def GET(self):
web.header('Content-Type', 'application/json')
user_data = web.input()
query = user_data.query
#Start with an empty list.
inputques = []
#Add the input query to our list.
inputques.append(query)
inputques = UsefulFunctions.tokenization_spellcheck(inputques)
im = UsefulFunctions.createTfidfVectorizer_Instance(inputques)
#Loading the tfidf matrix from disk
qm = np.load(Training.save_matrix_path)
coslist = cosine_similarity(qm, im).flatten()
maxsim = np.argmax(coslist)
response = Training.response[maxsim]
#returning response
return response
import pandas as pd
import numpy as np
import configparser
import UsefulFunctions
configParser = configparser.RawConfigParser()
configFilePath = r'C:\Users\Admin\Desktop\code-09-02\configfile.txt'
configParser.read(configFilePath)
data_path = configParser.get('file-config', 'data-path')
save_matrix_path = configParser.get('file-config', 'sparse-matrix-path')
#Reading Training data
df = pd.read_csv(data_path)
question,response=UsefulFunctions.columnstoList(df)
#Datacleaning and spell check
question = UsefulFunctions.tokenization_spellcheck(question)
#Saving Tfidf matrix into disk
qmatrix = UsefulFunctions.createTfidfVectorizer(question)
np.save(save_matrix_path,qmatrix)