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//paip/script/broiler_onoff/broiler_onoff.py

# Copyright (c) 2020~2023 PAIPTREE SmartFarm, Inc. All Rights Reserved.
# Title : 닭 입출하 감지
# Author: 박정훈
# Date : 2023.03.24

import tensorflow as tf
# from tensorflow import keras

import pandas as pd
import socket, warnings, os, sys, pymsteams, glob
from datetime import date, datetime, timedelta

if __name__ == '__main__':
    if len(sys.argv) <= 1 :
        print("fail - not input img_path")
        sys.exit()

print("입추 확인 시작!!")

img_path = sys.argv[1]
    print("Input img_path : ", img_path)

try :
        # find model file
        windows_model_path = 'D:\\gate\\script\\broiler_onoff\\model\\'
        linux_model_path = '/gate/script/broiler_onoff/model/'
        model_path = windows_model_path if os.name == 'nt' else linux_model_path
        #print("model_path : ", model_path)

model_file_list = glob.glob(model_path + '*')
        model_file_path = max(model_file_list, key=os.path.getmtime)
        #print("model_file_path : ", model_file_path)
        
        #수정/주석/삭제 금지 : gateway에서 이 구문을 통해 파일명 획득함
        print("model_file_name:", os.path.basename(model_file_path))

# load model
        loaded_model = tf.keras.models.load_model(model_file_path)
        #print("load model")

#img_path = '/gate/data/filesImageReal/20230324/H02_CT04,0_20230324122606_farm_image_real_a7741b014c3e.jpg'
        img = tf.keras.preprocessing.image.load_img(img_path, target_size=(540, 540))
        img_array = tf.keras.preprocessing.image.img_to_array(img)
        img_array = tf.expand_dims(img_array, 0)

prediction = loaded_model.predict(img_array)
        score = prediction[0]

print("score:", score)
        if (score[0] < score[1]) and score[1] >= 3 :
            print("Yes broiler!\n")
            print(1)
        else:
            print("No broiler!\n")
            print(0)
       
    except :
        print("fail")