# -*- coding:utf-8 -*-
"""InsightFace buffalo_l model wrapper."""
import os
import numpy as np
from PIL import Image

_model = None
_lock = False

MODEL_NAME = "buffalo_l"

def get_model():
    global _model, _lock
    if _model is None and not _lock:
        _lock = True
        try:
            import insightface
            from insightface.app import FaceAnalysis
            _model = FaceAnalysis(name=MODEL_NAME, providers=['CUDAExecutionProvider'])
            _model.prepare(ctx_id=0, det_size=(640, 640))
        except Exception as e:
            _lock = False
            raise RuntimeError(f"Failed to load InsightFace: {e}")
    return _model


def load_image(image_input):
    """Load image from path, URL, or base64."""
    if isinstance(image_input, str):
        if image_input.startswith("/") and os.path.exists(image_input):
            return Image.open(image_input).convert("RGB")
        elif image_input.startswith("http"):
            import urllib.request
            import io
            with urllib.request.urlopen(image_input, timeout=10) as resp:
                return Image.open(io.BytesIO(resp.read())).convert("RGB")
    raise ValueError(f"Cannot load image: {image_input[:50] if isinstance(image_input, str) else 'unknown'}")


def detect(image_input):
    """Detect faces in an image. Returns list of face info."""
    model = get_model()
    img = load_image(image_input)
    img_np = np.array(img)
    
    faces = model.get(img_np)
    
    results = []
    for i, face in enumerate(faces):
        results.append({
            "face_id": f"face_{i}",
            "bbox": face.bbox.tolist(),
            "det_score": round(float(face.det_score), 4),
            "age": int(face.age) if face.age > 0 else None,
            "gender": "M" if face.gender == 1 else "F",
            "embedding_dim": len(face.embedding)
        })
    
    return {"faces": results, "count": len(results)}


def recognize(image_input):
    """Get face embeddings from an image."""
    model = get_model()
    img = load_image(image_input)
    img_np = np.array(img)
    
    faces = model.get(img_np)
    
    results = []
    for i, face in enumerate(faces):
        # Normalize embedding to unit vector
        embedding = face.embedding / np.linalg.norm(face.embedding)
        results.append({
            "face_id": f"face_{i}",
            "bbox": face.bbox.tolist(),
            "det_score": round(float(face.det_score), 4),
            "embedding": embedding.tolist()
        })
    
    return {"faces": results, "count": len(results)}


def compare(embed1, embed2):
    """Compare two face embeddings. Returns cosine similarity."""
    e1 = np.array(embed1)
    e2 = np.array(embed2)
    
    # Normalize
    e1 = e1 / np.linalg.norm(e1)
    e2 = e2 / np.linalg.norm(e2)
    
    similarity = float(np.dot(e1, e2))
    
    return {
        "similarity": round(similarity, 6),
        "is_same": similarity > 0.4,
        "confidence": "high" if similarity > 0.6 else ("medium" if similarity > 0.4 else "low")
    }


def health_check():
    """Check model status."""
    model = get_model()
    return {
        "model": MODEL_NAME,
        "loaded": model is not None,
        "det_size": [640, 640]
    }