def rossmann_predict():
test_json = request.get_json()
if test_json: # there is data
if isinstance( test_json, dict ): # unique example
test_raw = pd.DataFrame( test_json, index=[0] )
else: # multiple example
test_raw = pd.DataFrame( test_json, columns=test_json[0].keys() )
# Instantiate Rossmann class
pipeline = Rossmann()
# data cleaning
df1 = pipeline.data_cleaning( test_raw )
# feature engineering
df2 = pipeline.feature_engineering( df1 )
# data preparation
df3 = pipeline.data_preparation( df2 )
# prediction
df_response = pipeline.get_prediction( model, test_raw, df3 )
return df_response
else:
return Response( '{}', status=200, mimetype='application/json' )
def rossmanPredict():
test_JSON = request.get_json()
if test_JSON: #there is data
if isinstance(test_JSON, dict):
teste_raw = pd.DataFrame(test_JSON, index=[0]) #unique example
else:
teste_raw = pd.DataFrame(
test_JSON, columns=test_JSON[0].keys()) #multiple examples
# Instantiate
pipeline = Rossmann()
# Data Cleaning
df1 = pipeline.data_cleaning(teste_raw)
# Feature Engineering
df2 = pipeline.feature_engineering(df1)
# Data Preparation
df3 = pipeline.data_preparation(df2)
# Prediction
df_response = pipeline.get_prediction(model, teste_raw, df3)
return df_response
else:
return Response('{}', status=200, mimetype='application/json')
def rossmann_predict():
json_request = request.get_json()
if json_request: # there is data
if isinstance( json_request, dict ): # unique example
df_raw = pd.DataFrame( json_request, index=[0] )
else: # multiple example
df_raw = pd.DataFrame( json_request, columns=json_request[0].keys() )
pipeline = Rossmann()
df1 = pipeline.data_cleaning( df_raw )
df2 = pipeline.feature_engineering( df1 )
df3 = pipeline.data_preparation( df2 )
df_response = pipeline.get_prediction( model, df_raw, df3 )
return df_response
else:
return Reponse( '{}', status=200, mimetype='application/json' )
