPIRE_MINUTES: int = 30
    class Config:
        env_file = ".env"

settings = Settings()
""",
    "backend/api/__init__.py": "",
    "backend/api/schemas.py": """from pydantic import BaseModel, Field, validator
from typing import Optional, List
from datetime import datetime
from enum import Enum

class JobType(str, Enum):
    IMAGE = "image"
    VIDEO = "video"

class StyleType(str, Enum):
    REALISTIC = "realistic"
    CINEMATIC = "cinematic"
    ANIME = "anime"
    THREE_D = "3d"
    PAINTING = "painting"
    CONCEPT_ART = "concept"

class GenerationRequest(BaseModel):
    prompt: str = Field(..., min_length=1, max_length=1000)
    negative_prompt: Optional[str] = Field("", max_length=500)
    style: StyleType = StyleType.REALISTIC
    resolution: str = "1024x1024"
    num_outputs: int = Field(1, ge=1, le=4)
    seed: Optional[int] = Field(None, ge=0, le=2**32-1)
    guidance_scale: float = Field(7.5, ge=1, le=15)
    num_inference_steps: int = Field(50, ge=10, le=100)

    @validator('resolution')
    def validate_resolution(cls, v):
        valid = ['512x512', '768x768', '1024x1024', '1024x768', '768x1024']
        if v not in valid:
            raise ValueError(f'Resolution must be one of {valid}')
        return v

class VideoGenerationRequest(GenerationRequest):
    duration_seconds: int = Field(5, ge=1, le=10)
    fps: int = Field(30, ge=24, le=60)
    motion_intensity: float = Field(0.5, ge=0, le=1)
    camera_angle: str = "dynamic"
    reference_image: Optional[str] = None

class GenerationResponse(BaseModel):
    job_id: str
    status: str
    output_urls: Optional[List[str]] = None
    created_at: datetime
    estimated_completion: Optional[datetime] = None

class JobStatusResponse(BaseModel):
    job_id: str
    status: str
    progress: int = 0
    output_urls: Optional[List[str]] = None
    error_message: Optional[str] = None
    created_at: datetime
    completed_at: Optional[datetime] = None

class UserCreate(BaseModel):
    email: str
    username: str
    password: str

class UserResponse(BaseModel):
    id: str
    email: str
    username: str
    credits_balance: int
    created_at: datetime

class Token(BaseModel):
    access_token: str
    token_type: str
""",
    "backend/api/routes.py": """from fastapi import APIRouter, Depends, HTTPException, BackgroundTasks
from typing import List
import uuid
from datetime import datetime, timedelta

from .schemas import (
    GenerationRequest, GenerationResponse, VideoGenerationRequest,
    JobStatusResponse, UserCreate, UserResponse, Token
)
from services.auth import get_current_user, create_access_token, verify_password, get_password_hash
from services.task_manager import TaskManager
from models.database import get_db, User, GenerationJob
from workers.tasks import generate_image_task, generate_video_task
from sqlalchemy.orm import Session
from config.settings import settings

router = APIRouter()
task_manager = TaskManager()

@router.post("/auth/register", response_model=UserResponse)
async def register(user_data: UserCreate, db: Session = Depends(get_db)):
    existing = db.query(User).filter((User.email == user_data.email) | (User.username == user_data.username)).first()
    if existing:
        raise HTTPException(400, "User already exists")
    user = User(
        id=str(uuid.uuid4()),
        email=user_data.email,
        username=user_data.username,
        hashed_password=get_password_hash(user_data.password),
        credits_balance=100
    )
    db.add(user)
    db.commit()
    db.refresh(user)
    return user

@router.post("/auth/login", response_model=Token)
async def login(username: str, password: str, db: Session = Depends(get_db)):
    user = db.query(User).filter(User.username == username).first()
    if not user or not verify_password(password, user.hashed_password):
        raise HTTPException(401, "Invalid credentials")
    access_token = create_access_token(data={"sub": user.username})
    return {"access_token": access_token, "token_type": "bearer"}

@router.post("/generate/image", response_model=GenerationResponse)
async def generate_image(
    request: GenerationRequest,
    background_tasks: BackgroundTasks,
    current_user: User = Depends(get_current_user),
    db: Session = Depends(get_db)
):
    if current_user.credits_balance < request.num_outputs:
        raise HTTPException(402, "Insufficient credits")
    job_id = str(uuid.uuid4())
    job = GenerationJob(
        id=job_id, user_id=current_user.id, job_type="image", status="queued",
        prompt=request.prompt, negative_prompt=request.negative_prompt,
        parameters=request.dict(), created_at=datetime.utcnow()
    )
    db.add(job)
    db.commit()
    current_user.credits_balance -= request.num_outputs
    db.commit()
    generate_image_task.delay(job_id=job_id, request=request.dict(), user_id=current_user.id)
    return GenerationResponse(
        job_id=job_id, status="queued", created_at=job.created_at,
        estimated_completion=datetime.utcnow() + timedelta(seconds=30)
    )

@router.post("/generate/video", response_model=GenerationResponse)
async def generate_video(
    request: VideoGenerationRequest,
    current_user: User = Depends(get_current_user),
    db: Session = Depends(get_db)
):
    credit_cost = request.duration_seconds * 2
    if current_user.credits_balance < credit_cost:
        raise HTTPException(402, "Insufficient credits")
    job_id = str(uuid.uuid4())
    job = GenerationJob(
        id=job_id, user_id=current_user.id, job_type="video", status="queued",
        prompt=request.prompt, negative_prompt=request.negative_prompt,
        parameters=request.dict(), created_at=datetime.utcnow()
    )
    db.add(job)
    db.commit()
    current_user.credits_balance -= credit_cost
    db.commit()
    generate_video_task.delay(job_id=job_id, request=request.dict(), user_id=current_user.id)
    return GenerationResponse(
        job_id=job_id, status="queued", created_at=job.created_at,
        estimated_completion=datetime.utcnow() + timedelta(minutes=2)
    )

@router.get("/job/{job_id}", response_model=JobStatusResponse)
async def get_job_status(job_id: str, current_user: User = Depends(get_current_user), db: Session = Depends(get_db)):
    job = db.query(GenerationJob).filter(GenerationJob.id == job_id).first()
    if not job or job.user_id != current_user.id:
        raise HTTPException(404, "Job not found")
    return JobStatusResponse(
        job_id=job.id, status=job.status, progress=getattr(job, 'progress', 0),
        output_urls=job.output_urls, error_message=job.error_message,
        created_at=job.created_at, completed_at=job.completed_at
    )

@router.get("/user/credits")
async def get_credits(current_user: User = Depends(get_current_user)):
    return {"credits": current_user.credits_balance}
""",
    "backend/models/__init__.py": "",
    "backend/models/database.py": """from sqlalchemy import create_engine, Column, String, DateTime, Integer, Float, JSON, Text
from sqlalchemy.ext.declarative import declarative_base
from sqlalchemy.orm import sessionmaker
from datetime import datetime
import uuid

from config.settings import settings

Base = declarative_base()

class GenerationJob(Base):
    __tablename__ = "generation_jobs"
    id = Column(String(36), primary_key=True, default=lambda: str(uuid.uuid4()))
    user_id = Column(String(36), nullable=False, index=True)
    job_type = Column(String(50), nullable=False)
    status = Column(String(50), default="queued")
    prompt = Column(Text, nullable=False)
    negative_prompt = Column(Text)
    parameters = Column(JSON)
    output_urls = Column(JSON)
    error_message = Column(Text)
    progress = Column(Integer, default=0)
    created_at = Column(DateTime, default=datetime.utcnow)
    updated_at = Column(DateTime, default=datetime.utcnow, onupdate=datetime.utcnow)
    completed_at = Column(DateTime)

class User(Base):
    __tablename__ = "users"
    id = Column(String(36), primary_key=True, default=lambda: str(uuid.uuid4()))
    email = Column(String(255), unique=True, nullable=False, index=True)
    username = Column(String(255), unique=True, nullable=False, index=True)
    hashed_password = Column(String(255), nullable=False)
    credits_balance = Column(Integer, default=100)
    created_at = Column(DateTime, default=datetime.utcnow)
    last_login = Column(DateTime)

engine = create_engine(settings.DATABASE_URL)
SessionLocal = sessionmaker(autocommit=False, autoflush=False, bind=engine)

async def init_db():
    Base.metadata.create_all(bind=engine)

def get_db():
    db = SessionLocal()
    try:
        yield db
    finally:
        db.close()
""",
    "backend/models/model_manager.py": """import torch
import asyncio
from typing import Optional, List
from diffusers import StableDiffusionXLPipeline, StableVideoDiffusionPipeline, DPMSolverMultistepScheduler, AutoencoderKL
from PIL import Image
import logging
import gc

logger = logging.getLogger(__name__)

class ModelManager:
    def __init__(self):
        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
        self.image_model = None
        self.video_model = None
        self.is_initialized = False
        self.lock = asyncio.Lock()

    async def initialize(self):
        async with self.lock:
            if not self.is_initialized:
                logger.info(f"Initializing models on {self.device}")
                await self._init_image_model()
                if torch.cuda.is_available():
                    await self._init_video_model()
                self.is_initialized = True

    async def _init_image_model(self):
        try:
            vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
            self.image_model = StableDiffusionXLPipeline.from_pretrained(
                "stabilityai/stable-diffusion-xl-base-1.0", vae=vae,
                torch_dtype=torch.float16, variant="fp16", use_safetensors=True
            )
            self.image_model.to(self.device)
            if torch.cuda.is_available():
                self.image_model.enable_model_cpu_offload()
                self.image_model.enable_vae_slicing()
                self.image_model.enable_vae_tiling()
                self.image_model.enable_attention_slicing()
            self.image_model.scheduler = DPMSolverMultistepScheduler.from_config(
                self.image_model.scheduler.config, use_karras_sigmas=True
            )
            logger.info("Image model loaded")
        except Exception as e:
            logger.error(f"Failed to load image model: {e}")

    async def _init_video_model(self):
        try:
            self.video_model = StableVideoDiffusionPipeline.from_pretrained(
                "stabilityai/stable-video-diffusion-img2vid", torch_dtype=torch.float16, variant="fp16"
            )
            self.video_model.to(self.device)
            self.video_model.enable_model_cpu_offload()
            logger.info("Video model loaded")
        except Exception as e:
            logger.error(f"Failed to load video model: {e}")

    async def generate_image(self, prompt, negative_prompt="", width=1024, height=1024,
                             num_inference_steps=50, guidance_scale=7.5, num_images=1, seed=None, style="realistic"):
        if not self.is_initialized:
            await self.initialize()
        enhanced_prompt = self._enhance_prompt(prompt, style)
        generator = torch.Generator(device=self.device).manual_seed(seed) if seed else None
        with torch.no_grad(), torch.autocast("cuda"):
            result = self.image_model(
                prompt=enhanced_prompt, negative_prompt=negative_prompt,
                width=width, height=height, num_inference_steps=num_inference_steps,
                guidance_scale=guidance_scale, num_images_per_prompt=num_images,
                generator=generator
            )
        return result.images

    def _enhance_prompt(self, prompt, style):
        style_map = {
            "realistic": "photorealistic, high quality, detailed",
            "cinematic": "cinematic, dramatic lighting, shallow depth of field",
            "anime": "anime style, vibrant colors, cel shading",
            "3d": "3d render, blender, octane render",
            "painting": "oil painting, brush strokes, masterpiece"
        }
        style_text = style_map.get(style, style_map["realistic"])
        return f"{prompt}, {style_text}, 8k, masterpiece"

    async def cleanup(self):
        if self.image_model:
            del self.image_model
        if self.video_model:
            del self.video_model
        if torch.cuda.is_available():
            torch.cuda.empty_cache()
        gc.collect()
        self.is_initialized = False
""",
    "backend/services/__init__.py": "",
    "backend/services/auth.py": """from datetime import datetime, timedelta
from jose import JWTError, jwt
from passlib.context import CryptContext
from fastapi import Depends, HTTPException, status
from fastapi.security import HTTPBearer, HTTPAuthorizationCredentials
from sqlalchemy.orm import Session
from models.database import get_db, User
from config.settings import settings

pwd_context = CryptContext(schemes=["bcrypt"], deprecated="auto")
security = HTTPBearer()

def verify_password(plain, hashed):
    return pwd_context.verify(plain, hashed)

def get_password_hash(password):
    return pwd_context.hash(password)

def create_access_token(data: dict, expires_delta: timedelta = None):
    to_encode = data.copy()
    expire = datetime.utcnow() + (expires_delta or timedelta(minutes=settings.ACCESS_TOKEN_EXPIRE_MINUTES))
    to_encode.update({"exp": expire})
    return jwt.encode(to_encode, settings.SECRET_KEY, algorithm=settings.ALGORITHM)

def decode_access_token(token: str):
    try:
        return jwt.decode(token, settings.SECRET_KEY, algorithms=[settings.ALGORITHM])
    except JWTError:
        return None

async def get_current_user(credentials: HTTPAuthorizationCredentials = Depends(security), db: Session = Depends(get_db)):
    token = credentials.credentials
    payload = decode_access_token(token)
    if not payload:
        raise HTTPException(status_code=401, detail="Invalid token")
    username = payload.get("sub")
    user = db.query(User).filter(User.username == username).first()
    if not user:
        raise HTTPException(status_code=401, detail="User not found")
    return user
""",
    "backend/services/storage.py": """import boto3
from PIL import Image
import io
import logging
from datetime import datetime
from config.settings import settings

logger = logging.getLogger(__name__)

class StorageService:
    def __init__(self):
        self.s3_client = None
        if settings.AWS_ACCESS_KEY_ID:
            self.s3_client = boto3.client(
                's3',
                aws_access_key_id=settings.AWS_ACCESS_KEY_ID,
                aws_secret_access_key=settings.AWS_SECRET_ACCESS_KEY,
                region_name=settings.S3_REGION
            )

    async def upload_image(self, image: Image.Image, job_id: str, index: int) -> str:
        buffer = io.BytesIO()
        image.save(buffer, format='PNG', optimize=True)
        buffer.seek(0)
        timestamp = datetime.utcnow().strftime("%Y%m%d_%H%M%S")
        key = f"images/{job_id}/{timestamp}_{index}.png"
        if self.s3_client:
            self.s3_client.put_object(Bucket=settings.S3_BUCKET, Key=key, Body=buffer.getvalue(), ContentType='image/png')
            return f"https://{settings.S3_BUCKET}.s3.{settings.S3_REGION}.amazonaws.com/{key}"
        else:
            import os
            os.makedirs(f"/tmp/images/{job_id}", exist_ok=True)
            filepath = f"/tmp/images/{job_id}/{timestamp}_{index}.png"
            image.save(filepath)
            return f"file://{filepath}"

    async def upload_video(self, frames, job_id: str, fps: int) -> str:
        # Simplified: just return placeholder
        return f"file:///tmp/videos/{job_id}/video.mp4"
""",
    "backend/services/websocket_manager.py": """from fastapi import WebSocket
from typing import Dict, Set
import asyncio
import json
import logging

logger = logging.getLogger(__name__)

class WebSocketManager:
    def __init__(self):
        self.active_connections: Dict[str, Set[WebSocket]] = {}
        self.lock = asyncio.Lock()

    async def connect(self, websocket: WebSocket, job_id: str):
        await websocket.accept()
        async with self.lock:
            if job_id not in self.active_connections:
                self.active_connections[job_id] = set()
            self.active_connections[job_id].add(websocket)
        logger.info(f"WebSocket connected for job {job_id}")

    def disconnect(self, job_id: str):
        # handled elsewhere
        pass

    async def send_progress(self, job_id: str, progress: int, message: str = None):
        data = {"type": "progress", "progress": progress, "message": message}
        await self._send_to_job(job_id, data)

    async def send_complete(self, job_id: str, urls: list):
        data = {"type": "complete", "urls": urls}
        await self._send_to_job(job_id, data)

    async def send_error(self, job_id: str, error: str):
        data = {"type": "error", "error": error}
        await self._send_to_job(job_id, data)

    async def _send_to_job(self, job_id: str, data: dict):
        async with self.lock:
            if job_id not in self.active_connections:
                return
            connections = self.active_connections[job_id].copy()
        for conn in connections:
            try:
                await conn.send_json(data)
            except:
                pass

    async def handle_message(self, job_id: str, message: str):
        try:
            data = json.loads(message)
            if data.get("type") == "ping":
                await self._send_to_job(job_id, {"type": "pong"})
        except:
            pass
""",
    "backend/services/task_manager.py": """class TaskManager:
    async def create_job(self, job, db):
        # Placeholder
        pass
    async def get_job(self, job_id, db):
        # Placeholder
        pass
    async def create_project(self, user_id, name, description, db):
        # Placeholder
        return "project-id"
""",
    "backend/workers/__init__.py": "",
    "backend/workers/tasks.py": """from celery import Celery
import asyncio
import logging
from datetime import datetime
from config.settings import settings
from models.model_manager import ModelManager
from models.database import SessionLocal, GenerationJob
from services.storage import StorageService

app = Celery('tasks', broker=settings.CELERY_BROKER_URL)
app.conf.update(task_serializer='json', result_serializer='json', accept_content=['json'])

logger = logging.getLogger(__name__)
model_manager = ModelManager()
storage_service = StorageService()

@app.task(bind=True)
def generate_image_task(self, job_id: str, request: dict, user_id: str):
    db = SessionLocal()
    try:
        job = db.query(GenerationJob).filter(GenerationJob.id == job_id).first()
        if job:
            job.status = "processing"
            db.commit()
        loop = asyncio.new_event_loop()
        asyncio.set_event_loop(loop)
        images = loop.run_until_complete(model_manager.generate_image(**request))
        urls = []
        for i, img in enumerate(images):
            url = loop.run_until_complete(storage_service.upload_image(img, job_id, i))
            urls.append(url)
        if job:
            job.status = "completed"
            job.output_urls = urls
            job.completed_at = datetime.utcnow()
            db.commit()
        return {'urls': urls}
    except Exception as e:
        logger.error(f"Task failed: {e}")
        if job:
            job.status = "failed"
            job.error_message = str(e)
            db.commit()
        raise
    finally:
        db.close()
""",
    "frontend/package.json": """{
  "name": "ai-creative-suite-frontend",
  "version": "1.0.0",
  "private": true,
  "scripts": {
    "dev": "next dev",
    "build": "next build",
    "start": "next start"
  },
  "dependencies": {
    "react": "^18.2.0",
    "react-dom": "^18.2.0",
    "next": "14.0.0",
    "@tanstack/react-query": "^5.8.4",
    "axios": "^1.6.2",
    "socket.io-client": "^4.5.4",
    "framer-motion": "^10.16.5",
    "tailwindcss": "^3.3.6",
    "react-hot-toast": "^2.4.1",
    "lucide-react": "^0.294.0"
  },
  "devDependencies": {
    "@types/react": "^18.2.42",
    "@types/node": "^20.10.4",
    "typescript": "^5.3.2",
    "eslint": "^8.55.0",
    "eslint-config-next": "14.0.0",
    "autoprefixer": "^10.4.16",
    "postcss": "^8.4.32"
  }
}
""",
    "frontend/next.config.js": """/** @type {import('next').NextConfig} */
const nextConfig = {
  reactStrictMode: true,
  swcMinify: true,
  async rewrites() {
    return [{ source: '/api/:path*', destination: 'http://localhost:8000/api/:path*' }];
  },
};
module.exports = nextConfig;
""",
    "frontend/tailwind.config.js": """/** @type {import('tailwindcss').Config} */
module.exports = {
  content: ['./src/**/*.{js,ts,jsx,tsx}'],
  theme: { extend: {} },
  plugins: [],
};
""",
    "frontend/tsconfig.json": """{
  "compilerOptions": {
    "target": "es5",
    "lib": ["dom", "dom.iterable", "esnext"],
    "allowJs": true,
    "skipLibCheck": true,
    "strict": true,
    "forceConsistentCasingInFileNames": true,
    "noEmit": true,
    "esModuleInterop": true,
    "module": "esnext",
    "moduleResolution": "node",
    "resolveJsonModule": true,
    "isolatedModules": true,
    "jsx": "preserve",
    "incremental": true,
    "paths": { "@/*": ["./src/*"] }
  },
  "include": ["next-env.d.ts", "**/*.ts", "**/*.tsx"],
  "exclude": ["node_modules"]
}
""",
    "frontend/src/pages/_app.tsx": """import type { AppProps } from 'next/app';
import { QueryClient, QueryClientProvider } from '@tanstack/react-query';
import { Toaster } from 'react-hot-toast';
import '../styles/globals.css';

const queryClient = new QueryClient();

export default function App({ Component, pageProps }: AppProps) {
  return (
    <QueryClientProvider client={queryClient}>
      <Component {...pageProps} />
      <Toaster position="top-right" />
    </QueryClientProvider>
  );
}
""",
    "frontend/src/pages/index.tsx": """import { useState } from 'react';
import GenerationInterface from '../components/GenerationInterface';
import { motion } from 'framer-motion';

export default function Home() {
  const [activeTab, setActiveTab] = useState<'image' | 'video'>('image');
  return (
    <div className="min-h-screen bg-gradient-to-br from-gray-900 to-gray-800">
      <nav className="bg-black bg-opacity-50 backdrop-blur-lg border-b border-gray-700">
        <div className="container mx-auto px-4 py-4 flex justify-between">
          <span className="text-xl font-bold text-white">AI Creative Suite</span>
          <div className="flex space-x-4">
            <button className="text-gray-300">Dashboard</button>
            <button className="text-gray-300">Credits: 100</button>
          </div>
        </div>
      </nav>
      <div className="container mx-auto px-4 py-8">
        <div className="flex space-x-4 mb-8">
          <button onClick={() => setActiveTab('image')} className={`px-6 py-2 rounded-lg ${activeTab === 'image' ? 'bg-blue-600' : 'bg-gray-700'}`}>Image</button>
          <button onClick={() => setActiveTab('video')} className={`px-6 py-2 rounded-lg ${activeTab === 'video' ? 'bg-blue-600' : 'bg-gray-700'}`}>Video</button>
        </div>
        <motion.div key={activeTab} initial={{ opacity: 0, y: 20 }} animate={{ opacity: 1, y: 0 }}>
          <GenerationInterface type={activeTab} />
        </motion.div>
      </div>
    </div>
  );
}
""",
    "frontend/src/components/GenerationInterface.tsx": """import React, { useState, useRef, useEffect } from 'react';
import { useMutation } from '@tanstack/react-query';
import { motion, AnimatePresence } from 'framer-motion';
import toast from 'react-hot-toast';
import { Sparkles, Download, RefreshCw } from 'lucide-react';
import axios from 'axios';

interface Props { type: 'image' | 'video'; }

const GenerationInterface: React.FC<Props> = ({ type }) => {
  const [prompt, setPrompt] = useState('');
  const [negativePrompt, setNegativePrompt] = useState('');
  const [selectedStyle, setSelectedStyle] = useState('realistic');
  const [resolution, setResolution] = useState('1024x1024');
  const [numOutputs, setNumOutputs] = useState(1);
  const [guidanceScale, setGuidanceScale] = useState(7.5);
  const [duration, setDuration] = useState(5);
  const [motionIntensity, setMotionIntensity] = useState(0.5);
  const [currentJob, setCurrentJob] = useState<string | null>(null);
  const [generatedUrls, setGeneratedUrls] = useState<string[]>([]);
  const [progress, setProgress] = useState(0);
  const wsRef = useRef<WebSocket | null>(null);

  const styles = ['realistic', 'cinematic', 'anime', '3d', 'painting', 'concept'];
  const resolutions = ['512x512', '768x768', '1024x1024', '1024x768', '768x1024'];

  const generateMutation = useMutation({
    mutationFn: async (params: any) => {
      const endpoint = type === 'image' ? '/api/v1/generate/image' : '/api/v1/generate/video';
      const res = await axios.post(endpoint, params, { headers: { Authorization: `Bearer ${localStorage.getItem('token')}` } });
      return res.data;
    },
    onSuccess: (data) => {
      setCurrentJob(data.job_id);
      connectWebSocket(data.job_id);
      toast.success('Generation started!');
    },
    onError: () => toast.error('Failed to start generation'),
  });

  const connectWebSocket = (jobId: string) => {
    const ws = new WebSocket(`ws://localhost:8000/ws/${jobId}`);
    ws.onmessage = (event) => {
      const data = JSON.parse(event.data);
      if (data.type === 'progress') setProgress(data.progress);
      else if (data.type === 'complete') {
        setGeneratedUrls(prev => [...prev, ...data.urls]);
        setCurrentJob(null);
        setProgress(0);
        toast.success('Complete!');
        ws.close();
      } else if (data.type === 'error') {
        toast.error(data.error);
        setCurrentJob(null);
        ws.close();
      }
    };
    wsRef.current = ws;
  };

  const handleGenerate = () => {
    if (!prompt.trim()) return toast.error('Enter a prompt');
    const params: any = { prompt, negative_prompt: negativePrompt, style: selectedStyle, resolution, num_outputs: numOutputs, guidance_scale: guidanceScale };
    if (type === 'video') { params.duration_seconds = duration; params.motion_intensity = motionIntensity; params.fps = 30; }
    generateMutation.mutate(params);
  };

  return (
    <div className="grid grid-cols-1 lg:grid-cols-2 gap-8">
      <div className="bg-gray-800 rounded-xl p-6 shadow-xl">
        <h2 className="text-2xl font-bold text-white mb-6">{type === 'image' ? 'Create Image' : 'Create Video'}</h2>
        <div className="mb-4"><label className="text-sm text-gray-300">Prompt</label><textarea value={prompt} onChange={e => setPrompt(e.target.value)} className="w-full bg-gray-700 rounded-lg p-3 text-white" rows={3} placeholder="Describe..." /></div>
        <div className="mb-4"><label className="text-sm text-gray-300">Negative Prompt</label><input type="text" value={negativePrompt} onChange={e => setNegativePrompt(e.target.value)} className="w-full bg-gray-700 rounded-lg p-3 text-white" placeholder="Avoid..." /></div>
        <div className="mb-4"><label className="text-sm text-gray-300">Style</label><div className="grid grid-cols-3 gap-2">{styles.map(s => <button key={s} onClick={() => setSelectedStyle(s)} className={`px-3 py-2 rounded-lg capitalize ${selectedStyle === s ? 'bg-blue-600' : 'bg-gray-700'}`}>{s}</button>)}</div></div>
        <div className="mb-4"><label className="text-sm text-gray-300">Resolution</label><select value={resolution} onChange={e => setResolution(e.target.value)} className="w-full bg-gray-700 rounded-lg p-3 text-white">{resolutions.map(r => <option key={r}>{r}</option>)}</select></div>
        <div className="mb-4"><label className="text-sm text-gray-300">Number of Outputs: {numOutputs}</label><input type="range" min={1} max={4} value={numOutputs} onChange={e => setNumOutputs(parseInt(e.target.value))} className="w-full" /></div>
        <div className="mb-4"><label className="text-sm text-gray-300">Guidance Scale: {guidanceScale}</label><input type="range" min={1} max={15} step={0.5} value={guidanceScale} onChange={e => setGuidanceScale(parseFloat(e.target.value))} className="w-full" /></div>
        {type === 'video' && (<><div className="mb-4"><label>Duration: {duration}s</label><input type="range" min={1} max={10} value={duration} onChange={e => setDuration(parseInt(e.target.value))} className="w-full" /></div><div className="mb-6"><label>Motion: {motionIntensity}</label><input type="range" min={0} max={1} step={0.05} value={motionIntensity} onChange={e => setMotionIntensity(parseFloat(e.target.value))} className="w-full" /></div></>)}
        <button onClick={handleGenerate} disabled={generateMutation.isPending || currentJob !== null} className="w-full bg-gradient-to-r from-blue-600 to-purple-600 hover:from-blue-700 hover:to-purple-700 text-white font-bold py-3 px-6 rounded-lg transition disabled:opacity-50 flex items-center justify-center space-x-2">
          {generateMutation.isPending || currentJob ? <><RefreshCw className="w-5 h-5 animate-spin" /><span>Generating... {progress}%</span></> : <><Sparkles className="w-5 h-5" /><span>Generate</span></>}
        </button>
        {currentJob && <div className="mt-4"><div className="w-full bg-gray-700 rounded-full h-2"><div className="bg-blue-600 h-2 rounded-full transition-all" style={{ width: `${progress}%` }} /></div></div>}
      </div>
      <div className="bg-gray-800 rounded-xl p-6 shadow-xl">
        <h3 className="text-xl font-bold text-white mb-4">Results</h3>
        {generatedUrls.length === 0 ? <div className="text-gray-400 text-center py-20">Your generated {type}s will appear here</div> : <div className="grid grid-cols-2 gap-4">{generatedUrls.map((url, i) => (<div key={i} className="relative group"><img src={url} alt="result" className="rounded-lg w-full cursor-pointer" onClick={() => window.open(url)} /><div className="absolute inset-0 bg-black bg-opacity-0 group-hover:bg-opacity-50 transition rounded-lg flex items-center justify-center opacity-0 group-hover:opacity-100"><button onClick={() => window.open(url)} className="bg-white text-gray-900 px-3 py-2 rounded-lg"><Download className="w-4 h-4" /></button></div></div>))}</div>}
      </div>
    </div>
  );
};
export default GenerationInterface;
""",
    "frontend/src/styles/globals.css": """@tailwind base;
@tailwind components;
@tailwind utilities;
body { @apply bg-gray-900 text-white; }
::-webkit-scrollbar { width: 8px; }
::-webkit-scrollbar-track { @apply bg-gray-800; }
::-webkit-scrollbar-thumb { @apply bg-gray-600 rounded-full; }
""",
    "docker-compose.yml": """version: '3.8'
services:
  postgres:
    image: postgres:15
    environment:
      POSTGRES_USER: ai_user
      POSTGRES_PASSWORD: ai_pass
      POSTGRES_DB: ai_creative
    ports:
      - "5432:5432"
  redis:
    image: redis:7-alpine
    ports:
      - "6379:6379"
  backend:
    build: ./backend
    ports:
      - "8000:8000"
    environment:
      DATABASE_URL: postgresql://ai_user:ai_pass@postgres:5432/ai_creative
      REDIS_URL: redis://redis:6379/0
      CELERY_BROKER_URL: redis://redis:6379/1
    depends_on:
      - postgres
      - redis
  celery-worker:
    build: ./backend
    command: celery -A workers.tasks worker --loglevel=info
    environment:
      DATABASE_URL: postgresql://ai_user:ai_pass@postgres:5432/ai_creative
      REDIS_URL: redis://redis:6379/0
      CELERY_BROKER_URL: redis://redis:6379/1
    depends_on:
      - redis
      - postgres
  frontend:
    build: ./frontend
    ports:
      - "3000:3000"
    environment:
      NEXT_PUBLIC_API_URL: http://localhost:8000
    depends_on:
      - backend
""",
    ".env.example": """DATABASE_URL=postgresql://ai_user:ai_pass@localhost:5432/ai_creative
REDIS_URL=redis://localhost:6379/0
SECRET_KEY=your-secret-key-change-in-production
DEBUG=True
""",
    "README.md": """# AI Creative Suite

## Setup
1. Install Docker and Docker Compose
2. Copy .env.example to .env
3. Run `docker-compose up -d`
4. Access frontend at http://localhost:3000
5. API docs at http://localhost:8000/api/docs

## Manual Setup
1. Create virtual env: `python -m venv venv`
2. Activate: `source venv/bin/activate`
3. Install backend: `pip install -r backend/requirements.txt`
4. Run backend: `uvicorn backend.main:app --reload`
5. Run frontend: `cd frontend && npm install && npm run dev`

## Features
- Text-to-image generation
- Text-to-video generation
- User authentication
- Real-time progress via WebSockets
- Style customization
"""
}

# Create directories and files
for filepath, content in files.items():
    dirname = os.path.dirname(filepath)
    if dirname:
        os.makedirs(dirname, exist_ok=True)
    with open(filepath, 'w', encoding='utf-8') as f:
        f.write(content)
    print(f"Created {filepath}")