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chat.py
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chat.py
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#!/usr/bin/env python
# coding: utf-8
# In[ ]:
import nltk
import random
import numpy as np
import json
import pickle
from nltk.stem import WordNetLemmatizer
from tensorflow.keras.models import load_model
lemmatizer=WordNetLemmatizer()
with open('breastCancer.json') as json_file:
intents = json.load(json_file)
words=pickle.load(open('words.pkl','rb'))
classes=pickle.load(open('classes.pkl','rb'))
model=load_model('chatbotmodel.h5')
def clean_up_sentence(sentence):
sentence_words=nltk.word_tokenize(sentence)
sentence_words=[lemmatizer.lemmatize(word) for word in sentence_words]
return sentence_words
def bag_of_words(sentence):
sentence_words=clean_up_sentence(sentence)
bag=[0]*len(words)
for w in sentence_words:
for i,word in enumerate(words):
if word == w:
bag[i]=1
return np.array(bag)
def predict_class(sentence):
bow=bag_of_words(sentence)
res=model.predict(np.array([bow]))[0]
ERROR_THRESHOLD=0.25
results=[[i,r] for i,r in enumerate(res) if r> ERROR_THRESHOLD]
results.sort(key=lambda x:x[1],reverse=True)
return_list=[]
for r in results:
return_list.append({'intent': classes[r[0]],'probability':str(r[1])})
return return_list
def get_response(intents_list,intents_json):
result = None
tag=intents_list[0]['intent']
print(intents_list[0])
list_of_intents=intents_json['intents']
for i in list_of_intents:
if tag in i['tags']:
result=i['responses']
break
return result
def main_(message:str):
while True:
ints=predict_class(message)
if len(ints) > 0:
res=get_response(ints,intents)
return res
else:
return "I Donot know about it"
# In[ ]: