from fastapi import FastAPI
from pydantic import BaseModel
from deepface import DeepFace
import cv2
import numpy as np
import requests
import base64

app = FastAPI()
MODEL_NAME = "ArcFace"
DETECTOR_BACKEND = "opencv"
DISTANCE_METRIC = "euclidean_l2"
STRICT_THRESHOLD = 0.85

class VerifyRequest(BaseModel):
    url_goc: str
    live_base64: str

# THỦ THUẬT: HÀM TỰ ĐỘNG BÓP NHỎ ẢNH ĐỂ AI CHẠY NHANH X10
def resize_image(img, max_width=600):
    h, w = img.shape[:2]
    if w > max_width:
        ratio = max_width / float(w)
        dim = (max_width, int(h * ratio))
        img = cv2.resize(img, dim, interpolation=cv2.INTER_AREA)
    return img

def url_to_image(url):
    resp = requests.get(url)
    image = np.asarray(bytearray(resp.content), dtype="uint8")
    img = cv2.imdecode(image, cv2.IMREAD_COLOR)
    return resize_image(img) # <--- Bóp nhỏ ảnh gốc

def base64_to_image(base64_string):
    if "," in base64_string:
        base64_string = base64_string.split(",")[1]
    img_data = base64.b64decode(base64_string)
    nparr = np.frombuffer(img_data, np.uint8)
    img = cv2.imdecode(nparr, cv2.IMREAD_COLOR)
    return resize_image(img) # <--- Bóp nhỏ ảnh selfie

@app.post("/api/ai/verify-face")
def verify_face(req: VerifyRequest):
    try:
        img_goc = url_to_image(req.url_goc)
        img_live = base64_to_image(req.live_base64)

        # CHẠY AI (Đã đổi sang opencv cho tốc độ bàn thờ)
        ket_qua = DeepFace.verify(
            img1_path=img_goc,
            img2_path=img_live,
            model_name=MODEL_NAME,
            detector_backend=DETECTOR_BACKEND,   
            distance_metric=DISTANCE_METRIC, 
            enforce_detection=True
        )

        distance = float(ket_qua['distance'])
        
        # Ngưỡng khắt khe (Cố định ở 0.85)
        is_verified = True if distance <= STRICT_THRESHOLD else False

        print(f"\n[AI REPORT] Khoảng cách: {distance:.4f} (Ngưỡng: {STRICT_THRESHOLD}) -> PASS: {is_verified}\n")

        return {
            "verified": is_verified, 
            "distance": distance,
            "message": "Quét thành công"
        }
    except Exception as e:
        return {"verified": False, "message": f"Lỗi AI: {str(e)}"}
    
@app.get("/")
def root():
    return {
        "status": "AI server is running",
        "docs": "/docs",
        "health": "/api/ai/health"
    }


@app.get("/api/ai/health")
def health_check():
    return {
        "status": "OK",
        "model": MODEL_NAME,
        "detector": DETECTOR_BACKEND,
        "metric": DISTANCE_METRIC,
        "threshold": STRICT_THRESHOLD
    }