问题描述
我已经使用python实现了一个聊天机器人。该聊天机器人向用户询问与房屋建造过程有关的问题（您可以在intents.json文件中检查问题模式）。聊天机器人主要有两个python文件，例如train.py（用于实现聊天机器人的模型）和gui.py（用于实现聊天机器人的聊天接口）。聊天机器人运行正常。
我的问题是如何将与用户的聊天机器人对话保存在MysqL数据库中。我试图找到一种解决方案，但是不幸的是，我找不到可行的解决方案。如果有人能回答这个问题，我将不胜感激。下面显示了主要python文件的代码结构。
import numpy as np
from keras.models import Sequential
from keras.layers import Dense,Activation,Dropout
from keras.optimizers import SGD
import random
import nltk
nltk.download('wordnet')
from nltk.stem import WordNetLemmatizer
lemmatizer = WordNetLemmatizer()
import json
import pickle
words=[]
classes = []
documents = []
ignore_letters = ['!','?',','.']
intents_file = open('intents.json').read()
intents = json.loads(intents_file)
for intent in intents['intents']:
for pattern in intent['patterns']:
#tokenize each word
word = nltk.word_tokenize(pattern)
words.extend(word)
#add documents in the corpus
documents.append((word,intent['tag']))
# add to our classes list
if intent['tag'] not in classes:
classes.append(intent['tag'])
print(documents)
# lemmaztize and lower each word and remove duplicates
words = [lemmatizer.lemmatize(w.lower()) for w in words if w not in ignore_letters]
words = sorted(list(set(words)))
# sort classes
classes = sorted(list(set(classes)))
# documents = combination between patterns and intents
print (len(documents),"documents")
# classes = intents
print (len(classes),"classes",classes)
# words = all words,vocabulary
print (len(words),"unique lemmatized words",words)
pickle.dump(words,open('words.pkl','wb'))
pickle.dump(classes,open('classes.pkl','wb'))
# create our training data
training = []
# create an empty array for our output
output_empty = [0] * len(classes)
# training set,bag of words for each sentence
for doc in documents:
# initialize our bag of words
bag = []
# list of tokenized words for the pattern
pattern_words = doc[0]
# lemmatize each word - create base word,in attempt to represent related words
pattern_words = [lemmatizer.lemmatize(word.lower()) for word in pattern_words]
# create our bag of words array with 1,if word match found in current pattern
for word in words:
bag.append(1) if word in pattern_words else bag.append(0)
# output is a '0' for each tag and '1' for current tag (for each pattern)
output_row = list(output_empty)
output_row[classes.index(doc[1])] = 1
training.append([bag,output_row])
# shuffle our features and turn into np.array
random.shuffle(training)
training = np.array(training)
# create train and test lists. X - patterns,Y - intents
train_x = list(training[:,0])
train_y = list(training[:,1])
print("Training data created")
# Create model - 3 layers. First layer 128 neurons,second layer 64 neurons and 3rd output layer contains number of neurons
# equal to number of intents to predict output intent with softmax
model = Sequential()
model.add(Dense(128,input_shape=(len(train_x[0]),),activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(64,activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(len(train_y[0]),activation='softmax'))
# Compile model. stochastic gradient descent with nesterov accelerated gradient gives good results for this model
sgd = SGD(lr=0.01,decay=1e-6,momentum=0.9,nesterov=True)
model.compile(loss='categorical_crossentropy',optimizer=sgd,metrics=['accuracy'])
#fitting and saving the model
hist = model.fit(np.array(train_x),np.array(train_y),epochs=200,batch_size=5,verbose=1)
model.save('chatbot_model.h5',hist)
print("model created")
2.gui.py（内容为聊天机器人的聊天界面。该界面由tkinter实现）
import nltk
from nltk.stem import WordNetLemmatizer
lemmatizer = WordNetLemmatizer()
import pickle
import numpy as np
from keras.models import load_model
model = load_model('chatbot_model.h5')
import json
import random
intents = json.loads(open('intents.json').read())
words = pickle.load(open('words.pkl','rb'))
classes = pickle.load(open('classes.pkl','rb'))
def clean_up_sentence(sentence):
# tokenize the pattern - splitting words into array
sentence_words = nltk.word_tokenize(sentence)
# stemming every word - reducing to base form
sentence_words = [lemmatizer.lemmatize(word.lower()) for word in sentence_words]
return sentence_words
# return bag of words array: 0 or 1 for words that exist in sentence
def bag_of_words(sentence,words,show_details=True):
# tokenizing patterns
sentence_words = clean_up_sentence(sentence)
# bag of words - vocabulary matrix
bag = [0]*len(words)
for s in sentence_words:
for i,word in enumerate(words):
if word == s:
# assign 1 if current word is in the vocabulary position
bag[i] = 1
if show_details:
print ("found in bag: %s" % word)
return(np.array(bag))
def predict_class(sentence):
# filter below threshold predictions
p = bag_of_words(sentence,show_details=False)
res = model.predict(np.array([p]))[0]
ERROR_THRESHOLD = 0.25
results = [[i,r] for i,r in enumerate(res) if r>ERROR_THRESHOLD]
# sorting strength probability
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 getResponse(ints,intents_json):
tag = ints[0]['intent']
list_of_intents = intents_json['intents']
for i in list_of_intents:
if(i['tag']== tag):
result = random.choice(i['responses'])
break
return result
#Creating tkinter GUI
import tkinter
from tkinter import *
def send():
msg = EntryBox.get("1.0",'end-1c').strip()
EntryBox.delete("0.0",END)
if msg != '':
ChatBox.config(state=norMAL)
ChatBox.insert(END,"You: " + msg + '\n\n')
ChatBox.config(foreground="#446665",font=("Verdana",12 ))
ints = predict_class(msg)
res = getResponse(ints,intents)
ChatBox.insert(END,"Bot: " + res + '\n\n')
ChatBox.config(state=disABLED)
ChatBox.yview(END)
root = Tk()
root.title("chatbot")
root.geometry("400x500")
root.resizable(width=FALSE,height=FALSE)
#Create Chat window
ChatBox = Text(root,bd=0,bg="white",height="8",width="50",font="Arial",)
ChatBox.config(state=disABLED)
#Bind scrollbar to Chat window
scrollbar = Scrollbar(root,command=ChatBox.yview,cursor="heart")
ChatBox['yscrollcommand'] = scrollbar.set
#Create Button to send message
SendButton = Button(root,12,'bold'),text="Send",width="12",height=5,bg="#f9a602",activebackground="#3c9d9b",fg='#000000',command= send )
#Create the Box to enter message
EntryBox = Text(root,width="29",height="5",font="Arial")
#EntryBox.bind("<Return>",send)
#Place all components on the screen
scrollbar.place(x=376,y=6,height=386)
ChatBox.place(x=6,height=386,width=370)
EntryBox.place(x=128,y=401,height=90,width=265)
SendButton.place(x=6,height=90)
root.mainloop()
3.intents.json（此文件包含根据JSON格式的不同标记，模式和响应）
{ "intents" : [
{"tag":"greeting","patterns": ["Hi","Hello","Good Day"],"responses":["Hi,This is the building chatbot,How Can I Help You?"],"context_set":""
},{"tag":"LandShape","patterns":["I need to get the details of the Home Plans","Hi I like to kNow about the samples house plans"],"responses":["Sure,I can Help you. What is the shape of your land?"],{"tag":"LandSize","patterns":["Circle","Rectangle","Square","Long Land","Trapezium","Triangle"],"responses":["What is the size of your land?"],{"tag":"FamilyInfo","patterns":["10 perches","12 perches","13 perches","14 perches","15 perches","18 perches","20 perches","22 perches","25 perches","30 perches","50 perches","100 perches"],"responses":["How many members in your family? "],{"tag":"FloorType","patterns":["1","2","3","4","5","6","7","8","9","10"],"responses":["What is the house type you want One storey or two storey?"],{"tag":"OneFloorHome","patterns":["One Storey Home"],"responses":["How many living rooms need for the home?"],{"tag":"OneFloorLiving","patterns":["One Living Room","Two Living Rooms","Three Living Rooms"],"responses":["How many bed rooms need for your home?"],{"tag":"OneFloorbed","patterns":["One bed Room","Two bed Rooms","Three bed Rooms","Four bed Rooms","Five bed Rooms"],"responses":["How many bathrooms need for your home?"],{"tag":"OneFloorBathroom","patterns":["One Bathroom","Two Bathrooms","Three Bathrooms"],"responses":["Do you need the bathrooms as attached or non-attached?"],{"tag":"OneFloorBathroomType","patterns":["Attached","Non-Attached"],"responses":["How many kitchen need for your home?"],{"tag":"OneFloorKitchen","patterns":["One kitchen","Two Kitchen","Three Kitchen"],"responses":["How many dining areas need for your home?"],{"tag":"OneFloorRoof","patterns":["One Dining Area","Two Dining Areas","Three Dining Areas"],"responses":["Does you need partial or non-partial roof?"],{"tag":"OneFloorRoofRes","patterns":["partial","full"],"responses":["What are the other additional features you suppose to add?"],{"tag":"OneFloorAdditionalFeatures","patterns":["children play area","ReligIoUs Place","Home Gym","nothing special"],"responses":["Thanks for your responses"],{"tag":"OneFloorConclusion","patterns":["Thanks for the assistance"],"responses":["Thanks for the time. collected your details!!"],{"tag":"TwoFloorHome","patterns":["Two Storey Home"],"responses":["How many living rooms need for the first floor?"],{"tag":"TwoFloorLiving","patterns":["1 living room","2 living rooms","3 living rooms"],"responses":["How many bed rooms need for the first floor?"],{"tag":"TwoFloorbed","patterns":["1 bed room","2 bed rooms","3 bed rooms","4 bed rooms","5 bed rooms"],"responses":["How many bathrooms need for the first floor?"],{"tag":"TwoFloorBathroom","patterns":["1 bathroom","2 bathrooms","3 bathrooms","4 bedrooms"],{"tag":"TwoFloorBathroomType","responses":["How many kitchen need for the first floor?"],{"tag":"TwoFloorKitchen","patterns":["1 kitchen","2 kitchen","3 kitchen"],"responses":["How many dining areas need for the first floor?"],{"tag":"TwoFloorDining","patterns":["1 Dining Area","2 Dining Areas","3 Dining Areas","4 Dining Areas"],"responses":["How many living rooms need for the second floor?"],{"tag":"TwoFloorLivingSecond","patterns":["one living","two living","three living","four living"],"responses":["How many bed rooms need for the second floor?"],{"tag":"TwoFloorbedSecond","patterns":["one bed","two bed","three bed","four bed","five bed"],"responses":["How many bathrooms need for the second floor?"],{"tag":"TwoFloorBathroomSecond","patterns":["one bath","two bath","three bath","four bath"],"responses":["How many kitchen need for the second floor?"],{"tag":"TwoFloorKitchenSecond","patterns":["one dining","two dining","three dining","four dining"],"responses":["How many dining ares need for the second floor?"],{"tag":"TwoFloorDiningSecond","responses":["Thank you for the responses"],{"tag":"TwoFloorConclusion","patterns":["ok thank you for the assistance"],"responses":["Thanks for your time. collected the details!!"],"context_set":""
}
] }
解决方法
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