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chat_bot_main.py
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chat_bot_main.py
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from tflearn_model import data_preprocessing, trained_model
from nltk.stem.lancaster import LancasterStemmer
import nltk
import numpy as np
import json
import enchant
import random
#from enchant.checker import SpellChecker
import re
stemmer = LancasterStemmer()
def cleaning_input_data(sentence):
# tokenize the pattern
sentence_words = nltk.word_tokenize(sentence)
# stem each word
sentence_words = [stemmer.stem(word.lower()) for word in sentence_words]
return sentence_words
# return bag of words array: 0 or 1 for each word in the bag that exists in the sentence
def data_encode(sentence, intents, show_details=False):
documents, classes, words = data_preprocessing(intents)
# tokenize the pattern
sentence_words = cleaning_input_data(sentence)
# bag of words
bag = [0]*len(words)
for s in sentence_words:
for i,w in enumerate(words):
if w == s:
bag[i] = 1
if show_details:
print ("found in bag: %s" % w)
return(np.array(bag))
with open('doctor_intent.json') as json_data:
intents = json.load(json_data)
ERROR_THRESHOLD = 0.25
def classify(intents, sentence, model):
_, classes, _= data_preprocessing(intents)
# generate probabilities from the model
results = model.predict([data_encode(sentence, intents)])[0]
print (results[0])
# filter out predictions below a threshold
results = [[i,r] for i,r in enumerate(results) if r>ERROR_THRESHOLD]
# sort by strength of probability
results.sort(key=lambda x: x[1], reverse=True)
return_list = []
print(classes)
for r in results:
print(r)
return_list.append((classes[r[0]], r[1]))
# return tuple of intent and probability
return return_list
def output_message(intents, sentence, model, userID='123', show_details=False):
results = classify(intents, sentence, model)
# if we have a classification then find the matching intent tag
if results:
# loop as long as there are matches to process
while results:
for i in intents['intents']:
# find a tag matching the first result
if i['tag'] == results[0][0]:
# a random response from the intent
return print("Dr. RK -> ",random.choice(i['responses']))
results.pop(0)
#d = enchant.Dict("en_GB")
d = enchant.request_pwl_dict("english_dict.txt")
model = trained_model(intents)
while True:
sentence = input("")
if sentence.lower().strip() == "bye":
break
else:
output = output_message(intents, sentence, model)