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import pandas as pd
from sklearn.externals import joblib
from flask import Flask, jsonify, request
from utiles import *
app = Flask(__name__)
@app.route('/predict', methods=['POST'])
def predict():
"""API request
req_json = request.get_json()
input = pd.read_json(req_json, orient='records')
except Exception as e:
raise e
if input.empty:
#Load the saved model
print("Cargar el modelo...")
loaded_model = cargarModeloSiEsNecesario()
print("Hacer Pronosticos")
continuas = input[['var1(t-7)','var1(t-6)','var1(t-5)','var1(t-4)','var1(t-3)','var1(t-2)','var1(t-1)']]
predictions = loaded_model.predict([input['weekday'], input['month'], continuas])
print("Transformando datos")
loaded_scaler = load_object('scaler_time_series.pkl')
inverted = loaded_scaler.inverse_transform(predictions)
inverted = inverted.astype('int32')
final_predictions = pd.DataFrame(inverted)
final_predictions.columns = ['ventas']
print("Enviar respuesta")
responses = jsonify(predictions=final_predictions.to_json(orient="records"))
responses.status_code = 200
print("Fin de Peticion")
return (responses)
global_model = None
def cargarModeloSiEsNecesario():
global global_model
if global_model is not None:
print('Modelo YA cargado')
return global_model
global_model = crear_modeloEmbeddings()
print('Modelo Cargado')
return global_model
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