media_clicks_app.py

#####################FASTAPI___________________##############
import os
os.environ["OMP_NUM_THREADS"] = "1"
import shutil
import uuid
import cv2
import numpy as np
import threading
import asyncio
import subprocess
import logging
import tempfile
import sys
import time
from datetime import datetime,timedelta
import tempfile
import insightface
from insightface.app import FaceAnalysis
from huggingface_hub import hf_hub_download
from fastapi import FastAPI, UploadFile, File, HTTPException, Response, Depends, Security, Form
from fastapi.responses import RedirectResponse
from fastapi.security import HTTPBearer, HTTPAuthorizationCredentials
from motor.motor_asyncio import AsyncIOMotorClient
from bson import ObjectId
from bson.errors import InvalidId
import httpx
import uvicorn
from PIL import Image
import io
import requests
# DigitalOcean Spaces
import boto3
from botocore.client import Config
from typing import Optional

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

# --------------------- Secrets & Paths ---------------------
REPO_ID = "HariLogicgo/face_swap_models"
MODELS_DIR = "./models"
os.makedirs(MODELS_DIR, exist_ok=True)

HF_TOKEN = os.getenv("HF_TOKEN")
API_SECRET_TOKEN = os.getenv("API_SECRET_TOKEN")

DO_SPACES_REGION = os.getenv("DO_SPACES_REGION", "blr1")
DO_SPACES_ENDPOINT = f"https://{DO_SPACES_REGION}.digitaloceanspaces.com"
DO_SPACES_KEY = os.getenv("DO_SPACES_KEY")
DO_SPACES_SECRET = os.getenv("DO_SPACES_SECRET")
DO_SPACES_BUCKET = os.getenv("DO_SPACES_BUCKET")

# NEW admin DB (with error handling for missing env vars)
ADMIN_MONGO_URL = os.getenv("ADMIN_MONGO_URL")
admin_client = None
admin_db = None
subcategories_col = None
if ADMIN_MONGO_URL:
    try:
        admin_client = AsyncIOMotorClient(ADMIN_MONGO_URL)
        admin_db = admin_client.adminPanel
        subcategories_col = admin_db.subcategories
    except Exception as e:
        logger.warning(f"MongoDB admin connection failed (optional): {e}")

# Collage Maker DB (optional)
COLLAGE_MAKER_DB_URL = os.getenv("COLLAGE_MAKER_DB_URL")
collage_maker_client = None
collage_maker_db = None
collage_subcategories_col = None
if COLLAGE_MAKER_DB_URL:
    try:
        collage_maker_client = AsyncIOMotorClient(COLLAGE_MAKER_DB_URL)
        collage_maker_db = collage_maker_client.adminPanel
        collage_subcategories_col = collage_maker_db.subcategories
    except Exception as e:
        logger.warning(f"MongoDB collage-maker connection failed (optional): {e}")
        
# AI Enhancer DB (optional)

AI_ENHANCER_DB_URL = os.getenv("AI_ENHANCER_DB_URL")
ai_enhancer_client = None
ai_enhancer_db = None
ai_enhancer_subcategories_col = None

if AI_ENHANCER_DB_URL:
    try:
        ai_enhancer_client = AsyncIOMotorClient(AI_ENHANCER_DB_URL)
        ai_enhancer_db = ai_enhancer_client.test   # 🔴 test database
        ai_enhancer_subcategories_col = ai_enhancer_db.subcategories
    except Exception as e:
        logger.warning(f"MongoDB ai-enhancer connection failed (optional): {e}")


# OLD logs DB
MONGODB_URL = os.getenv("MONGODB_URL_LOGS")
client = None
database = None

# --------------------- Download Models ---------------------
def download_models():
    try:
        logger.info("Downloading models...")
        inswapper_path = hf_hub_download(
            repo_id=REPO_ID,
            filename="models/inswapper_128.onnx",
            repo_type="model",
            local_dir=MODELS_DIR,
            token=HF_TOKEN
        )

        buffalo_files = ["1k3d68.onnx", "2d106det.onnx", "genderage.onnx", "det_10g.onnx", "w600k_r50.onnx"]
        for f in buffalo_files:
            hf_hub_download(
                repo_id=REPO_ID,
                filename=f"models/buffalo_l/" + f,
                repo_type="model",
                local_dir=MODELS_DIR,
                token=HF_TOKEN
            )

        logger.info("Models downloaded successfully.")
        return inswapper_path
    except Exception as e:
        logger.error(f"Model download failed: {e}")
        raise

try:
    inswapper_path = download_models()
    
    # --------------------- Face Analysis + Swapper ---------------------
    providers = ['CUDAExecutionProvider', 'CPUExecutionProvider']
    face_analysis_app = FaceAnalysis(name="buffalo_l", root=MODELS_DIR, providers=providers)
    face_analysis_app.prepare(ctx_id=0, det_size=(640, 640))
    swapper = insightface.model_zoo.get_model(inswapper_path, providers=providers)
    logger.info("Face analysis models loaded successfully")
except Exception as e:
    logger.error(f"Failed to initialize face analysis models: {e}")
    # Set defaults to prevent crash
    inswapper_path = None
    face_analysis_app = None
    swapper = None

# --------------------- CodeFormer ---------------------
CODEFORMER_PATH = "CodeFormer/inference_codeformer.py"

def ensure_codeformer():
    """
    Ensure CodeFormer's local basicsr + facelib are importable and
    pretrained weights are downloaded. No setup.py needed — we use
    sys.path / PYTHONPATH instead.
    """
    try:
        if not os.path.exists("CodeFormer"):
            logger.info("CodeFormer not found, cloning repository...")
            subprocess.run("git clone https://github.com/sczhou/CodeFormer.git", shell=True, check=True)
            subprocess.run("pip install -r CodeFormer/requirements.txt", shell=True, check=False)
        
        # Add CodeFormer root to sys.path so `import basicsr` and
        # `import facelib` resolve to the local (compatible) versions
        # instead of the broken PyPI basicsr==1.4.2.
        codeformer_root = os.path.join(os.getcwd(), "CodeFormer")
        if codeformer_root not in sys.path:
            sys.path.insert(0, codeformer_root)
            logger.info(f"Added {codeformer_root} to sys.path for local basicsr/facelib")
        
        # NOTE: We do NOT need the PyPI 'realesrgan' package.
        # Both in-process and subprocess paths use CodeFormer's local
        # basicsr.utils.realesrgan_utils.RealESRGANer instead.
        # Installing PyPI realesrgan at runtime would re-install the
        # broken basicsr==1.4.2 and break everything.
        
        # Download pretrained weights if not already present
        if os.path.exists("CodeFormer"):
            try:
                subprocess.run("python CodeFormer/scripts/download_pretrained_models.py facelib", shell=True, check=False, timeout=300)
            except (subprocess.TimeoutExpired, subprocess.CalledProcessError):
                logger.warning("Failed to download facelib models (optional)")
            try:
                subprocess.run("python CodeFormer/scripts/download_pretrained_models.py CodeFormer", shell=True, check=False, timeout=300)
            except (subprocess.TimeoutExpired, subprocess.CalledProcessError):
                logger.warning("Failed to download CodeFormer models (optional)")
    except Exception as e:
        logger.error(f"CodeFormer setup failed: {e}")
        logger.warning("Continuing without CodeFormer features...")

ensure_codeformer()

# --------------------- In-Process CodeFormer (No Subprocess!) ---------------------
# Load CodeFormer models ONCE at startup instead of spawning a new Python process per request.
# This eliminates 15-40s of model loading overhead per request.

codeformer_net = None
codeformer_upsampler = None
codeformer_face_helper = None
codeformer_device = None

def init_codeformer_in_process():
    """Load CodeFormer models once into memory for fast per-request inference."""
    global codeformer_net, codeformer_upsampler, codeformer_face_helper, codeformer_device
    try:
        import torch
        from torchvision.transforms.functional import normalize as torch_normalize
        
        # Add CodeFormer to Python path
        codeformer_root = os.path.join(os.getcwd(), "CodeFormer")
        if codeformer_root not in sys.path:
            sys.path.insert(0, codeformer_root)
        
        from basicsr.utils.registry import ARCH_REGISTRY
        from basicsr.utils.download_util import load_file_from_url
        from facelib.utils.face_restoration_helper import FaceRestoreHelper
        
        codeformer_device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
        logger.info(f"Initializing CodeFormer on device: {codeformer_device}")
        
        # 1) Load CodeFormer network
        net = ARCH_REGISTRY.get('CodeFormer')(
            dim_embd=512, codebook_size=1024, n_head=8, n_layers=9,
            connect_list=['32', '64', '128', '256']
        ).to(codeformer_device)
        
        ckpt_path = load_file_from_url(
            url='https://github.com/sczhou/CodeFormer/releases/download/v0.1.0/codeformer.pth',
            model_dir='weights/CodeFormer', progress=True, file_name=None
        )
        checkpoint = torch.load(ckpt_path, map_location=codeformer_device)['params_ema']
        net.load_state_dict(checkpoint)
        net.eval()
        codeformer_net = net
        
        # 2) RealESRGAN upsampler — SKIPPED for face swap
        # Background/face upsampling is the #1 bottleneck (~20s per image).
        # For face swap we only need CodeFormer face restoration, not super-resolution.
        # The upsampler is kept as None; we no longer download the 64MB model at startup.
        codeformer_upsampler = None
        
        # 3) Create FaceRestoreHelper (reused per request)
        # NOTE: local CodeFormer uses "upscale_factor" (not "upscale")
        # upscale_factor=1 → keep original resolution (no 2x upscale needed for face swap)
        codeformer_face_helper = FaceRestoreHelper(
            upscale_factor=1,
            face_size=512,
            crop_ratio=(1, 1),
            det_model='retinaface_resnet50',
            save_ext='png',
            use_parse=True,
            device=codeformer_device
        )
        
        logger.info("✅ CodeFormer models loaded in-process successfully!")
        return True
    except Exception as e:
        logger.error(f"Failed to load CodeFormer in-process: {e}")
        logger.warning("CodeFormer enhancement will be unavailable.")
        return False

# Try to load CodeFormer models in-process
_codeformer_available = init_codeformer_in_process()
# --------------------- FastAPI ---------------------
fastapi_app = FastAPI()

@fastapi_app.on_event("startup")
async def startup_db():
    global client, database
    if MONGODB_URL:
        try:
            logger.info("Initializing MongoDB for API logs...")
            client = AsyncIOMotorClient(MONGODB_URL)
            database = client.logs
            logger.info("MongoDB initialized for API logs")
        except Exception as e:
            logger.warning(f"MongoDB connection failed (optional): {e}")
            client = None
            database = None
    else:
        logger.warning("MONGODB_URL not set, skipping MongoDB initialization")

@fastapi_app.on_event("shutdown")
async def shutdown_db():
    global client, admin_client, collage_maker_client
    if client is not None:
        client.close()
        logger.info("MongoDB connection closed")
    if admin_client is not None:
        admin_client.close()
        logger.info("Admin MongoDB connection closed")
    if collage_maker_client is not None:
        collage_maker_client.close()
        logger.info("Collage Maker MongoDB connection closed")

# --------------------- Auth ---------------------
security = HTTPBearer()

def verify_token(credentials: HTTPAuthorizationCredentials = Security(security)):
    if credentials.credentials != API_SECRET_TOKEN:
        raise HTTPException(status_code=401, detail="Invalid or missing token")
    return credentials.credentials

# --------------------- DB Selector ---------------------
def get_app_db_collections(appname: Optional[str] = None):
    """
    Returns subcategories_collection based on appname.
    """

    if appname:
        app = appname.strip().lower()

        if app == "collage-maker":
            if collage_subcategories_col is not None:
                return collage_subcategories_col
            logger.warning("Collage-maker DB not configured, falling back to admin")

        elif app == "ai-enhancer":
            if ai_enhancer_subcategories_col is not None:
                return ai_enhancer_subcategories_col
            logger.warning("AI-Enhancer DB not configured, falling back to admin")

    # default fallback
    return subcategories_col



# --------------------- Logging API Hits ---------------------
async def log_faceswap_hit(token: str, status: str = "success"):
    global database
    if database is None:
        return
    await database.faceswap.insert_one({
        "token": token,
        "endpoint": "/faceswap",
        "status": status,
        "timestamp": datetime.utcnow()
    })

# --------------------- Face Swap Pipeline ---------------------
swap_lock = threading.Lock()

def enhance_image_with_codeformer(rgb_img, temp_dir=None, w=0.7):
    """
    Enhance face image using CodeFormer.
    Uses in-process models (fast) if available, falls back to subprocess (slow).
    """
    global codeformer_net, codeformer_upsampler, codeformer_face_helper, codeformer_device
    
    t0 = time.time()
    
    # ── FAST PATH: In-process CodeFormer (no subprocess!) ──
    if codeformer_net is not None and codeformer_face_helper is not None:
        import torch
        from torchvision.transforms.functional import normalize as torch_normalize
        from basicsr.utils import img2tensor, tensor2img
        from facelib.utils.misc import is_gray
        
        bgr_img = cv2.cvtColor(rgb_img, cv2.COLOR_RGB2BGR)
        
        # Reset face helper state
        codeformer_face_helper.clean_all()
        codeformer_face_helper.read_image(bgr_img)
        
        num_faces = codeformer_face_helper.get_face_landmarks_5(
            only_center_face=False, resize=640, eye_dist_threshold=5
        )
        logger.info(f"[CodeFormer] Detected {num_faces} faces in {time.time()-t0:.2f}s")
        
        codeformer_face_helper.align_warp_face()
        
        # Enhance each cropped face with CodeFormer neural net
        t_faces = time.time()
        for idx, cropped_face in enumerate(codeformer_face_helper.cropped_faces):
            cropped_face_t = img2tensor(cropped_face / 255., bgr2rgb=True, float32=True)
            torch_normalize(cropped_face_t, (0.5, 0.5, 0.5), (0.5, 0.5, 0.5), inplace=True)
            cropped_face_t = cropped_face_t.unsqueeze(0).to(codeformer_device)
            
            try:
                with torch.no_grad():
                    output = codeformer_net(cropped_face_t, w=w, adain=True)[0]
                    restored_face = tensor2img(output, rgb2bgr=True, min_max=(-1, 1))
                del output
                torch.cuda.empty_cache()
            except Exception as e:
                logger.warning(f"[CodeFormer] Face {idx} inference failed: {e}")
                restored_face = tensor2img(cropped_face_t, rgb2bgr=True, min_max=(-1, 1))
            
            restored_face = restored_face.astype('uint8')
            codeformer_face_helper.add_restored_face(restored_face, cropped_face)
        logger.info(f"[CodeFormer] Face restoration ({num_faces} faces): {time.time()-t_faces:.2f}s")
        
        # Paste restored faces back onto original image
        # NOTE: We skip RealESRGAN background/face upsampling — it's the #1 bottleneck
        # (~20s) and unnecessary for face swap. We only need CodeFormer face restoration.
        t_paste = time.time()
        codeformer_face_helper.get_inverse_affine(None)
        restored_img = codeformer_face_helper.paste_faces_to_input_image(
            upsample_img=None, draw_box=False
        )
        logger.info(f"[CodeFormer] Paste back: {time.time()-t_paste:.2f}s")
        
        logger.info(f"[CodeFormer] In-process enhancement done in {time.time()-t0:.2f}s")
        return cv2.cvtColor(restored_img, cv2.COLOR_BGR2RGB)
    
    # ── SLOW FALLBACK: Subprocess CodeFormer (with timeout!) ──
    logger.warning("[CodeFormer] In-process models unavailable, falling back to subprocess")
    if temp_dir is None:
        temp_dir = os.path.join(tempfile.gettempdir(), f"enhance_{uuid.uuid4().hex[:8]}")
    os.makedirs(temp_dir, exist_ok=True)

    input_path = os.path.join(temp_dir, "input.jpg")
    cv2.imwrite(input_path, cv2.cvtColor(rgb_img, cv2.COLOR_RGB2BGR))

    python_cmd = sys.executable if sys.executable else "python3"
    cmd = (
        f"{python_cmd} {CODEFORMER_PATH} "
        f"-w {w} "
        f"--input_path {input_path} "
        f"--output_path {temp_dir} "
        f"--bg_upsampler None "
        f"--upscale 1"
    )

    result = subprocess.run(cmd, shell=True, capture_output=True, text=True, timeout=120)
    if result.returncode != 0:
        raise RuntimeError(result.stderr)

    final_dir = os.path.join(temp_dir, "final_results")
    files = [f for f in os.listdir(final_dir) if f.endswith(".png")]
    if not files:
        raise RuntimeError("No enhanced output")

    final_path = os.path.join(final_dir, files[0])
    enhanced = cv2.imread(final_path)
    logger.info(f"[CodeFormer] Subprocess enhancement done in {time.time()-t0:.2f}s")
    return cv2.cvtColor(enhanced, cv2.COLOR_BGR2RGB)
    
def multi_face_swap(src_img, tgt_img):
    pipeline_start = time.time()
    src_bgr = cv2.cvtColor(src_img, cv2.COLOR_RGB2BGR)
    tgt_bgr = cv2.cvtColor(tgt_img, cv2.COLOR_RGB2BGR)

    t0 = time.time()
    src_faces = face_analysis_app.get(src_bgr)
    tgt_faces = face_analysis_app.get(tgt_bgr)
    logger.info(f"[Pipeline] Multi-face detection: {time.time()-t0:.2f}s")

    if not src_faces or not tgt_faces:
        raise ValueError("No faces detected")

    def face_sort_key(face):
        x1, y1, x2, y2 = face.bbox
        area = (x2 - x1) * (y2 - y1)
        cx = (x1 + x2) / 2
        return (-area, cx)

    src_male = sorted([f for f in src_faces if f.gender == 1], key=face_sort_key)
    src_female = sorted([f for f in src_faces if f.gender == 0], key=face_sort_key)
    tgt_male = sorted([f for f in tgt_faces if f.gender == 1], key=face_sort_key)
    tgt_female = sorted([f for f in tgt_faces if f.gender == 0], key=face_sort_key)

    pairs = []
    for s, t in zip(src_male, tgt_male):
        pairs.append((s, t))
    for s, t in zip(src_female, tgt_female):
        pairs.append((s, t))

    if not pairs:
        src_faces = sorted(src_faces, key=face_sort_key)
        tgt_faces = sorted(tgt_faces, key=face_sort_key)
        pairs = list(zip(src_faces, tgt_faces))

    t0 = time.time()
    result_img = tgt_bgr.copy()
    for src_face, _ in pairs:
        if face_analysis_app is None:
            raise ValueError("Face analysis models not initialized.")
        current_faces = sorted(face_analysis_app.get(result_img), key=face_sort_key)
        candidates = [f for f in current_faces if f.gender == src_face.gender] or current_faces
        target_face = candidates[0]

        if swapper is None:
            raise ValueError("Face swap models not initialized.")
        result_img = swapper.get(result_img, target_face, src_face, paste_back=True)
    logger.info(f"[Pipeline] Multi-face swap ({len(pairs)} pairs): {time.time()-t0:.2f}s")

    logger.info(f"[Pipeline] TOTAL multi_face_swap: {time.time()-pipeline_start:.2f}s")
    return cv2.cvtColor(result_img, cv2.COLOR_BGR2RGB)



def face_swap_and_enhance(src_img, tgt_img, temp_dir=None):
    try:
        with swap_lock:
            pipeline_start = time.time()
            
            if temp_dir is None:
                temp_dir = os.path.join(tempfile.gettempdir(), f"faceswap_work_{uuid.uuid4().hex[:8]}")
            if os.path.exists(temp_dir):
                shutil.rmtree(temp_dir)
            os.makedirs(temp_dir, exist_ok=True)

            if face_analysis_app is None:
                return None, None, "❌ Face analysis models not initialized."
            if swapper is None:
                return None, None, "❌ Face swap models not initialized."

            src_bgr = cv2.cvtColor(src_img, cv2.COLOR_RGB2BGR)
            tgt_bgr = cv2.cvtColor(tgt_img, cv2.COLOR_RGB2BGR)

            t0 = time.time()
            src_faces = face_analysis_app.get(src_bgr)
            tgt_faces = face_analysis_app.get(tgt_bgr)
            logger.info(f"[Pipeline] Face detection: {time.time()-t0:.2f}s")
            
            if not src_faces or not tgt_faces:
                return None, None, "❌ Face not detected in one of the images"

            t0 = time.time()
            swapped_bgr = swapper.get(tgt_bgr, tgt_faces[0], src_faces[0])
            logger.info(f"[Pipeline] Face swap: {time.time()-t0:.2f}s")
            
            if swapped_bgr is None:
                return None, None, "❌ Face swap failed"

            # Use in-process CodeFormer enhancement (fast path)
            t0 = time.time()
            swapped_rgb = cv2.cvtColor(swapped_bgr, cv2.COLOR_BGR2RGB)
            try:
                enhanced_rgb = enhance_image_with_codeformer(swapped_rgb)
                enhanced_bgr = cv2.cvtColor(enhanced_rgb, cv2.COLOR_RGB2BGR)
            except Exception as e:
                logger.error(f"[Pipeline] CodeFormer failed, using raw swap: {e}")
                enhanced_bgr = swapped_bgr
            logger.info(f"[Pipeline] Enhancement: {time.time()-t0:.2f}s")

            final_path = os.path.join(temp_dir, f"result_{uuid.uuid4().hex[:8]}.png")
            cv2.imwrite(final_path, enhanced_bgr)
            
            final_img = cv2.cvtColor(enhanced_bgr, cv2.COLOR_BGR2RGB)
            
            logger.info(f"[Pipeline] TOTAL face_swap_and_enhance: {time.time()-pipeline_start:.2f}s")
            return final_img, final_path, ""

    except Exception as e:
        return None, None, f"❌ Error: {str(e)}"
        
def compress_image(
    image_bytes: bytes,
    max_size=(1280, 1280),   # max width/height
    quality=75               # JPEG quality (60–80 is ideal)
) -> bytes:
    """
    Compress image by resizing and lowering quality.
    Returns compressed image bytes.
    """
    img = Image.open(io.BytesIO(image_bytes)).convert("RGB")

    # Resize while maintaining aspect ratio
    img.thumbnail(max_size, Image.LANCZOS)

    output = io.BytesIO()
    img.save(
        output,
        format="JPEG",
        quality=quality,
        optimize=True,
        progressive=True
    )

    return output.getvalue()

# --------------------- DigitalOcean Spaces Helper ---------------------
def get_spaces_client():
    session = boto3.session.Session()
    client = session.client(
        's3',
        region_name=DO_SPACES_REGION,
        endpoint_url=DO_SPACES_ENDPOINT,
        aws_access_key_id=DO_SPACES_KEY,
        aws_secret_access_key=DO_SPACES_SECRET,
        config=Config(signature_version='s3v4')
    )
    return client

def upload_to_spaces(file_bytes, key, content_type="image/png"):
    client = get_spaces_client()
    client.put_object(Bucket=DO_SPACES_BUCKET, Key=key, Body=file_bytes, ContentType=content_type, ACL='public-read')
    return f"{DO_SPACES_ENDPOINT}/{DO_SPACES_BUCKET}/{key}"

def download_from_spaces(key):
    client = get_spaces_client()
    obj = client.get_object(Bucket=DO_SPACES_BUCKET, Key=key)
    return obj['Body'].read()

def mandatory_enhancement(rgb_img):
    """
    Always runs CodeFormer on the final image.
    Fail-safe: returns original if enhancement fails.
    """
    try:
        return enhance_image_with_codeformer(rgb_img)
    except Exception as e:
        logger.error(f"CodeFormer failed, returning original: {e}")
        return rgb_img

# --------------------- API Endpoints ---------------------
@fastapi_app.get("/")
async def root():
    """Root endpoint"""
    return {
        "success": True,
        "message": "FaceSwap API",
        "data": {
            "version": "1.0.0",
            "Product Name":"Beauty Camera - GlowCam AI Studio",
            "Released By" : "LogicGo Infotech"
        }
    }
@fastapi_app.get("/health")
async def health():
    return {"status": "healthy"}

@fastapi_app.get("/test-admin-db")
async def test_admin_db():
    try:
        doc = await admin_db.list_collection_names()
        return {"ok": True, "collections": doc}
    except Exception as e:
        return {"ok": False, "error": str(e), "url": ADMIN_MONGO_URL}

@fastapi_app.post("/face-swap", dependencies=[Depends(verify_token)])
async def face_swap_api(
    source: UploadFile = File(...),
    image2: Optional[UploadFile] = File(None),
    target_category_id: str = Form(None),
    new_category_id: str = Form(None),
    user_id: Optional[str] = Form(None),
    appname: Optional[str] = Form(None),
    credentials: HTTPAuthorizationCredentials = Security(security)
):
    start_time = datetime.utcnow()

    try:
        # ------------------------------------------------------------------
        # VALIDATION
        # ------------------------------------------------------------------
        # --------------------------------------------------------------
        # BACKWARD COMPATIBILITY FOR OLD ANDROID VERSIONS
        # --------------------------------------------------------------
        if target_category_id == "":
            target_category_id = None
        
        if new_category_id == "":
            new_category_id = None
        
        if user_id == "":
            user_id = None

        subcategories_collection = get_app_db_collections(appname)

        logger.info(f"[FaceSwap] Incoming request → target_category_id={target_category_id}, new_category_id={new_category_id}, user_id={user_id}")

        if target_category_id and new_category_id:
            raise HTTPException(400, "Provide only one of new_category_id or target_category_id.")

        if not target_category_id and not new_category_id:
            raise HTTPException(400, "Either new_category_id or target_category_id is required.")

        # ------------------------------------------------------------------
        # READ SOURCE IMAGE
        # ------------------------------------------------------------------
        src_bytes = await source.read()
        src_key = f"faceswap/source/{uuid.uuid4().hex}_{source.filename}"
        upload_to_spaces(src_bytes, src_key, content_type=source.content_type)

        # ------------------------------------------------------------------
        # CASE 1 : new_category_id → MongoDB lookup
        # ------------------------------------------------------------------
        if new_category_id:

            # doc = await subcategories_col.find_one({
            #     "asset_images._id": ObjectId(new_category_id)
            # })
            doc = await subcategories_collection.find_one({
                "asset_images._id": ObjectId(new_category_id)
            })


            if not doc:
                raise HTTPException(404, "Asset image not found in database")

            # extract correct asset
            asset = next(
                (img for img in doc["asset_images"] if str(img["_id"]) == new_category_id),
                None
            )

            if not asset:
                raise HTTPException(404, "Asset image URL not found")

            # correct URL
            target_url = asset["url"]

            # correct categoryId (ObjectId)
            #category_oid = doc["categoryId"]   # <-- DO NOT CONVERT TO STRING
            subcategory_oid = doc["_id"]

#         # ------------------------------------------------------------------
#         # CASE 2 : target_category_id → DigitalOcean path (unchanged logic)
#         # ------------------------------------------------------------------
        if target_category_id:
            client = get_spaces_client()
            base_prefix = "faceswap/target/"
            resp = client.list_objects_v2(
                Bucket=DO_SPACES_BUCKET, Prefix=base_prefix, Delimiter="/"
            )
            
            # Extract categories from the CommonPrefixes
            categories = [p["Prefix"].split("/")[2] for p in resp.get("CommonPrefixes", [])]

            target_url = None
            
            # --- FIX STARTS HERE ---
            for category in categories:
                original_prefix = f"faceswap/target/{category}/original/"
                thumb_prefix = f"faceswap/target/{category}/thumb/" # Keep for file list check (optional but safe)
                
                # List objects in original/
                original_objects = client.list_objects_v2(
                    Bucket=DO_SPACES_BUCKET, Prefix=original_prefix
                ).get("Contents", [])

                # List objects in thumb/ (optional: for the old code's extra check)
                thumb_objects = client.list_objects_v2(
                    Bucket=DO_SPACES_BUCKET, Prefix=thumb_prefix
                ).get("Contents", [])
                
                # Extract only the filenames and filter for .png
                original_filenames = sorted([
                    obj["Key"].split("/")[-1] for obj in original_objects 
                    if obj["Key"].split("/")[-1].endswith(".png")
                ])
                thumb_filenames = [
                    obj["Key"].split("/")[-1] for obj in thumb_objects
                ]

                # Replicate the old indexing logic based on sorted filenames
                for idx, filename in enumerate(original_filenames, start=1):
                    cid = f"{category.lower()}image_{idx}"

                    # Optional: Replicate the thumb file check for 100% parity
                    # if filename in thumb_filenames and cid == target_category_id:
                    # Simpler check just on the ID, assuming thumb files are present
                    if cid == target_category_id:
                        # Construct the final target URL using the full prefix and the filename
                        target_url = f"{DO_SPACES_ENDPOINT}/{DO_SPACES_BUCKET}/{original_prefix}{filename}"
                        break
                
                if target_url:
                    break
            # --- FIX ENDS HERE ---

            if not target_url:
                raise HTTPException(404, "Target categoryId not found")
#         # ------------------------------------------------------------------
#         # DOWNLOAD TARGET IMAGE
#         # ------------------------------------------------------------------
        async with httpx.AsyncClient(timeout=30.0) as client:
            response = await client.get(target_url)
            response.raise_for_status()
            tgt_bytes = response.content

        src_bgr = cv2.imdecode(np.frombuffer(src_bytes, np.uint8), cv2.IMREAD_COLOR)
        tgt_bgr = cv2.imdecode(np.frombuffer(tgt_bytes, np.uint8), cv2.IMREAD_COLOR)

        if src_bgr is None or tgt_bgr is None:
            raise HTTPException(400, "Invalid image data")

        src_rgb = cv2.cvtColor(src_bgr, cv2.COLOR_BGR2RGB)
        tgt_rgb = cv2.cvtColor(tgt_bgr, cv2.COLOR_BGR2RGB)

        # ------------------------------------------------------------------
        # READ OPTIONAL IMAGE2
        # ------------------------------------------------------------------
        img2_rgb = None
        if image2:
            img2_bytes = await image2.read()
            img2_bgr = cv2.imdecode(np.frombuffer(img2_bytes, np.uint8), cv2.IMREAD_COLOR)
            if img2_bgr is not None:
                img2_rgb = cv2.cvtColor(img2_bgr, cv2.COLOR_BGR2RGB)

        # ------------------------------------------------------------------
        # FACE SWAP EXECUTION (run in thread to not block event loop)
        # ------------------------------------------------------------------
        if img2_rgb is not None:
            def _couple_swap():
                pipeline_start = time.time()
                src_images = [src_rgb, img2_rgb]

                all_src_faces = []
                t0 = time.time()
                for img in src_images:
                    faces = face_analysis_app.get(cv2.cvtColor(img, cv2.COLOR_RGB2BGR))
                    all_src_faces.extend(faces)
                
                tgt_faces = face_analysis_app.get(cv2.cvtColor(tgt_rgb, cv2.COLOR_RGB2BGR))
                logger.info(f"[Pipeline] Couple face detection: {time.time()-t0:.2f}s")

                if not all_src_faces:
                    raise ValueError("No faces detected in source images")
                if not tgt_faces:
                    raise ValueError("No faces detected in target image")

                def face_sort_key(face):
                    x1, y1, x2, y2 = face.bbox
                    area = (x2 - x1) * (y2 - y1)
                    cx = (x1 + x2) / 2
                    return (-area, cx)

                src_male = sorted([f for f in all_src_faces if f.gender == 1], key=face_sort_key)
                src_female = sorted([f for f in all_src_faces if f.gender == 0], key=face_sort_key)
                tgt_male = sorted([f for f in tgt_faces if f.gender == 1], key=face_sort_key)
                tgt_female = sorted([f for f in tgt_faces if f.gender == 0], key=face_sort_key)

                pairs = []
                for s, t in zip(src_male, tgt_male):
                    pairs.append((s, t))
                for s, t in zip(src_female, tgt_female):
                    pairs.append((s, t))

                if not pairs:
                    src_all = sorted(all_src_faces, key=face_sort_key)
                    tgt_all = sorted(tgt_faces, key=face_sort_key)
                    pairs = list(zip(src_all, tgt_all))

                t0 = time.time()
                with swap_lock:
                    result_img = cv2.cvtColor(tgt_rgb, cv2.COLOR_RGB2BGR)
                    for src_face, _ in pairs:
                        current_faces = sorted(face_analysis_app.get(result_img), key=face_sort_key)
                        candidates = [f for f in current_faces if f.gender == src_face.gender] or current_faces
                        target_face = candidates[0]
                        result_img = swapper.get(result_img, target_face, src_face, paste_back=True)
                logger.info(f"[Pipeline] Couple face swap: {time.time()-t0:.2f}s")

                result_rgb_out = cv2.cvtColor(result_img, cv2.COLOR_BGR2RGB)

                t0 = time.time()
                enhanced_rgb = mandatory_enhancement(result_rgb_out)
                logger.info(f"[Pipeline] Couple enhancement: {time.time()-t0:.2f}s")
                
                enhanced_bgr = cv2.cvtColor(enhanced_rgb, cv2.COLOR_RGB2BGR)

                temp_dir = tempfile.mkdtemp(prefix="faceswap_")
                final_path = os.path.join(temp_dir, "result.png")
                cv2.imwrite(final_path, enhanced_bgr)

                with open(final_path, "rb") as f:
                    result_bytes = f.read()
                
                logger.info(f"[Pipeline] TOTAL couple swap: {time.time()-pipeline_start:.2f}s")
                return result_bytes

            try:
                result_bytes = await asyncio.to_thread(_couple_swap)
            except ValueError as ve:
                raise HTTPException(400, str(ve))

        else:
            # ----- SINGLE SOURCE SWAP (run in thread) -----
            def _single_swap():
                return face_swap_and_enhance(src_rgb, tgt_rgb)
            
            final_img, final_path, err = await asyncio.to_thread(_single_swap)
            
            if err:
                raise HTTPException(500, err)

            with open(final_path, "rb") as f:
                result_bytes = f.read()

        result_key = f"faceswap/result/{uuid.uuid4().hex}_enhanced.png"
        result_url = upload_to_spaces(result_bytes, result_key)
        # -------------------------------------------------
        # COMPRESS IMAGE (2–3 MB target)
        # -------------------------------------------------
        compressed_bytes = compress_image(
            image_bytes=result_bytes,
            max_size=(1280, 1280),
            quality=72
        )
        
        compressed_key = f"faceswap/result/{uuid.uuid4().hex}_enhanced_compressed.jpg"
        compressed_url = upload_to_spaces(
            compressed_bytes,
            compressed_key,
            content_type="image/jpeg"
        )
        end_time = datetime.utcnow()
        response_time_ms = (end_time - start_time).total_seconds() * 1000

        if database is not None:
            log_entry = {
                "endpoint": "/face-swap",
                "status": "success",
                "response_time_ms": response_time_ms,
                "timestamp": end_time
            }
            if appname:
                log_entry["appname"] = appname
            await database.faceswap.insert_one(log_entry)


        return {
            "result_key": result_key,
            "result_url": result_url,
            "Compressed_Image_URL": compressed_url
        }
        
    except Exception as e:
        end_time = datetime.utcnow()
        response_time_ms = (end_time - start_time).total_seconds() * 1000

        if database is not None:
            log_entry = {
                "endpoint": "/face-swap",
                "status": "fail",
                "response_time_ms": response_time_ms,
                "timestamp": end_time,
                "error": str(e)
            }
            if appname:
                log_entry["appname"] = appname
            await database.faceswap.insert_one(log_entry)

        raise HTTPException(500, f"Face swap failed: {str(e)}")

@fastapi_app.get("/preview/{result_key:path}")
async def preview_result(result_key: str):
    try:
        img_bytes = download_from_spaces(result_key)
    except Exception:
        raise HTTPException(status_code=404, detail="Result not found")
    return Response(
        content=img_bytes,
        media_type="image/png",
        headers={"Content-Disposition": "inline; filename=result.png"}
    )
    
@fastapi_app.post("/multi-face-swap", dependencies=[Depends(verify_token)])
async def multi_face_swap_api(
    source_image: UploadFile = File(...),
    target_image: UploadFile = File(...)
):
    start_time = datetime.utcnow()

    try:
        # -----------------------------
        # Read images
        # -----------------------------
        src_bytes = await source_image.read()
        tgt_bytes = await target_image.read()

        src_bgr = cv2.imdecode(np.frombuffer(src_bytes, np.uint8), cv2.IMREAD_COLOR)
        tgt_bgr = cv2.imdecode(np.frombuffer(tgt_bytes, np.uint8), cv2.IMREAD_COLOR)

        if src_bgr is None or tgt_bgr is None:
            raise HTTPException(400, "Invalid image data")

        src_rgb = cv2.cvtColor(src_bgr, cv2.COLOR_BGR2RGB)
        tgt_rgb = cv2.cvtColor(tgt_bgr, cv2.COLOR_BGR2RGB)

        # -----------------------------
        # Multi-face swap (run in thread to not block event loop)
        # -----------------------------
        def _multi_swap_and_enhance():
            swapped_rgb = multi_face_swap(src_rgb, tgt_rgb)
            return mandatory_enhancement(swapped_rgb)
        
        final_rgb = await asyncio.to_thread(_multi_swap_and_enhance)

        final_bgr = cv2.cvtColor(final_rgb, cv2.COLOR_RGB2BGR)

        # -----------------------------
        # Save temp result
        # -----------------------------
        temp_dir = tempfile.mkdtemp(prefix="multi_faceswap_")
        result_path = os.path.join(temp_dir, "result.png")
        cv2.imwrite(result_path, final_bgr)

        with open(result_path, "rb") as f:
            result_bytes = f.read()

        # -----------------------------
        # Upload
        # -----------------------------
        result_key = f"faceswap/multi/{uuid.uuid4().hex}.png"
        result_url = upload_to_spaces(
            result_bytes,
            result_key,
            content_type="image/png"
        )

        return {
            "result_key": result_key,
            "result_url": result_url
        }

    except Exception as e:
        raise HTTPException(status_code=500, detail=str(e))


@fastapi_app.post("/face-swap-couple", dependencies=[Depends(verify_token)])
async def face_swap_couple_api(
    image1: UploadFile = File(...),
    image2: Optional[UploadFile] = File(None),
    target_category_id: str = Form(None),
    new_category_id: str = Form(None),
    user_id: Optional[str] = Form(None),
    appname: Optional[str] = Form(None),
    credentials: HTTPAuthorizationCredentials = Security(security)
):
    """
    Production-ready face swap endpoint supporting:
    - Multiple source images (image1 + optional image2)
    - Gender-based pairing
    - Merged faces from multiple sources
    - Mandatory CodeFormer enhancement
    """
    start_time = datetime.utcnow()
    
    try:
        # -----------------------------
        # Validate input
        # -----------------------------
        if target_category_id == "":
            target_category_id = None
        if new_category_id == "":
            new_category_id = None
        if user_id == "":
            user_id = None

        subcategories_collection = get_app_db_collections(appname)

        if target_category_id and new_category_id:
            raise HTTPException(400, "Provide only one of new_category_id or target_category_id.")
        if not target_category_id and not new_category_id:
            raise HTTPException(400, "Either new_category_id or target_category_id is required.")

        logger.info(f"[FaceSwap] Incoming request → target_category_id={target_category_id}, new_category_id={new_category_id}, user_id={user_id}")

        # -----------------------------
        # Read source images
        # -----------------------------
        src_images = []
        img1_bytes = await image1.read()
        src1 = cv2.imdecode(np.frombuffer(img1_bytes, np.uint8), cv2.IMREAD_COLOR)
        if src1 is None:
            raise HTTPException(400, "Invalid image1 data")
        src_images.append(cv2.cvtColor(src1, cv2.COLOR_BGR2RGB))

        if image2:
            img2_bytes = await image2.read()
            src2 = cv2.imdecode(np.frombuffer(img2_bytes, np.uint8), cv2.IMREAD_COLOR)
            if src2 is not None:
                src_images.append(cv2.cvtColor(src2, cv2.COLOR_BGR2RGB))

        # -----------------------------
        # Resolve target image
        # -----------------------------
        target_url = None
        if new_category_id:
            doc = await subcategories_collection.find_one({
                "asset_images._id": ObjectId(new_category_id)
            })

            if not doc:
                raise HTTPException(404, "Asset image not found in database")

            asset = next(
                (img for img in doc["asset_images"] if str(img["_id"]) == new_category_id),
                None
            )

            if not asset:
                raise HTTPException(404, "Asset image URL not found")

            target_url = asset["url"]
            subcategory_oid = doc["_id"]

        if target_category_id:
            client = get_spaces_client()
            base_prefix = "faceswap/target/"
            resp = client.list_objects_v2(
                Bucket=DO_SPACES_BUCKET, Prefix=base_prefix, Delimiter="/"
            )

            categories = [p["Prefix"].split("/")[2] for p in resp.get("CommonPrefixes", [])]

            for category in categories:
                original_prefix = f"faceswap/target/{category}/original/"
                thumb_prefix = f"faceswap/target/{category}/thumb/"

                original_objects = client.list_objects_v2(
                    Bucket=DO_SPACES_BUCKET, Prefix=original_prefix
                ).get("Contents", [])

                thumb_objects = client.list_objects_v2(
                    Bucket=DO_SPACES_BUCKET, Prefix=thumb_prefix
                ).get("Contents", [])

                original_filenames = sorted([
                    obj["Key"].split("/")[-1] for obj in original_objects
                    if obj["Key"].split("/")[-1].endswith(".png")
                ])

                for idx, filename in enumerate(original_filenames, start=1):
                    cid = f"{category.lower()}image_{idx}"
                    if cid == target_category_id:
                        target_url = f"{DO_SPACES_ENDPOINT}/{DO_SPACES_BUCKET}/{original_prefix}{filename}"
                        break

                if target_url:
                    break

            if not target_url:
                raise HTTPException(404, "Target categoryId not found")

        async with httpx.AsyncClient(timeout=30.0) as client:
            response = await client.get(target_url)
            response.raise_for_status()
            tgt_bytes = response.content

        tgt_bgr = cv2.imdecode(np.frombuffer(tgt_bytes, np.uint8), cv2.IMREAD_COLOR)
        if tgt_bgr is None:
            raise HTTPException(400, "Invalid target image data")

        # -----------------------------
        # Couple face swap + enhance (run in thread)
        # -----------------------------
        def _couple_face_swap_and_enhance():
            pipeline_start = time.time()
            
            all_src_faces = []
            t0 = time.time()
            for img in src_images:
                faces = face_analysis_app.get(cv2.cvtColor(img, cv2.COLOR_RGB2BGR))
                all_src_faces.extend(faces)

            tgt_faces = face_analysis_app.get(tgt_bgr)
            logger.info(f"[Pipeline] Couple-ep face detection: {time.time()-t0:.2f}s")

            if not all_src_faces:
                raise ValueError("No faces detected in source images")
            if not tgt_faces:
                raise ValueError("No faces detected in target image")

            def face_sort_key(face):
                x1, y1, x2, y2 = face.bbox
                area = (x2 - x1) * (y2 - y1)
                cx = (x1 + x2) / 2
                return (-area, cx)

            src_male = sorted([f for f in all_src_faces if f.gender == 1], key=face_sort_key)
            src_female = sorted([f for f in all_src_faces if f.gender == 0], key=face_sort_key)
            tgt_male = sorted([f for f in tgt_faces if f.gender == 1], key=face_sort_key)
            tgt_female = sorted([f for f in tgt_faces if f.gender == 0], key=face_sort_key)

            pairs = []
            for s, t in zip(src_male, tgt_male):
                pairs.append((s, t))
            for s, t in zip(src_female, tgt_female):
                pairs.append((s, t))

            if not pairs:
                src_all = sorted(all_src_faces, key=face_sort_key)
                tgt_all = sorted(tgt_faces, key=face_sort_key)
                pairs = list(zip(src_all, tgt_all))

            t0 = time.time()
            with swap_lock:
                result_img = tgt_bgr.copy()
                for src_face, _ in pairs:
                    current_faces = sorted(face_analysis_app.get(result_img), key=face_sort_key)
                    candidates = [f for f in current_faces if f.gender == src_face.gender] or current_faces
                    target_face = candidates[0]
                    result_img = swapper.get(result_img, target_face, src_face, paste_back=True)
            logger.info(f"[Pipeline] Couple-ep face swap: {time.time()-t0:.2f}s")

            result_rgb = cv2.cvtColor(result_img, cv2.COLOR_BGR2RGB)

            t0 = time.time()
            enhanced_rgb = mandatory_enhancement(result_rgb)
            logger.info(f"[Pipeline] Couple-ep enhancement: {time.time()-t0:.2f}s")
            
            enhanced_bgr = cv2.cvtColor(enhanced_rgb, cv2.COLOR_RGB2BGR)

            temp_dir = tempfile.mkdtemp(prefix="faceswap_")
            final_path = os.path.join(temp_dir, "result.png")
            cv2.imwrite(final_path, enhanced_bgr)

            with open(final_path, "rb") as f:
                result_bytes = f.read()
            
            logger.info(f"[Pipeline] TOTAL couple-ep swap: {time.time()-pipeline_start:.2f}s")
            return result_bytes

        try:
            result_bytes = await asyncio.to_thread(_couple_face_swap_and_enhance)
        except ValueError as ve:
            raise HTTPException(400, str(ve))

        result_key = f"faceswap/result/{uuid.uuid4().hex}_enhanced.png"
        result_url = upload_to_spaces(result_bytes, result_key)

        compressed_bytes = compress_image(result_bytes, max_size=(1280, 1280), quality=72)
        compressed_key = f"faceswap/result/{uuid.uuid4().hex}_enhanced_compressed.jpg"
        compressed_url = upload_to_spaces(compressed_bytes, compressed_key, content_type="image/jpeg")

        # -----------------------------
        # Log API usage
        # -----------------------------
        end_time = datetime.utcnow()
        response_time_ms = (end_time - start_time).total_seconds() * 1000
        if database is not None:
            log_entry = {
                "endpoint": "/face-swap-couple",
                "status": "success",
                "response_time_ms": response_time_ms,
                "timestamp": end_time
            }
            if appname:
                log_entry["appname"] = appname
            await database.faceswap.insert_one(log_entry)

        return {
            "result_key": result_key,
            "result_url": result_url,
            "compressed_url": compressed_url
        }

    except Exception as e:
        end_time = datetime.utcnow()
        response_time_ms = (end_time - start_time).total_seconds() * 1000
        if database is not None:
            log_entry = {
                "endpoint": "/face-swap-couple",
                "status": "fail",
                "response_time_ms": response_time_ms,
                "timestamp": end_time,
                "error": str(e)
            }
            if appname:
                log_entry["appname"] = appname
            await database.faceswap.insert_one(log_entry)
        raise HTTPException(500, f"Face swap failed: {str(e)}")


if __name__ == "__main__":
    uvicorn.run(fastapi_app, host="0.0.0.0", port=7860)