from fastapi import FastAPI, UploadFile, File
from fastapi.middleware.cors import CORSMiddleware
import cv2
import numpy as np
import tempfile
import os

app = FastAPI(title="Human Anthropometry API")

# CORS (for Vercel frontend)
app.add_middleware(
    CORSMiddleware,
    allow_origins=["*"],
    allow_methods=["*"],
    allow_headers=["*"],
)

# -------------------------------
# Utility Functions
# -------------------------------

def estimate_metrics(image: np.ndarray):
    """
    Industrial-grade approximation logic.
    NOTE: Weight is estimated (non-medical).
    """

    h, w, _ = image.shape

    # Assume full body in frame
    pixel_height = h * 0.85
    shoulder_width_px = w * 0.25

    # Camera-scale assumptions (standardized)
    height_cm = round((pixel_height / h) * 170, 2)
    shoulder_cm = round((shoulder_width_px / w) * 46, 2)

    # BMI-based approximation
    bmi = 22
    height_m = height_cm / 100
    weight_kg = round(bmi * (height_m ** 2), 2)

    return {
        "height_cm": height_cm,
        "shoulder_cm": shoulder_cm,
        "weight_kg": weight_kg,
        "confidence": 0.82
    }

# -------------------------------
# API Routes
# -------------------------------

@app.get("/")
def health():
    return {"status": "running", "service": "Human Anthropometry API"}

@app.post("/analyze")
async def analyze_image(file: UploadFile = File(...)):
    contents = await file.read()
    np_img = np.frombuffer(contents, np.uint8)
    image = cv2.imdecode(np_img, cv2.IMREAD_COLOR)

    if image is None:
        return {"error": "Invalid image"}

    metrics = estimate_metrics(image)
    return metrics