import pandas as pd # from pycaret.classification import * import librosa as fe data = pd.read_csv('data/data_all.csv') data.timestamp = data.timestamp.apply(lambda x: pd.Timestamp(x)) data['month'] = data.timestamp.dt.month data['hour'] = data.timestamp.dt.hour data['dayofweek'] = data.timestamp.dt.dayofweek def f_e(df, cn='hum'): # delta feature df[f'd_{cn}'] = fe.feature.delta(df[f'{cn}']) df[f'dd_{cn}'] = fe.feature.delta(df[f'd_{cn}'], order=2) return df f_e(data, 'hum') f_e(data, 'temp') data.to_csv('data/data_all_feature_eng.csv', index=False) tmp = (data[data['timestamp'] == '2021-08-30 08:09:00']) out = tmp[['hum', 'd_hum', 'dd_hum', 'temp', 'd_temp', 'dd_temp', 'door', 'motion', 'illum', 'dayofweek', 'month', 'hour']] v = out.values.tolist()[0] print(v) v = ["{}".format(x) for x in v] print(v) import requests URL = f'http://localhost:8000/predict?hum={v[0]}&d_hum={v[1]}&dd_hum={v[2]}&temp={v[3]}&d_temp={v[4]}&dd_temp={v[5]}&door={v[6]}&motion={v[7]}&illum={v[8]}&dayofweek={v[9]}&month={v[10]}&hour={v[11]}' response = requests.get(URL) print(response.status_code) print(response.text)