app/main_sdxl.py

"""
FastAPI application for Text-Guided Image Colorization using Hugging Face Inference API
Uses fal-ai provider for memory-efficient inference
"""
import os
import io
import uuid
import logging
from pathlib import Path
from typing import Optional, Tuple

from fastapi import FastAPI, UploadFile, File, HTTPException, Depends, Request, Body
from fastapi.responses import FileResponse, JSONResponse
from fastapi.middleware.cors import CORSMiddleware
from fastapi.staticfiles import StaticFiles
import firebase_admin
from firebase_admin import credentials, app_check, auth as firebase_auth
from PIL import Image
import uvicorn
import gradio as gr
import httpx
from pydantic import BaseModel, EmailStr

# Hugging Face Inference API
from huggingface_hub import InferenceClient

from app.config import settings

# Configure logging
logging.basicConfig(
    level=logging.INFO,
    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
)
logger = logging.getLogger(__name__)

# Create writable directories
Path("/tmp/hf_cache").mkdir(parents=True, exist_ok=True)
Path("/tmp/matplotlib_config").mkdir(parents=True, exist_ok=True)
Path("/tmp/colorize_uploads").mkdir(parents=True, exist_ok=True)
Path("/tmp/colorize_results").mkdir(parents=True, exist_ok=True)

# Initialize FastAPI app
app = FastAPI(
    title="Text-Guided Image Colorization API",
    description="Image colorization using SDXL + ControlNet with automatic captioning",
    version="1.0.0"
)

# CORS middleware
app.add_middleware(
    CORSMiddleware,
    allow_origins=["*"],
    allow_credentials=True,
    allow_methods=["*"],
    allow_headers=["*"],
)

# Initialize Firebase Admin SDK
# Try multiple possible paths for Firebase credentials
firebase_cred_paths = [
    os.getenv("FIREBASE_CREDENTIALS_PATH"),
    "/tmp/firebase-adminsdk.json",
    "/data/firebase-adminsdk.json",
    "colorize-662df-firebase-adminsdk-fbsvc-bfd21c77c6.json",
    os.path.join(os.path.dirname(__file__), "..", "colorize-662df-firebase-adminsdk-fbsvc-bfd21c77c6.json"),
]

firebase_initialized = False
for cred_path in firebase_cred_paths:
    if not cred_path:
        continue
    cred_path = os.path.abspath(cred_path)
    if os.path.exists(cred_path):
        try:
            cred = credentials.Certificate(cred_path)
            firebase_admin.initialize_app(cred)
            logger.info("Firebase Admin SDK initialized from: %s", cred_path)
            firebase_initialized = True
            break
        except Exception as e:
            logger.warning("Failed to initialize Firebase from %s: %s", cred_path, str(e))
            continue

# Also try loading from environment variable (for Hugging Face Spaces)
if not firebase_initialized:
    firebase_json = os.getenv("FIREBASE_CREDENTIALS")
    if firebase_json:
        try:
            import json
            firebase_dict = json.loads(firebase_json)
            cred = credentials.Certificate(firebase_dict)
            firebase_admin.initialize_app(cred)
            logger.info("Firebase Admin SDK initialized from environment variable")
            firebase_initialized = True
        except Exception as e:
            logger.warning("Failed to initialize Firebase from environment: %s", str(e))

if not firebase_initialized:
    logger.warning("Firebase credentials file not found. App Check will be disabled.")
    try:
        firebase_admin.initialize_app()
    except:
        pass

# Storage directories
UPLOAD_DIR = Path("/tmp/colorize_uploads")
RESULT_DIR = Path("/tmp/colorize_results")

# Mount static files
app.mount("/results", StaticFiles(directory=str(RESULT_DIR)), name="results")
app.mount("/uploads", StaticFiles(directory=str(UPLOAD_DIR)), name="uploads")

# Global Inference API client
inference_client = None
model_load_error: Optional[str] = None

# ========== Utility Functions ==========

def apply_color(image: Image.Image, color_map: Image.Image) -> Image.Image:
    """Apply color from color_map to image using LAB color space."""
    # Convert to LAB color space
    image_lab = image.convert('LAB')
    color_map_lab = color_map.convert('LAB')

    # Extract and merge LAB channels
    l, _, _ = image_lab.split()
    _, a_map, b_map = color_map_lab.split()
    merged_lab = Image.merge('LAB', (l, a_map, b_map))

    return merged_lab.convert('RGB')


def remove_unlikely_words(prompt: str) -> str:
    """Removes predefined unlikely phrases from prompt text."""
    unlikely_words = []

    a1 = [f'{i}s' for i in range(1900, 2000)]
    a2 = [f'{i}' for i in range(1900, 2000)]
    a3 = [f'year {i}' for i in range(1900, 2000)]
    a4 = [f'circa {i}' for i in range(1900, 2000)]

    b1 = [f"{y[0]} {y[1]} {y[2]} {y[3]} s" for y in a1]
    b2 = [f"{y[0]} {y[1]} {y[2]} {y[3]}" for y in a1]
    b3 = [f"year {y[0]} {y[1]} {y[2]} {y[3]}" for y in a1]
    b4 = [f"circa {y[0]} {y[1]} {y[2]} {y[3]}" for y in a1]

    manual = [
        "black and white,", "black and white", "black & white,", "black & white", "circa", 
        "balck and white,", "monochrome,", "black-and-white,", "black-and-white photography,", 
        "black - and - white photography,", "monochrome bw,", "black white,", "black an white,",
        "grainy footage,", "grainy footage", "grainy photo,", "grainy photo", "b&w photo",
        "back and white", "back and white,", "monochrome contrast", "monochrome", "grainy",
        "grainy photograph,", "grainy photograph", "low contrast,", "low contrast", "b & w",
        "grainy black-and-white photo,", "bw", "bw,", "grainy black-and-white photo",
        "b & w,", "b&w,", "b&w!,", "b&w", "black - and - white,", "bw photo,", "grainy  photo,",
        "black-and-white photo,", "black-and-white photo", "black - and - white photography",
        "b&w photo,", "monochromatic photo,", "grainy monochrome photo,", "monochromatic",
        "blurry photo,", "blurry,", "blurry photography,", "monochromatic photo",
        "black - and - white photograph,", "black - and - white photograph", "black on white,",
        "black on white", "black-and-white", "historical image,", "historical picture,", 
        "historical photo,", "historical photograph,", "archival photo,", "taken in the early",
        "taken in the late", "taken in the", "historic photograph,", "restored,", "restored", 
        "historical photo", "historical setting,",
        "historic photo,", "historic", "desaturated!!,", "desaturated!,", "desaturated,", "desaturated", 
        "taken in", "shot on leica", "shot on leica sl2", "sl2",
        "taken with a leica camera", "leica sl2", "leica", "setting", 
        "overcast day", "overcast weather", "slight overcast", "overcast", 
        "picture taken in", "photo taken in", 
        ", photo", ",  photo", ",   photo", ",    photo", ", photograph",
        ",,", ",,,", ",,,,", " ,", "  ,", "   ,", "    ,", 
    ]

    unlikely_words.extend(a1 + a2 + a3 + a4 + b1 + b2 + b3 + b4 + manual)

    for word in unlikely_words:
        prompt = prompt.replace(word, "")
    return prompt


# ========== Model Loading ==========

@app.on_event("startup")
async def startup_event():
    """Initialize Hugging Face Inference API client"""
    global inference_client, model_load_error
    
    try:
        logger.info("🔄 Initializing Hugging Face Inference API client...")
        
        # Get HF token from environment or settings
        hf_token = os.getenv("HF_TOKEN") or settings.HF_TOKEN
        if not hf_token:
            raise ValueError("HF_TOKEN environment variable is required for Inference API")
        
        # Initialize InferenceClient with fal-ai provider
        inference_client = InferenceClient(
            provider="fal-ai",
            api_key=hf_token,
        )
        
        logger.info("✅ Inference API client initialized successfully!")
        model_load_error = None
        
    except Exception as e:
        error_msg = str(e)
        logger.error(f"❌ Failed to initialize Inference API client: {error_msg}")
        model_load_error = error_msg
        # Don't raise - allow health check to work


@app.on_event("shutdown")
async def shutdown_event():
    """Cleanup on shutdown"""
    global inference_client
    if inference_client:
        inference_client = None
    logger.info("Application shutdown")


# ========== Authentication Models ==========

class RegisterRequest(BaseModel):
    email: EmailStr
    password: str
    display_name: Optional[str] = None

class LoginRequest(BaseModel):
    email: EmailStr
    password: str

class TokenResponse(BaseModel):
    id_token: str
    refresh_token: Optional[str] = None
    expires_in: int
    token_type: str = "Bearer"
    user: dict

# ========== Authentication ==========

def _extract_bearer_token(authorization_header: str | None) -> str | None:
    if not authorization_header:
        return None
    parts = authorization_header.split(" ", 1)
    if len(parts) == 2 and parts[0].lower() == "bearer":
        return parts[1].strip()
    return None


async def verify_request(request: Request):
    """
    Verify Firebase authentication.
    Priority:
    1. Firebase App Check token (X-Firebase-AppCheck header) - Primary method per documentation
    2. Firebase Auth ID token (Authorization: Bearer header) - Fallback for auth endpoints
    """
    if not firebase_admin._apps or os.getenv("DISABLE_AUTH", "false").lower() == "true":
        return True

    # Primary: Check Firebase App Check token (X-Firebase-AppCheck header)
    app_check_token = request.headers.get("X-Firebase-AppCheck")
    if app_check_token:
        try:
            app_check_claims = app_check.verify_token(app_check_token)
            logger.info("App Check token verified for: %s", app_check_claims.get("app_id"))
            return True
        except Exception as e:
            logger.warning("App Check token verification failed: %s", str(e))
            if settings.ENABLE_APP_CHECK:
                raise HTTPException(status_code=401, detail="Invalid App Check token")

    # Secondary: Check Firebase Auth ID token (Authorization: Bearer header)
    # This is for /auth/* endpoints that use email/password login
    bearer = _extract_bearer_token(request.headers.get("Authorization"))
    if bearer:
        try:
            decoded = firebase_auth.verify_id_token(bearer)
            request.state.user = decoded
            logger.info("Firebase Auth id_token verified for uid: %s", decoded.get("uid"))
            return True
        except Exception as e:
            logger.warning("Auth token verification failed: %s", str(e))

    # If App Check is enabled and no valid token provided, require it
    if settings.ENABLE_APP_CHECK:
        if not app_check_token:
            raise HTTPException(status_code=401, detail="Missing App Check token")
        raise HTTPException(status_code=401, detail="Invalid App Check token")

    # If auth is disabled, allow access
    return True


# ========== Auth Endpoints ==========

@app.post("/auth/register", response_model=TokenResponse)
async def register_user(user_data: RegisterRequest):
    """
    Register a new user with email and password.
    Returns Firebase ID token for immediate use.
    """
    if not firebase_admin._apps:
        raise HTTPException(status_code=503, detail="Firebase not initialized")
    
    try:
        # Create user using Firebase Admin SDK
        user_record = firebase_auth.create_user(
            email=user_data.email,
            password=user_data.password,
            display_name=user_data.display_name,
            email_verified=False
        )
        
        # Generate custom token that client can exchange for ID token
        custom_token = firebase_auth.create_custom_token(user_record.uid)
        
        logger.info("User registered: %s (uid: %s)", user_data.email, user_record.uid)
        
        return TokenResponse(
            id_token=custom_token.decode('utf-8'),  # Custom token (client should exchange)
            token_type="Bearer",
            expires_in=3600,
            user={
                "uid": user_record.uid,
                "email": user_record.email,
                "display_name": user_record.display_name,
                "email_verified": user_record.email_verified
            }
        )
    except firebase_auth.EmailAlreadyExistsError:
        raise HTTPException(status_code=400, detail="Email already registered")
    except ValueError as e:
        raise HTTPException(status_code=400, detail=f"Invalid input: {str(e)}")
    except Exception as e:
        logger.error("Registration error: %s", str(e))
        raise HTTPException(status_code=500, detail=f"Registration failed: {str(e)}")


@app.post("/auth/login", response_model=TokenResponse)
async def login_user(credentials: LoginRequest):
    """
    Login with email and password.
    Uses Firebase REST API to authenticate and get ID token.
    """
    if not firebase_admin._apps:
        raise HTTPException(status_code=503, detail="Firebase not initialized")
    
    # Firebase REST API endpoint for email/password authentication
    firebase_api_key = os.getenv("FIREBASE_API_KEY") or settings.FIREBASE_API_KEY
    if not firebase_api_key:
        # Fallback: verify user exists and return custom token
        try:
            user_record = firebase_auth.get_user_by_email(credentials.email)
            custom_token = firebase_auth.create_custom_token(user_record.uid)
            
            logger.info("User login: %s (uid: %s)", credentials.email, user_record.uid)
            
            return TokenResponse(
                id_token=custom_token.decode('utf-8'),
                token_type="Bearer",
                expires_in=3600,
                user={
                    "uid": user_record.uid,
                    "email": user_record.email,
                    "display_name": user_record.display_name,
                    "email_verified": user_record.email_verified
                }
            )
        except firebase_auth.UserNotFoundError:
            raise HTTPException(status_code=401, detail="Invalid email or password")
        except Exception as e:
            logger.error("Login error: %s", str(e))
            raise HTTPException(status_code=500, detail=f"Login failed: {str(e)}")
    
    # Use Firebase REST API for proper authentication
    try:
        async with httpx.AsyncClient() as client:
            response = await client.post(
                f"https://identitytoolkit.googleapis.com/v1/accounts:signInWithPassword?key={firebase_api_key}",
                json={
                    "email": credentials.email,
                    "password": credentials.password,
                    "returnSecureToken": True
                }
            )
            
            if response.status_code != 200:
                error_data = response.json()
                error_msg = error_data.get("error", {}).get("message", "Authentication failed")
                raise HTTPException(status_code=401, detail=error_msg)
            
            data = response.json()
            logger.info("User login successful: %s", credentials.email)
            
            # Get user details from Admin SDK
            user_record = firebase_auth.get_user(data["localId"])
            
            return TokenResponse(
                id_token=data["idToken"],
                refresh_token=data.get("refreshToken"),
                expires_in=int(data.get("expiresIn", 3600)),
                token_type="Bearer",
                user={
                    "uid": user_record.uid,
                    "email": user_record.email,
                    "display_name": user_record.display_name,
                    "email_verified": user_record.email_verified
                }
            )
    except httpx.HTTPError as e:
        logger.error("HTTP error during login: %s", str(e))
        raise HTTPException(status_code=500, detail="Authentication service unavailable")
    except Exception as e:
        logger.error("Login error: %s", str(e))
        raise HTTPException(status_code=500, detail=f"Login failed: {str(e)}")


@app.get("/auth/me")
async def get_current_user(request: Request, verified: bool = Depends(verify_request)):
    """Get current authenticated user information"""
    if not firebase_admin._apps:
        raise HTTPException(status_code=503, detail="Firebase not initialized")
    
    # Get user from request state (set by verify_request)
    if hasattr(request, 'state') and hasattr(request.state, 'user'):
        user_data = request.state.user
        uid = user_data.get("uid")
        
        try:
            user_record = firebase_auth.get_user(uid)
            return {
                "uid": user_record.uid,
                "email": user_record.email,
                "display_name": user_record.display_name,
                "email_verified": user_record.email_verified,
                "created_at": user_record.user_metadata.creation_timestamp,
            }
        except Exception as e:
            logger.error("Error getting user: %s", str(e))
            raise HTTPException(status_code=404, detail="User not found")
    
    raise HTTPException(status_code=401, detail="Not authenticated")


@app.post("/auth/refresh")
async def refresh_token(refresh_token: str = Body(..., embed=True)):
    """Refresh Firebase ID token using refresh token"""
    firebase_api_key = os.getenv("FIREBASE_API_KEY") or settings.FIREBASE_API_KEY
    if not firebase_api_key:
        raise HTTPException(status_code=503, detail="Firebase API key not configured")
    
    try:
        async with httpx.AsyncClient() as client:
            response = await client.post(
                f"https://securetoken.googleapis.com/v1/token?key={firebase_api_key}",
                json={
                    "grant_type": "refresh_token",
                    "refresh_token": refresh_token
                }
            )
            
            if response.status_code != 200:
                error_data = response.json()
                error_msg = error_data.get("error", {}).get("message", "Token refresh failed")
                raise HTTPException(status_code=401, detail=error_msg)
            
            data = response.json()
            return {
                "id_token": data["id_token"],
                "refresh_token": data.get("refresh_token"),
                "expires_in": int(data.get("expires_in", 3600)),
                "token_type": "Bearer"
            }
    except httpx.HTTPError as e:
        logger.error("HTTP error during token refresh: %s", str(e))
        raise HTTPException(status_code=500, detail="Token refresh service unavailable")
    except Exception as e:
        logger.error("Token refresh error: %s", str(e))
        raise HTTPException(status_code=500, detail=f"Token refresh failed: {str(e)}")


# ========== API Endpoints ==========

@app.get("/api")
async def api_info():
    """API info endpoint"""
    return {
        "app": "Text-Guided Image Colorization API",
        "version": "1.0.0",
        "endpoints": {
            "health": "/health",
            "upload": "/upload",
            "colorize": "/colorize",
            "download": "/download/{file_id}",
            "results": "/results/{filename}",
            "uploads": "/uploads/{filename}",
            "auth": {
                "register": "/auth/register",
                "login": "/auth/login",
                "me": "/auth/me",
                "refresh": "/auth/refresh"
            },
            "gradio": "/"
        }
    }


@app.get("/health")
async def health_check():
    """Health check endpoint"""
    response = {
        "status": "healthy",
        "model_loaded": inference_client is not None,
        "model_type": "hf_inference_api",
        "provider": "fal-ai"
    }
    if model_load_error:
        response["model_error"] = model_load_error
    return response


def colorize_image_sdxl(
    image: Image.Image,
    positive_prompt: Optional[str] = None,
    negative_prompt: Optional[str] = None,
    seed: int = 123,
    num_inference_steps: int = 8
) -> Tuple[Image.Image, str]:
    """
    Colorize a grayscale or low-color image using Hugging Face Inference API.
    
    Args:
        image: PIL Image to colorize
        positive_prompt: Additional descriptive text to enhance the caption
        negative_prompt: Words or phrases to avoid during generation
        seed: Random seed for reproducible generation
        num_inference_steps: Number of inference steps
        
    Returns:
        Tuple of (colorized PIL Image, caption string)
    """
    if inference_client is None:
        raise RuntimeError("Inference API client not initialized")
    
    original_size = image.size
    # Resize to 512x512 for inference (FLUX models work well at this size)
    control_image = image.convert("RGB").resize((512, 512))
    
    # Convert image to bytes for API
    img_bytes = io.BytesIO()
    control_image.save(img_bytes, format="PNG")
    img_bytes.seek(0)
    input_image = img_bytes.read()
    
    # Construct prompt
    base_prompt = positive_prompt or "colorize this image with vibrant natural colors, high quality"
    if negative_prompt:
        # Note: Some models may not support negative_prompt directly
        final_prompt = f"{base_prompt}. Avoid: {negative_prompt}"
    else:
        final_prompt = base_prompt
    
    # Use Inference API for image-to-image generation
    model_name = settings.INFERENCE_MODEL
    logger.info(f"Calling Inference API with model {model_name}, prompt: {final_prompt}")
    try:
        result_image = inference_client.image_to_image(
            input_image,
            prompt=final_prompt,
            model=model_name,
        )
        
        # Resize back to original size
        if isinstance(result_image, Image.Image):
            colorized = result_image.resize(original_size)
        else:
            # If it's bytes, convert to PIL Image
            colorized = Image.open(io.BytesIO(result_image)).resize(original_size)
        
        # Generate a simple caption from the prompt
        caption = final_prompt[:100]  # Truncate for display
        
        return colorized, caption
        
    except Exception as e:
        logger.error(f"Inference API error: {e}")
        raise RuntimeError(f"Failed to colorize image: {str(e)}")


@app.post("/upload")
async def upload_image(
    file: UploadFile = File(...),
    verified: bool = Depends(verify_request)
):
    """
    Upload an image and get the uploaded image URL.
    Requires Firebase App Check authentication.
    """
    if not file.content_type or not file.content_type.startswith("image/"):
        raise HTTPException(status_code=400, detail="File must be an image")
    
    try:
        # Generate unique filename
        file_extension = file.filename.split('.')[-1] if file.filename else 'jpg'
        image_id = f"{uuid.uuid4()}.{file_extension}"
        file_path = UPLOAD_DIR / image_id
        
        # Save uploaded file
        img_bytes = await file.read()
        with open(file_path, "wb") as f:
            f.write(img_bytes)
        
        logger.info("Image uploaded: %s", image_id)
        
        # Get base URL from settings or environment
        base_url = os.getenv("BASE_URL", settings.BASE_URL)
        if not base_url or base_url == "http://localhost:8000":
            # Try to get from request
            base_url = "https://logicgoinfotechspaces-text-guided-image-colorization.hf.space"
        
        return JSONResponse({
            "success": True,
            "image_id": image_id.replace(f".{file_extension}", ""),
            "image_url": f"{base_url}/uploads/{image_id}",
            "filename": image_id
        })
    except Exception as e:
        logger.error("Error uploading image: %s", str(e))
        raise HTTPException(status_code=500, detail=f"Error uploading image: {str(e)}")


@app.post("/colorize")
async def colorize_api(
    file: UploadFile = File(...),
    positive_prompt: Optional[str] = None,
    negative_prompt: Optional[str] = None,
    seed: int = 123,
    num_inference_steps: int = 8,
    verified: bool = Depends(verify_request)
):
    """
    Upload a grayscale image -> returns colorized image.
    Uses SDXL + ControlNet with automatic captioning.
    """
    if inference_client is None:
        raise HTTPException(status_code=503, detail="Inference API client not initialized")
    
    if not file.content_type or not file.content_type.startswith("image/"):
        raise HTTPException(status_code=400, detail="File must be an image")
    
    try:
        img_bytes = await file.read()
        image = Image.open(io.BytesIO(img_bytes)).convert("RGB")
        
        logger.info("Colorizing image with SDXL + ControlNet...")
        colorized, caption = colorize_image_sdxl(
            image,
            positive_prompt=positive_prompt,
            negative_prompt=negative_prompt,
            seed=seed,
            num_inference_steps=num_inference_steps
        )
        
        output_filename = f"{uuid.uuid4()}.png"
        output_path = RESULT_DIR / output_filename
        colorized.save(output_path, "PNG")
        
        logger.info("Colorized image saved: %s", output_filename)
        
        # Get base URL from settings or environment
        base_url = os.getenv("BASE_URL", settings.BASE_URL)
        if not base_url or base_url == "http://localhost:8000":
            base_url = "https://logicgoinfotechspaces-text-guided-image-colorization.hf.space"
        
        result_id = output_filename.replace(".png", "")
        
        return JSONResponse({
            "success": True,
            "result_id": result_id,
            "download_url": f"{base_url}/results/{output_filename}",
            "api_download_url": f"{base_url}/download/{result_id}",
            "filename": output_filename,
            "caption": caption
        })
    except Exception as e:
        logger.error("Error colorizing image: %s", str(e))
        raise HTTPException(status_code=500, detail=f"Error colorizing image: {str(e)}")


@app.get("/download/{file_id}")
def download_result(file_id: str, verified: bool = Depends(verify_request)):
    """Download colorized image by file ID"""
    filename = f"{file_id}.png"
    path = RESULT_DIR / filename
    
    if not path.exists():
        raise HTTPException(status_code=404, detail="Result not found")
    
    return FileResponse(path, media_type="image/png")


@app.get("/results/{filename}")
def get_result(filename: str):
    """Public endpoint to access colorized images"""
    path = RESULT_DIR / filename
    if not path.exists():
        raise HTTPException(status_code=404, detail="Result not found")
    return FileResponse(path, media_type="image/png")


# ========== Gradio Interface (Optional) ==========

def gradio_colorize(image, positive_prompt=None, negative_prompt=None, seed=123):
    """Gradio colorization function"""
    if image is None:
        return None, ""
    try:
        if inference_client is None:
            return None, "Inference API client not initialized"
        colorized, caption = colorize_image_sdxl(
            image,
            positive_prompt=positive_prompt,
            negative_prompt=negative_prompt,
            seed=seed
        )
        return colorized, caption
    except Exception as e:
        logger.error("Gradio colorization error: %s", str(e))
        return None, str(e)


title = "🎨 Text-Guided Image Colorization"
description = "Upload a grayscale image and generate a color version using Hugging Face Inference API (fal-ai provider)."

iface = gr.Interface(
    fn=gradio_colorize,
    inputs=[
        gr.Image(type="pil", label="Upload Image"),
        gr.Textbox(label="Positive Prompt", placeholder="Enter details to enhance the caption"),
        gr.Textbox(label="Negative Prompt", value=settings.NEGATIVE_PROMPT),
        gr.Slider(0, 1000, 123, label="Seed")
    ],
    outputs=[
        gr.Image(type="pil", label="Colorized Image"),
        gr.Textbox(label="Caption", show_copy_button=True)
    ],
    title=title,
    description=description,
)

# Mount Gradio app at root
app = gr.mount_gradio_app(app, iface, path="/")


# ========== Run Server ==========

if __name__ == "__main__":
    port = int(os.getenv("PORT", "7860"))
    uvicorn.run(app, host="0.0.0.0", port=port)