Comment créer un Chatbot avec Python

 pip install tensorflow keras pickle nltk

Python 
 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
 from nltk.stem import WordNetLemmatizer
 lemmatizer = WordNetLemmatizer()
 import json
 import pickle
 intents_file = open('intents.json').read()
 intents = json.loads(intents_file) 

Python 
 words=[]
 classes = []
 documents = []
 ignore_letters = ['!', '?', ',', '.']
 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) 

Python 
 # 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')) 

Python 
 # create the training data
 training = []
 # create empty array for the output
 output_empty = [0] * len(classes)
 # training set, bag of words for every sentence
 for doc in documents:
     # initializing bag of words
     bag = []
     # list of tokenized words for the pattern
     word_patterns = doc[0]
     # lemmatize each word - create base word, in attempt to represent related words
     word_patterns = [lemmatizer.lemmatize(word.lower()) for word in word_patterns]
     # create the bag of words array with 1, if word is found in current pattern
     for word in words:
         bag.append(1) if word in word_patterns 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 the features and make numpy array
 random.shuffle(training)
 training = np.array(training)
 # create training and testing lists. X - patterns, Y - intents
 train_x = list(training[:,0])
 train_y = list(training[:,1])
 print("Training data is created"

Python 
 # deep neural networds model
 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'))
 # Compiling model. SGD 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'])
 #Training 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 is created")

Python 
 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, words,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, font=("Verdana",12,'bold'), text="Send", width="12", height=5,
                     bd=0, bg="#f9a602", activebackground="#3c9d9b",fg='#000000',
                     command= send )
 #Create the box to enter message
 EntryBox = Text(root, bd=0, bg="white",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,y=6, height=386, width=370)
 EntryBox.place(x=128, y=401, height=90, width=265)
 SendButton.place(x=6, y=401, height=90)
 root.mainloop()

 python train_chatbot.py