Code/Backend/api_server.py

"""
FastAPI REST API server for Pokemon Card Authentication.

This server wraps the DL prediction pipeline (ResNet50 + EfficientNet-B7)
to provide a clean REST interface for the frontend application.
"""

import hashlib
import os
import sys
from contextlib import asynccontextmanager
from pathlib import Path
from threading import Lock, Thread
from typing import Any, Dict, List, Optional
from urllib.parse import urlparse
from urllib.request import Request, urlopen

BASE_DIR = Path(__file__).resolve().parent

# Load .env from the Backend directory (ignored by git; overrides nothing already set)
try:
    from dotenv import load_dotenv
    load_dotenv(BASE_DIR / ".env", override=False)
except ImportError:
    pass


def _find_model_package_root() -> Path:
    candidates = [
        BASE_DIR.parent / "Model",  # Code/Model in monorepo
        BASE_DIR.parent.parent / "Code" / "Model",  # Repo root /Code/Model
        BASE_DIR,  # If src/ is vendored into Code/Backend/
    ]
    for candidate in candidates:
        if (candidate / "src" / "dl" / "prediction_pipeline.py").is_file():
            return candidate
    raise RuntimeError(
        "Could not locate model source package root containing "
        "'src/dl/prediction_pipeline.py'. For Railway, prefer deploying "
        "with Root Directory set to the repository root so `Code/Model/` is "
        "included; alternatively vendor `Code/Model/src` into `Code/Backend/src`."
    )


def _find_models_dir(model_package_root: Path) -> Path:
    candidates = [
        model_package_root / "data" / "models",  # Monorepo Model directory
        BASE_DIR / "data" / "models",  # Vendored into Backend for deployment
    ]
    for candidate in candidates:
        if candidate.is_dir():
            return candidate
    raise RuntimeError(
        "Could not locate trained models directory. Ensure "
        "model files are present in the deploy build context, or vendor "
        "them into `Code/Backend/data/models`. Looked for: "
        f"{candidates}"
    )


def _discover_local_checkpoint(dl_models_dir: Path) -> Optional[Path]:
    """
    Discover a local checkpoint in preferred order.

    Priority:
    1) *_best.pth
    2) *_final.pth
    3) any *.pth
    """
    if not dl_models_dir.exists():
        return None

    for pattern in ("*_best.pth", "*_final.pth", "*.pth"):
        candidates = sorted(dl_models_dir.glob(pattern))
        if candidates:
            return candidates[-1]
    return None


def _compute_sha256(file_path: Path) -> str:
    """Compute SHA256 hash for file integrity checks."""
    sha256 = hashlib.sha256()
    with open(file_path, "rb") as f:
        for chunk in iter(lambda: f.read(1024 * 1024), b""):
            sha256.update(chunk)
    return sha256.hexdigest()


def _resolve_model_filename(download_url: str, filename_override: Optional[str]) -> str:
    """Resolve destination filename from override or URL path."""
    if filename_override:
        candidate = Path(filename_override).name
        if candidate:
            return candidate

    candidate = Path(urlparse(download_url).path).name
    if candidate:
        return candidate

    return "downloaded_model_best.pth"


def _download_file(download_url: str, destination: Path, bearer_token: Optional[str] = None, timeout_seconds: int = 120) -> None:
    """Download file from URL to destination path."""
    headers = {}
    if bearer_token:
        headers["Authorization"] = f"Bearer {bearer_token}"

    destination.parent.mkdir(parents=True, exist_ok=True)
    tmp_destination = destination.with_suffix(destination.suffix + ".tmp")

    request = Request(download_url, headers=headers)
    with urlopen(request, timeout=timeout_seconds) as response, open(tmp_destination, "wb") as out_file:
        while True:
            chunk = response.read(1024 * 1024)
            if not chunk:
                break
            out_file.write(chunk)

    tmp_destination.replace(destination)


def _download_checkpoint_from_env(dl_models_dir: Path) -> Optional[Path]:
    """
    Download checkpoint when DL_MODEL_URL is configured.

    Optional env vars:
      - DL_MODEL_FILENAME: override downloaded filename
      - DL_MODEL_SHA256: expected checksum (lowercase hex)
      - DL_MODEL_BEARER_TOKEN: bearer token for private URLs
    """
    download_url = os.getenv("DL_MODEL_URL", "").strip()
    if not download_url:
        return None

    filename_override = os.getenv("DL_MODEL_FILENAME", "").strip() or None
    expected_sha256 = os.getenv("DL_MODEL_SHA256", "").strip().lower() or None
    bearer_token = os.getenv("DL_MODEL_BEARER_TOKEN", "").strip() or None

    filename = _resolve_model_filename(download_url, filename_override)
    destination = dl_models_dir / filename

    if destination.exists() and expected_sha256:
        existing_hash = _compute_sha256(destination).lower()
        if existing_hash != expected_sha256:
            print(f"⚠️ Existing checkpoint hash mismatch, re-downloading: {destination.name}")
            destination.unlink()

    if not destination.exists():
        print(f"Downloading DL checkpoint from DL_MODEL_URL to {destination}")
        _download_file(download_url, destination, bearer_token=bearer_token)

    if expected_sha256:
        actual_sha256 = _compute_sha256(destination).lower()
        if actual_sha256 != expected_sha256:
            try:
                destination.unlink()
            except OSError:
                pass
            raise RuntimeError(
                f"Downloaded checkpoint hash mismatch for {destination.name}. "
                f"Expected {expected_sha256}, got {actual_sha256}"
            )

    return destination


def _should_load_model_on_startup() -> bool:
    """
    Decide whether to eagerly load the DL model during startup.

    Env var:
      - DL_LOAD_ON_STARTUP=true|false (default: true)
    """
    raw = os.getenv("DL_LOAD_ON_STARTUP", "").strip().lower()
    if raw in ("", "1", "true", "yes", "on"):
        return True
    if raw in ("0", "false", "no", "off"):
        return False

    print(f"⚠️ Invalid DL_LOAD_ON_STARTUP value '{raw}', defaulting to eager loading.")
    return True


MODEL_PACKAGE_ROOT = _find_model_package_root()
MODELS_DIR = _find_models_dir(MODEL_PACKAGE_ROOT)

# Add model package root to path for importing `src.*` modules
sys.path.insert(0, str(MODEL_PACKAGE_ROOT))

from fastapi import FastAPI, HTTPException
from fastapi.middleware.cors import CORSMiddleware
from pydantic import BaseModel, Field
import numpy as np
import cv2
import base64
import json
import time

from src.dl.prediction_pipeline import create_dl_pipeline
from src.preprocessing.card_detector import detect_card_boundary_strict

# Import validators
import sys
backend_src_path = str(BASE_DIR / "src")
if backend_src_path not in sys.path:
    sys.path.insert(0, backend_src_path)

from validators.feature_based_validator import FeatureBasedValidator
from validators.multilayer_validation import run_multilayer_validation


@asynccontextmanager
async def app_lifespan(_: FastAPI):
    """Run startup initialization via FastAPI lifespan to avoid deprecated startup events."""
    await startup_event()
    yield


# Initialize FastAPI app
app = FastAPI(
    title="Pokemon Card Authentication API",
    description="AI-powered Pokemon card authentication using ResNet50 + EfficientNet-B7",
    version="2.0.0",
    lifespan=app_lifespan,
)

# CORS middleware for frontend connectivity
app.add_middleware(
    CORSMiddleware,
    allow_origins=[
        "http://localhost:3000",
        "http://127.0.0.1:3000",
        "https://pokemonauthenticator.com",
        "https://www.pokemonauthenticator.com",
    ],
    allow_origin_regex=r"^https://.*\.(vercel\.app|vercel\.com)$",
    allow_credentials=True,
    allow_methods=["*"],
    allow_headers=["*"],
)

# Global DL pipeline instance
dl_pipeline = None
model_load_error = None
model_version_info = None  # DL model version metadata
model_filename = None  # DL model filename
model_registry = None  # Cached model registry metadata
model_load_lock = Lock()
model_load_mode = "eager"  # "eager" (default) or "lazy"
model_loading = False  # True while a model load is in progress

# Global validators (validation layers kept from earlier pipeline revisions)
feature_validator = None


def _load_version_registry(registry_path: Path) -> Optional[dict]:
    """Load version registry if it exists."""
    if not registry_path.exists():
        print(f"⚠️  Version registry not found: {registry_path}")
        return None

    try:
        with open(registry_path, 'r') as f:
            registry = json.load(f)
        print(f"✅ Loaded version registry (schema v{registry.get('schema_version')})")
        return registry
    except Exception as e:
        print(f"⚠️  Failed to load version registry: {e}")
        return None


def _get_model_version_info(registry: Optional[dict], model_filename: str) -> Optional[Dict[str, Any]]:
    """Extract version info for a specific model from registry."""
    if registry is None:
        return None

    try:
        # Prefer exact filename match across all model types.
        for model_entries in registry.get('models', {}).values():
            for model_entry in model_entries:
                if model_entry.get('filename') == model_filename:
                    return model_entry

        # Fallback: Extract version token (YYYYMMDD_HHMMSS) from filename.
        stem = Path(model_filename).stem
        import re
        match = re.search(r"(\d{8}_\d{6})", stem)
        if not match:
            return None
        version = match.group(1)

        for model_entries in registry.get('models', {}).values():
            for model_entry in model_entries:
                if model_entry.get('version') == version:
                    return model_entry

    except Exception as e:
        print(f"⚠️  Failed to extract version info: {e}")
        return None

    return None


def _initialize_feature_validator() -> None:
    """Initialize pre-DL validation layers."""
    global feature_validator

    if feature_validator is not None:
        return

    print("\n" + "=" * 80)
    print("Initializing validators...")
    print("=" * 80)

    try:
        feature_validator = FeatureBasedValidator(confidence_threshold=0.75)
        print("✅ Pokemon card validators loaded (color-based back validation)")
    except Exception as e:
        print(f"⚠️ Failed to load validators: {e}")
        import traceback
        traceback.print_exc()
        feature_validator = None


def _load_dl_pipeline_on_demand():
    """Lazy-load DL pipeline on first authenticate request."""
    global dl_pipeline, model_load_error, model_version_info, model_filename, model_registry, model_loading

    if dl_pipeline is not None:
        return dl_pipeline

    # Avoid repeated expensive retries when the model has already failed to load.
    if model_load_error:
        return None

    with model_load_lock:
        if dl_pipeline is not None:
            return dl_pipeline
        if model_load_error:
            return None

        model_loading = True
        try:
            print("\n" + "=" * 80)
            print("Loading DL model...")
            print("=" * 80)

            if model_registry is None:
                registry_path = MODELS_DIR / "version_registry.json"
                model_registry = _load_version_registry(registry_path)

            dl_models_dir = MODELS_DIR / "dl"
            dl_model_path = None
            download_error = None

            # Find local DL checkpoint first
            discovered_checkpoint = _discover_local_checkpoint(dl_models_dir)
            if discovered_checkpoint is not None:
                dl_model_path = str(discovered_checkpoint)
                model_filename = discovered_checkpoint.name
                print(f"Loading local DL model: {model_filename}")
            else:
                try:
                    downloaded_checkpoint = _download_checkpoint_from_env(dl_models_dir)
                    if downloaded_checkpoint is not None:
                        dl_model_path = str(downloaded_checkpoint)
                        model_filename = downloaded_checkpoint.name
                        print(f"Loading downloaded DL model: {model_filename}")
                except Exception as e:
                    download_error = str(e)
                    print(f"⚠️ DL checkpoint download failed: {download_error}")

            if dl_model_path:
                try:
                    dl_pipeline = create_dl_pipeline(
                        model_path=dl_model_path,
                        preprocessing_config={"target_size": 256},
                    )
                    print(f"✅ DL pipeline loaded: {model_filename}")

                    # Extract version info
                    version_entry = _get_model_version_info(model_registry, model_filename)
                    if version_entry:
                        model_version_info = version_entry
                        print(f"✅ DL Model version: {version_entry.get('version')} ({version_entry.get('status')})")
                except Exception as e:
                    import traceback
                    print(f"⚠️ DL model failed to load: {e}")
                    traceback.print_exc()
                    dl_pipeline = None

            if dl_pipeline is None:
                error_msg_parts = [f"No DL model found in {MODELS_DIR / 'dl'}."]
                if download_error:
                    error_msg_parts.append(f"DL_MODEL_URL bootstrap failed: {download_error}.")
                error_msg_parts.append(
                    "Provide a checkpoint in that directory, or set DL_MODEL_URL "
                    "(optional: DL_MODEL_FILENAME, DL_MODEL_SHA256, DL_MODEL_BEARER_TOKEN)."
                )
                error_msg_parts.append("Train locally with: cd ../Model && python -m src.dl.train_dl")
                model_load_error = " ".join(error_msg_parts)
                print(f"❌ {model_load_error}")
                return None

            model_load_error = None
            print("=" * 80)
            return dl_pipeline
        except Exception as e:
            import traceback
            print(f"⚠️ Unexpected DL model load failure: {e}")
            traceback.print_exc()
            dl_pipeline = None
            model_load_error = f"Unexpected DL model load failure: {e}"
            return None
        finally:
            model_loading = False


def _start_background_model_load_if_needed() -> bool:
    """
    Trigger model loading in a daemon thread.

    Returns:
      True if a new background load was started, False otherwise.
    """
    global model_loading, model_load_error

    with model_load_lock:
        if dl_pipeline is not None or model_load_error or model_loading:
            return False
        model_loading = True

    def _run_loader():
        global model_loading
        try:
            _load_dl_pipeline_on_demand()
        finally:
            model_loading = False

    try:
        Thread(target=_run_loader, daemon=True, name="dl-model-loader").start()
        return True
    except Exception as e:
        model_loading = False
        model_load_error = f"Failed to start background model load: {e}"
        print(f"⚠️ {model_load_error}")
        return False


async def startup_event():
    """Initialize lightweight components; defer DL model to first request."""
    global model_registry, model_load_mode

    load_on_startup = _should_load_model_on_startup()
    model_load_mode = "eager" if load_on_startup else "lazy"

    print("=" * 80)
    print(f"Starting API (DL model load mode: {model_load_mode})...")
    print(f"Models directory: {MODELS_DIR}")
    print(f"Models directory exists: {MODELS_DIR.exists()}")

    # Load version registry
    registry_path = MODELS_DIR / "version_registry.json"
    model_registry = _load_version_registry(registry_path)

    # Initialize Pokemon card validators (validation layers unchanged)
    _initialize_feature_validator()

    if load_on_startup:
        print("Eager mode enabled: loading DL model during startup.")
        _load_dl_pipeline_on_demand()
    else:
        print("Lazy mode enabled: DL model initialization deferred to first /api/authenticate request.")

    print("=" * 80)


# Pydantic models for request/response validation
class AuthenticateRequest(BaseModel):
    """Request body for card authentication."""
    front_image: str = Field(..., description="Base64 encoded front image")
    back_image: str = Field(..., description="Base64 encoded back image")


class CardDetectRequest(BaseModel):
    """Request body for card edge detection."""
    image: str = Field(..., description="Base64 encoded image")


class CardDetectResponse(BaseModel):
    """Response body for card edge detection."""
    card_detected: bool = Field(..., description="True if card edges are detected")


class PredictionResult(BaseModel):
    """Individual image prediction result."""
    prediction: int = Field(..., description="-1=no_card, 0=counterfeit, 1=authentic")
    label: str = Field(..., description="'authentic', 'counterfeit', or 'no_card'")
    confidence: float = Field(..., ge=0, le=1, description="Confidence score")
    probabilities: Dict[str, float] = Field(..., description="Class probabilities")
    inference_time_ms: float = Field(..., description="Inference time in milliseconds")
    component_scores: Optional[Dict[str, float]] = Field(None, description="Per-head DL scores")
    rarity_pred_class: Optional[str] = Field(None, description="Predicted rarity class name for front images")
    back_score: Optional[float] = Field(None, ge=0, le=1, description="Back authentication score for back images")


class QualityCheckResult(BaseModel):
    """Image quality check result."""
    blur_score: float = Field(..., description="Laplacian variance (higher = sharper)")
    brightness: float = Field(..., description="Mean pixel value (0-255)")
    contrast: float = Field(..., description="Std deviation of pixels")
    is_acceptable: bool = Field(..., description="Whether image passes quality checks")


class PokemonBackValidation(BaseModel):
    """Pokemon back color validation result."""
    passed: bool = Field(..., description="Whether back image passes Pokemon back validation")
    confidence: float = Field(..., ge=0, le=1, description="Confidence score for validation")
    reason: str = Field(..., description="Validation failure/success reason")


class ModelVersionInfo(BaseModel):
    """Model version and training metadata."""
    version: str = Field(..., description="Model version (timestamp)")
    model_type: str = Field(..., description="Model type (dl_multihead)")
    model_class: str = Field(default="", description="Python class name")
    training_date: str = Field(default="", description="ISO timestamp of training")
    status: str = Field(..., description="Deployment status (production, staging, training)")
    accuracy: Optional[float] = Field(None, description="Test accuracy")
    f1_score: Optional[float] = Field(None, description="Test F1 score")
    roc_auc: Optional[float] = Field(None, description="Test ROC AUC")
    dataset_size: Optional[int] = Field(None, description="Number of training samples")
    n_features: Optional[Any] = Field(None, description="Number of features or 'end-to-end'")
    pipeline_type: Optional[str] = Field(None, description="Pipeline type: 'dl'")
    backbone: Optional[str] = Field(None, description="DL backbone architecture")


class RejectionReason(BaseModel):
    """Detailed information about why a card was rejected as 'no_card'."""
    category: str = Field(..., description="Rejection category: 'geometry', 'back_pattern', 'front_is_back', 'mismatch'")
    message: str = Field(..., description="User-friendly error message")
    details: Dict[str, Any] = Field(default_factory=dict, description="Technical details for debugging")


class AuthenticateResponse(BaseModel):
    """Response body for card authentication."""
    is_authentic: bool = Field(..., description="Final authentication result")
    confidence: float = Field(..., ge=0, le=1, description="Overall confidence")
    label: str = Field(..., description="'authentic', 'counterfeit', or 'no_card'")
    probabilities: Dict[str, float] = Field(..., description="Average probabilities")
    front_analysis: PredictionResult = Field(..., description="Front card analysis")
    back_analysis: PredictionResult = Field(..., description="Back card analysis")
    processing_time_ms: float = Field(..., description="Total processing time")
    quality_checks: Dict[str, QualityCheckResult] = Field(..., description="Quality checks for both images")
    pokemon_back_validation: Optional[PokemonBackValidation] = Field(None, description="Pokemon back validation result (if performed)")
    model_version: Optional[ModelVersionInfo] = Field(None, description="DL model version information")
    rejection_reason: Optional[RejectionReason] = Field(None, description="Detailed rejection reason (if label='no_card')")
    processed_sides: Optional[List[str]] = Field(None, description="Side(s) that passed validation and were processed by DL inference")




@app.get("/")
async def root():
    """Root endpoint."""
    return {
        "message": "Pokemon Card Authentication API",
        "version": "2.0.0",
        "status": "running",
        "endpoints": {
            "health": "/api/health",
            "warmup": "/api/warmup",
            "card_detect": "/api/card-detect",
            "authenticate": "/api/authenticate",
            "docs": "/docs"
        }
    }


@app.get("/api/health")
async def health_check():
    """Health check endpoint to verify API and model status."""
    if dl_pipeline is None:
        response = {
            "status": "degraded" if model_load_error else "ok",
            "model_loaded": False,
            "model_loading": model_loading,
            "model_load_mode": model_load_mode,
            "api_version": "2.0.0",
            "error": model_load_error,
            "models_dir": str(MODELS_DIR),
            "models_dir_exists": MODELS_DIR.exists(),
        }
        if model_version_info:
            info = dict(model_version_info)
            if 'trained_at' in info and not info.get('training_date'):
                info['training_date'] = info['trained_at']
            response["model_version"] = ModelVersionInfo(**info).model_dump()
        return response

    response = {
        "status": "ok",
        "model_loaded": True,
        "model_loading": model_loading,
        "model_load_mode": model_load_mode,
        "api_version": "2.0.0",
        "model_name": model_filename or "dl_model",
    }

    # Add version info if available
    if model_version_info:
        info = dict(model_version_info)
        if 'trained_at' in info and not info.get('training_date'):
            info['training_date'] = info['trained_at']
        response["model_version"] = ModelVersionInfo(**info).model_dump()

    return response


@app.post("/api/warmup")
async def warmup_model():
    """Trigger asynchronous DL model loading."""
    if dl_pipeline is not None:
        return {
            "status": "ready",
            "model_loaded": True,
            "model_loading": False,
            "model_load_mode": model_load_mode,
        }

    if model_load_error:
        return {
            "status": "error",
            "model_loaded": False,
            "model_loading": False,
            "model_load_mode": model_load_mode,
            "error": model_load_error,
        }

    started = _start_background_model_load_if_needed()
    return {
        "status": "warming" if (started or model_loading) else "pending",
        "model_loaded": False,
        "model_loading": True if (started or model_loading) else False,
        "model_load_mode": model_load_mode,
    }


@app.post("/api/card-detect", response_model=CardDetectResponse)
async def card_detect(request: CardDetectRequest):
    """
    Detect card edges in a single image.

    Args:
        request: Contains base64-encoded image

    Returns:
        Card detection result
    """
    img = decode_base64_image(request.image)
    if img is None:
        raise HTTPException(status_code=400, detail="Failed to decode image")

    corners = detect_card_boundary_strict(
        img,
        min_area_ratio=0.001,
        max_area_ratio=0.999,
        aspect_ratio_range=(0.30, 1.0),
        solidity_threshold=0.60,
        fill_ratio_threshold=0.40,
    )

    return CardDetectResponse(card_detected=corners is not None)


@app.post("/api/authenticate", response_model=AuthenticateResponse)
async def authenticate_card(request: AuthenticateRequest):
    """
    Authenticate a Pokemon card using front and back images.

    Args:
        request: Contains base64-encoded front and back images

    Returns:
        Authentication result with confidence scores and quality checks

    Raises:
        HTTPException: If model not loaded or processing fails
    """
    if dl_pipeline is None and model_load_mode == "lazy":
        if model_loading or _start_background_model_load_if_needed():
            raise HTTPException(
                status_code=503,
                detail=(
                    "DL model warm-up in progress. Retry in 20-60 seconds. "
                    "You can poll /api/health (model_loaded/model_loading) or call /api/warmup."
                ),
            )

    pipeline = _load_dl_pipeline_on_demand()
    if pipeline is None:
        raise HTTPException(
            status_code=503,
            detail=model_load_error or (
                "No DL model loaded. Add checkpoint to Code/Model/data/models/dl "
                "or set DL_MODEL_URL, then restart backend."
            ),
        )

    print("Using DL pipeline for authentication")
    start_time = time.time()

    try:
        # Decode base64 images
        front_img = decode_base64_image(request.front_image)
        back_img = decode_base64_image(request.back_image)

        # Validate images
        if front_img is None:
            raise HTTPException(status_code=400, detail="Failed to decode front image")
        if back_img is None:
            raise HTTPException(status_code=400, detail="Failed to decode back image")

        def _no_card_result() -> Dict[str, Any]:
            return {
                "prediction": -1,
                "label": "no_card",
                "confidence": 0.0,
                "probabilities": {"authentic": 0.0, "counterfeit": 0.0},
                "inference_time_ms": 0.0,
            }

        validation = run_multilayer_validation(
            front_image=front_img,
            back_image=back_img,
            feature_validator=feature_validator,
            require_both_sides=False,
        )
        def _to_pokemon_back_validation() -> Optional[PokemonBackValidation]:
            if (
                validation.pokemon_back_validation is not None
                and not validation.pokemon_back_validation.passed
            ):
                return PokemonBackValidation(
                    passed=False,
                    confidence=validation.pokemon_back_validation.confidence,
                    reason=validation.pokemon_back_validation.reason,
                )

            if (
                validation.front_not_back_validation is not None
                and not validation.front_not_back_validation.passed
            ):
                return PokemonBackValidation(
                    passed=False,
                    confidence=validation.front_not_back_validation.confidence,
                    reason=(
                        "Front image appears to be a card back: "
                        f"{validation.front_not_back_validation.reason}"
                    ),
                )

            return None

        front_result = _no_card_result()
        back_result = _no_card_result()

        if "front" in validation.processed_sides:
            front_result = pipeline.predict(front_img, is_back=False)
        if "back" in validation.processed_sides:
            back_result = pipeline.predict(back_img, is_back=True)

        pokemon_back_validation = _to_pokemon_back_validation()
        processing_time_ms = (time.time() - start_time) * 1000
        processed_sides = validation.processed_sides or None

        if validation.rejected:
            response_data: Dict[str, Any] = {
                "is_authentic": False,
                "confidence": 0.0,
                "label": "no_card",
                "probabilities": {"authentic": 0.0, "counterfeit": 0.0},
                "front_analysis": PredictionResult(**front_result),
                "back_analysis": PredictionResult(**back_result),
                "processing_time_ms": processing_time_ms,
                "quality_checks": {
                    "front": QualityCheckResult(**validation.front.quality),
                    "back": QualityCheckResult(**validation.back.quality),
                },
                "rejection_reason": RejectionReason(
                    category=validation.rejection_category,
                    message=validation.rejection_message,
                    details=validation.rejection_details,
                ),
                "processed_sides": processed_sides,
            }
            if pokemon_back_validation is not None:
                response_data["pokemon_back_validation"] = pokemon_back_validation
            return AuthenticateResponse(**response_data)

        valid_authentic_probs: List[float] = []
        if front_result.get("label") != "no_card":
            valid_authentic_probs.append(float(front_result["probabilities"]["authentic"]))
        if back_result.get("label") != "no_card":
            valid_authentic_probs.append(float(back_result["probabilities"]["authentic"]))

        if not valid_authentic_probs:
            response_data = {
                "is_authentic": False,
                "confidence": 0.0,
                "label": "no_card",
                "probabilities": {"authentic": 0.0, "counterfeit": 0.0},
                "front_analysis": PredictionResult(**front_result),
                "back_analysis": PredictionResult(**back_result),
                "processing_time_ms": processing_time_ms,
                "quality_checks": {
                    "front": QualityCheckResult(**validation.front.quality),
                    "back": QualityCheckResult(**validation.back.quality),
                },
                "rejection_reason": RejectionReason(
                    category="geometry",
                    message="Validated side(s) were classified as non-Pokemon cards",
                    details={"processed_sides": validation.processed_sides},
                ),
                "processed_sides": processed_sides,
            }
            if pokemon_back_validation is not None:
                response_data["pokemon_back_validation"] = pokemon_back_validation
            return AuthenticateResponse(**response_data)

        avg_authentic_prob = sum(valid_authentic_probs) / len(valid_authentic_probs)
        avg_counterfeit_prob = 1.0 - avg_authentic_prob
        final_label = "authentic" if avg_authentic_prob >= 0.5 else "counterfeit"
        final_confidence = max(avg_authentic_prob, avg_counterfeit_prob)

        response_data = {
            "is_authentic": avg_authentic_prob >= 0.5,
            "confidence": final_confidence,
            "label": final_label,
            "probabilities": {
                "authentic": avg_authentic_prob,
                "counterfeit": avg_counterfeit_prob,
            },
            "front_analysis": PredictionResult(**front_result),
            "back_analysis": PredictionResult(**back_result),
            "processing_time_ms": processing_time_ms,
            "quality_checks": {
                "front": QualityCheckResult(**validation.front.quality),
                "back": QualityCheckResult(**validation.back.quality),
            },
            "processed_sides": processed_sides,
        }

        if pokemon_back_validation is not None:
            response_data["pokemon_back_validation"] = pokemon_back_validation

        if model_version_info:
            info = dict(model_version_info)
            if "trained_at" in info and not info.get("training_date"):
                info["training_date"] = info["trained_at"]
            response_data["model_version"] = ModelVersionInfo(**info)

        return AuthenticateResponse(**response_data)

    except HTTPException:
        raise
    except Exception as e:
        raise HTTPException(
            status_code=500,
            detail=f"Authentication failed: {str(e)}"
        )
def decode_base64_image(base64_str: str) -> Optional[np.ndarray]:
    """
    Decode base64 string to OpenCV image (BGR format).

    Args:
        base64_str: Base64 encoded image string (with or without data URI prefix)

    Returns:
        NumPy array in BGR format, or None if decoding fails
    """
    try:
        # Remove data URI prefix if present (data:image/jpeg;base64,...)
        if ',' in base64_str:
            base64_str = base64_str.split(',')[1]

        # Decode base64 to bytes
        img_bytes = base64.b64decode(base64_str)

        # Convert bytes to numpy array
        nparr = np.frombuffer(img_bytes, np.uint8)

        # Decode to OpenCV image
        img = cv2.imdecode(nparr, cv2.IMREAD_COLOR)

        return img
    except Exception as e:
        print(f"Error decoding base64 image: {e}")
        return None


if __name__ == "__main__":
    import uvicorn
    uvicorn.run(
        app,
        host="0.0.0.0",
        port=8000,
        log_level="info"
    )