algorithm_platform/models_processing/model_predict/model_prediction_bp.py

from flask import Flask,request
import time
import logging
import traceback
from itertools import chain
from common.database_dml import get_data_from_mongo,insert_data_into_mongo,get_h5_model_from_mongo,get_scaler_model_from_mongo
from common.processing_data_common import str_to_list
app = Flask('model_prediction_bp——service')


# 创建时间序列数据
def model_prediction(df,args):
    if 'is_limit' in df.columns:
        df = df[df['is_limit'] == False]
    features, time_steps, col_time, model_name,col_reserve,howlongago =  str_to_list(args['features']), int(args['time_steps']),args['col_time'],args['model_name'],str_to_list(args['col_reserve']),int(args['howlongago'])
    feature_scaler,target_scaler = get_scaler_model_from_mongo(args)
    df = df.sort_values(by=col_time).fillna(method='ffill').fillna(method='bfill')
    scaled_features = feature_scaler.transform(df[features])
    # X_predict, _ = create_sequences(scaled_features, [], time_steps)
    # 加载模型时传入自定义损失函数
    # model = load_model(f'{farmId}_model.h5', custom_objects={'rmse': rmse})
    model = get_h5_model_from_mongo(args)
    y_predict = list(chain.from_iterable(target_scaler.inverse_transform([model.predict(scaled_features).flatten()])))

    result = df[-len(y_predict):]
    result['predict'] = y_predict
    result.loc[result['predict'] < 0, 'predict'] = 0
    result['model'] = model_name
    result['howlongago'] = howlongago
    features_reserve = col_reserve + ['model', 'predict', 'howlongago']
    return result[list(set(features_reserve))]


@app.route('/model_prediction_bp', methods=['POST'])
def model_prediction_bp():
    # 获取程序开始时间  
    start_time = time.time()  
    result = {}
    success = 0
    print("Program starts execution!")
    try:
        args = request.values.to_dict()
        print('args',args)
        logger.info(args)
        power_df = get_data_from_mongo(args)
        model = model_prediction(power_df,args)
        insert_data_into_mongo(model,args)
        success = 1
    except Exception as e:
        my_exception = traceback.format_exc()
        my_exception.replace("\n","\t")
        result['msg'] = my_exception
    end_time = time.time() 
   
    result['success'] = success
    result['args'] = args
    result['start_time'] = time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(start_time))
    result['end_time'] = time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(end_time))
    print("Program execution ends!")
    return result


if __name__=="__main__":  
    print("Program starts execution!")
    logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')
    logger = logging.getLogger("model_prediction_bp log")
    from waitress import serve
    serve(app, host="0.0.0.0", port=10104)
    print("server start!")