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chatbot.py
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chatbot.py
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# _____TF-IDF libraries_____
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.metrics.pairwise import cosine_similarity
from sklearn.feature_extraction.text import TfidfVectorizer
import numpy as np
# _____helper Libraries_____
import pickle
import csv
import json
import timeit
import random
#import os
def talk_to_cb_primary(test_set_sentence, minimum_score , json_file_path , tfidf_vectorizer_pikle_path ,tfidf_matrix_train_pikle_path):
# json_file_path = "data/convertcsv.json"
# tfidf_vectorizer_pikle_path = "data/tfidf_vectorizer.pickle"
# tfidf_matrix_train_pikle_path ="data/tfidf_matrix_train.pickle"
test_set = (test_set_sentence, "")
try:
##--------------to use------------------#
f = open(tfidf_vectorizer_pikle_path, 'rb')
tfidf_vectorizer = pickle.load(f)
f.close()
f = open(tfidf_matrix_train_pikle_path, 'rb')
tfidf_matrix_train = pickle.load(f)
f.close()
# ----------------------------------------#
except:
# ---------------to train------------------#
tfidf_vectorizer , tfidf_matrix_train = train_chat(json_file_path , tfidf_vectorizer_pikle_path , tfidf_matrix_train_pikle_path)
# -----------------------------------------#
tfidf_matrix_test = tfidf_vectorizer.transform(test_set)
cosine = cosine_similarity(tfidf_matrix_test, tfidf_matrix_train)
cosine = np.delete(cosine, 0)
max = cosine.max()
response_index = 0
if (max > minimum_score):
new_max = max - 0.01
list = np.where(cosine > new_max)
# print ("number of responses with 0.01 from max = " + str(list[0].size))
response_index = random.choice(list[0])
else :
return "live_chat" , 0
# else:
#print ("not sure")
#print ("max is = " + str(max))
#response_index = np.where(cosine == max)[0][0] + 2 # no offset at all +3
j = 0
with open(json_file_path, "r") as sentences_file:
reader = json.load(sentences_file)
for row in reader:
j += 1 # we begin with 1 not 0 & j is initialized by 0
if j == response_index:
#if delimeter in row[1]:
# # get newest suggestion
# answer_row = row[1].split(delimeter)
# row[1] = answer_row[1]
#else: # add new suggestion
# note = "just return old original suggestion"
return row["response"], max
break
#def previous_chats(query):
# minimum_score = 0.7
# file = "data/previous_chats.json"
# tfidf_vectorizer_pikle_path = "data/previous_tfidf_vectorizer.pickle"
# tfidf_matrix_train_path = "data/previous_tfidf_matrix_train.pickle"
# query_response, score = talk_to_cb_primary(query , minimum_score , file , tfidf_vectorizer_pikle_path , tfidf_matrix_train_path)
# return query_response , score
def train_chat(json_file_path, tfidf_vectorizer_pikle_path , tfidf_matrix_train_pikle_path):
i = 0
sentences = []
# enter your test sentence
# 3ashan yzabt el indexes
sentences.append(" No you.")
sentences.append(" No you.")
start = timeit.default_timer()
# enter jabberwakky sentence
with open(json_file_path, "r") as sentences_file:
reader = json.load(sentences_file)
# reader.next()
# reader.next()
for row in reader:
# if i==stop_at_sentence:
# break
sentences.append(row["message"])
i += 1
tfidf_vectorizer = TfidfVectorizer()
tfidf_matrix_train = tfidf_vectorizer.fit_transform(sentences) # finds the tfidf score with normalization
# tfidf_matrix_test =tfidf_vectorizer.transform(test_set)
stop = timeit.default_timer()
print ("training time took was : ")
print (stop - start)
f = open(tfidf_vectorizer_pikle_path, 'wb')
pickle.dump(tfidf_vectorizer, f)
f.close()
f = open(tfidf_matrix_train_pikle_path, 'wb')
pickle.dump(tfidf_matrix_train, f)
f.close()
return tfidf_vectorizer , tfidf_matrix_train
# -----------------------------------------#
def previous_chats(query):
minimum_score = 0.7
file = "D:/Projects/Bot/data/previous_chats.json"
tfidf_vectorizer_pikle_path = "D:/Projects/Bot/data/previous_tfidf_vectorizer.pickle"
tfidf_matrix_train_path = "D:/Projects/Bot/data/previous_tfidf_matrix_train.pickle"
query_response, score = talk_to_cb_primary(query , minimum_score , file , tfidf_vectorizer_pikle_path , tfidf_matrix_train_path)
return query_response
while 1:
sent = input("ishika : ")
print(previous_chats(sent))