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 core.defect_detection_image 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", [])

    # -------------------------------
    # BASIC VALIDATION
    # -------------------------------
    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")
    model = model_path

    # =====================================================
    # BASE64 IMAGE DETECTION (NOT VIDEO STREAM)
    # =====================================================
    logger.info(f"[START] Station {station_id} → {len(cameras)} camera(s) with base64 images")

    # Jalankan detection di 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": "Base64 image detection is running in background."
        }
    )


# @app.post("/start-detection") # live stream
# 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)