app.py

from fastapi import FastAPI
from fastapi.responses import JSONResponse
from fastapi.middleware.cors import CORSMiddleware
from ultralytics import YOLO

from typing import Dict

from core.defect_detection import *
from utils import *

import uvicorn, asyncio

# ============================================================
# APP SETUP
# ============================================================
app = FastAPI(
    title="AI Engine Dummy",
    version="1.0.0",
    description="""
    ## 🧠 AI Engine Dummy API

    API simulasi integrasi **AI Engine** untuk deteksi defect pada sistem monitoring.

    ---
    ### 🔹 Endpoint Utama
    - `/start-detection` → Memulai simulasi deteksi untuk beberapa kamera.
    
    ### 🔹 Webhook
    - Gunakan https://webhook.site/ untuk menerima hasil deteksi.
    - Pastikan mengisi `webhook_url` pada payload request.
    
    ### 🔹 Simulasi
    - Tiap kamera akan melakukan deteksi selama max 5 (Sesuai Waktu Timeout) detik.
    - Jika ditemukan defect, hasil langsung dikirim ke webhook dan semua kamera berhenti.
    - Jika semua kamera tidak menemukan defect setelah 5 (Sesuai Waktu Timeout) detik → status "OK" dikirim satu kali.
    """,
)

# ============================================================
# CORS CONFIG (Hanya port 8899)
# ============================================================
allowed_origins = [
    "*",
    # "http://localhost:8899",
    # "http://127.0.0.1:8899",
    # "http://0.0.0.0:8899",
]

app.add_middleware(
    CORSMiddleware,
    allow_origins=allowed_origins,
    allow_credentials=True,
    allow_methods=["*"],
    allow_headers=["*"],
)


# ============================================================
# ROUTES
# ============================================================
@app.get("/")
def read_root():
    return {"message": "Defect Detection API is running."}


@app.post("/start-detection")
async def start_detection(data: Dict):
    station_id  = data.get("station_id")
    parts       = data.get("parts")
    webhook_url = data.get("webhook_url")
    cameras     = data.get("cameras", [])

    if not station_id or not parts or not webhook_url or not cameras:
        return JSONResponse(
                status_code=400,
                content={
                    "status": "error", 
                    "station_id": station_id,
                    "camera_count": len(cameras),
                    "message": "Missing required fields"
                }
            )

    # -------------------------------
    # VALIDATION BEFORE EXECUTION
    # -------------------------------
    required_parts_fields = [
        "id", 
        "pin_api", 
        "name", 
        "sku"
    ]
    validation_errors = validate_input(required_parts_fields, station_id, cameras, parts, webhook_url)

    if validation_errors:
        logger.error("[VALIDATION FAILED] Input data is invalid.")
        for err in validation_errors:
            logger.error(f" - {err}")
        return JSONResponse(
                status_code=400,
                content={
                    "status": "error", 
                    "station_id": station_id,
                    "camera_count": len(cameras),
                    "message": " | ".join(validation_errors)
                }
            )
    logger.info(f"[INFO] Get metadata parts")
    model_path = model_by_id_metadata(parts['id'])

    logger.info(f"[INFO] Checking model_path")
    if isinstance(model_path, str):
        if not os.path.exists(model_path):
            logger.info(f"[INFO] Model file not found")
            return {"status": "error", "message": f"Model file not found: {model_path}"}
        model = YOLO(model_path)
    else:
        model = model_path

    logger.info(f"[START] Station {station_id} → {len(cameras)} kamera diproses")

    # running background
    asyncio.create_task(run_detection_group(station_id, cameras, webhook_url, model, parts))

    return JSONResponse(
            status_code=200,
            content={
            "status": "started",
            "station_id": station_id,
            "camera_count": len(cameras),
            "message": "Detection is running in background."
            }
        )


# ============================================================
# ENTRY POINT
# ============================================================
if __name__ == "__main__":
    uvicorn.run(app, host="0.0.0.0", port=7860)