from fastapi import FastAPI, File, UploadFile
from fastapi.middleware.cors import CORSMiddleware
from ultralytics import YOLO
import cv2
import numpy as np
from PIL import Image
import io

app = FastAPI(title="Pothole Detection API")

# السماح للـ Flutter بالاتصال بالـ API
app.add_middleware(
    CORSMiddleware,
    allow_origins=["*"],
    allow_credentials=True,
    allow_methods=["*"],
    allow_headers=["*"],
)

# تحميل الموديل الجاهز من الـ Hugging Face أوتوماتيك
model = YOLO('Harisanth/Pothole-Finetuned-YOLOv8')

@app.get("/")
def home():
    return {"message": "Pothole Detection API is running بنجاح!"}

@app.post("/predict")
async def predict_pothole(file: UploadFile = File(...)):
    # قراءة الصورة
    request_object_content = await file.read()
    image = Image.open(io.BytesIO(request_object_content)).convert("RGB")
    
    # تحويل الصورة لصيغة OpenCV
    open_cv_image = np.array(image)
    open_cv_image = open_cv_image[:, :, ::-1].copy()

    # تشغيل الموديل
    results = model(open_cv_image)
    
    boxes_count = 0
    pothole_detected = False
    
    for result in results:
        boxes_count = len(result.boxes)
        if boxes_count > 0:
            pothole_detected = True

    # الرد لزميلك بتاع الفلتر
    return {
        "pothole_detected": pothole_detected,
        "number_of_potholes": boxes_count,
        "status": "success"
    }