# app.py
import base64
import cv2
import numpy as np
import requests
from fastapi import FastAPI
from pydantic import BaseModel
import insightface

# Load Face Detector + Recognition Model (first import may download weights)
model = insightface.app.FaceAnalysis(name="buffalo_l")
model.prepare(ctx_id=0, det_size=(640, 640))

app = FastAPI(title="Face Compare API")

class CompareRequest(BaseModel):
    image1: str | None = None     # base64
    image2: str | None = None     # base64
    image1_url: str | None = None # URL
    image2_url: str | None = None # URL

def b64_to_img(b64_string: str):
    try:
        img_data = base64.b64decode(b64_string)
        arr = np.frombuffer(img_data, np.uint8)
        img = cv2.imdecode(arr, cv2.IMREAD_COLOR)
        return img
    except Exception:
        return None

def url_to_img(url: str):
    try:
        resp = requests.get(url, timeout=10)
        arr = np.frombuffer(resp.content, np.uint8)
        img = cv2.imdecode(arr, cv2.IMREAD_COLOR)
        return img
    except Exception:
        return None

def get_embedding(img):
    faces = model.get(img)
    if len(faces) == 0:
        return None
    return faces[0].embedding

@app.post("/compare")
async def compare_faces(req: CompareRequest):
    # Load images (prefer raw base64, else url)
    img1 = b64_to_img(req.image1) if req.image1 else (url_to_img(req.image1_url) if req.image1_url else None)
    img2 = b64_to_img(req.image2) if req.image2 else (url_to_img(req.image2_url) if req.image2_url else None)

    if img1 is None or img2 is None:
        return {"error": "Invalid image data or URL."}

    emb1 = get_embedding(img1)
    emb2 = get_embedding(img2)

    if emb1 is None or emb2 is None:
        return {"error": "No face detected in one or both images."}

    # cosine similarity
    similarity = float(np.dot(emb1, emb2) / (np.linalg.norm(emb1) * np.linalg.norm(emb2)))
    matched = similarity > 0.55

    return {"similarity": similarity, "match": matched}