from chatterbot import ChatBot from chatterbot.trainers import ListTrainer from chatterbot.conversation import Statement from chatterbot.trainers import ChatterBotCorpusTrainer import nltk import ssl try: _create_unverified_https_context = ssl._create_unverified_context except AttributeError: pass else: ssl._create_default_https_context = _create_unverified_https_context #initializes the trainer bot trainer_bot = ChatBot(name="trainer") #Make a new trainer to train the trainer bot trainer = ChatterBotCorpusTrainer(trainer_bot) #Trains the chatbot on the english corpus trainer.train("chatterbot.corpus.english")
def _baseTrain(self): trainer = ChatterBotCorpusTrainer(self.chatbot) trainer.train('chatterbot.corpus.english')
'r') as file: data = file.readlines() while format_line < num_lines: if format_line == 0: data[format_line] = "- " + data[format_line] format_line = format_line + 1 else: data[format_line] = " " + data[format_line] format_line = format_line + 1 with open( r'C:\Users\wenka_000\Anaconda3\envs\chat\Lib\site-packages\chatterbot_corpus\data\training\trained.yml', 'w') as file: file.write("categories:\n") file.write("- trained\n") file.write("conversations:\n") file.writelines(data) english_bot = ChatBot( "Chatterbot", storage_adapter="chatterbot.storage.MongoDatabaseAdapter", logic_adapters=['chatterbot.logic.BestMatch'], database_uri="mongodb://localhost:27017/chatbot") trainer = ChatterBotCorpusTrainer(english_bot) trainer.train("chatterbot.corpus.training") myclient = pymongo.MongoClient("mongodb://localhost:27017/") mydb = myclient["chatbot"] mycol = mydb["training"] mycol.drop()
def setUp(self): super().setUp() self.trainer = ChatterBotCorpusTrainer(self.chatbot, show_training_progress=False)
from chatterbot import ChatBot from chatterbot.trainers import ListTrainer from chatterbot.trainers import ChatterBotCorpusTrainer import json import pyttsx3 import datetime import random ChatBot = ChatBot(name = 'BankBot', read_only = False, logic_adapters = ["chatterbot.logic.BestMatch"], storage_adapter = "chatterbot.storage.SQLStorageAdapter") corpus_trainer = ChatterBotCorpusTrainer(ChatBot) corpus_trainer.train("chatterbot.corpus.english") greet_conversation = [ "yes", "ok", "good", "glad to hear it" "Hello", "Hi there!", "How are you doing?", "I'm doing great.", "That is good to hear", "Thank you.", "how are you?", "i am ok", "are you ok?", random.choice(["yes Why not?", 'It dependes']), "How old are you", "I burned In 2020 09/01/20",
from flask import Flask, render_template, request from chatterbot import ChatBot from chatterbot.trainers import ChatterBotCorpusTrainer, ListTrainer import playsound import speech_recognition as sr from gtts import gTTS import random app = Flask(__name__) credi_bot = ChatBot("CrediBot", storage_adapter="chatterbot.storage.SQLStorageAdapter", database_uri='sqlite:///database.sqlite3') trainer = ChatterBotCorpusTrainer(credi_bot) trainer.train("./data/creditos.yml") trainer.export_for_training('./traning.json') @app.route("/") def home(): return render_template("index.html") @app.route("/get") def get_bot_response(): userText = request.args.get('msg') return str(credi_bot.get_response(userText)) # return speak(str(daliaBot.get_response(userText)))
'hi there!', 'hi!', 'how do you do?', 'how are you?', 'i\'m cool.', 'fine, you?', 'always cool.', 'i\'m ok.', 'glad to hear that.', 'i\'m fine.', 'glad to hear that!', 'i feel awesome!', 'excellent, glad to hear that!', 'not so good.', 'sorry to hear that.', 'what\'s your name?', 'i\'m Kix. ask me a question.' ] math_talk1 = [ 'pythagorean theorem.', 'a squared plus b squared equals c squared!' ] math_talk2 = ['law of cosines', 'c**2 = a**2 + b**2 - 2 * a * b * cos(gamma)'] list_trainer = ListTrainer(shit) for item in (small_talk, math_talk1, math_talk2): list_trainer.train(item) corpus_trainer = ChatterBotCorpusTrainer(shit) corpus_trainer.train('chatterbot.corpus.english') #_input = input("Say something!: ") while True: try: _input = shit.get_response(input()) print(_input) except (KeyboardInterrupt, EOFError, SystemExit): break
def train_on_corpuses(): trainer = ChatterBotCorpusTrainer(bot) trainer.train(*CORPUSES)
database_uri='mongodb+srv://Gabby:[email protected]/test?retryWrites=true&w=majority',# pylint: disable=line-too-long statement_comparison_function=levenshtein_distance, filters=[ 'chatterbot.filters.RepetitiveResponseFilter'], preprocessors=[ 'chatterbot.preprocessors.clean_whitespace'], logic_adapters=[ { 'import_path': 'chatterbot.logic.BestMatch', 'threshold': 0.85, 'default_response': 'I am sorry, but I do not understand.' } ] ) TRAINER = ChatterBotCorpusTrainer(ENGLISH_BOT) # For training Custom corpus data #TRAINER.train("./data/mycorpus/") # For training English corpus data #TRAINER.train('chatterbot.corpus.english') # For training list of conversations #TRAINER_LIST = ListTrainer(ENGLISH_BOT) #TRAINER_LIST.train([ # "How are you?", # "I am good.", # "That is good to hear.", # "Thank you", # "You are welcome.",
def get_chatterbot_corpus_trainer(chatbot): return ChatterBotCorpusTrainer(chatbot, show_training_progress=False)
def get_ChatBot(self): if self.bot is None: self.bot = ChatBot('Norman') trainer = ChatterBotCorpusTrainer(self.bot) trainer.train("chatterbot.corpus.english") return self.bot
from chatterbot.trainers import ListTrainer from pymongo import MongoClient from datetime import datetime app = Flask(__name__) bot = ChatBot( 'TiPi', #storage_adapter="chatterbot.storage.MongoDatabaseAdapter", #database="tipi_db", #database_uri="mongodb://localhost/tipi_db", logic_adapters=['chatterbot.logic.BestMatch'], filters=['chatterbot.filters.RepetitiveResponseFilter']) training = ChatterBotCorpusTrainer(bot) training.train("chatterbot.corpus.english") @app.route("/") def home(): return render_template("index.html") @app.route("/get") def get_bot_response(): userText = request.args.get('msg') return str(bot.get_response(userText)) if __name__ == "__main__":
_LOGGER = logging.getLogger("aicoe.sesheta.chat") _THOTH_INHABITANTS = [ "bissenbay", "fridex", "goern", "harshad16", "KPostOffice", "pacospace", "saisankargochhayat", "sub-mod", "xtuchyna", ] CHATBOT = ChatBot("Sesheta", read_only=True) _TRAINER = ChatterBotCorpusTrainer(CHATBOT) _TRAINER.train("chatterbot.corpus.english") _GITHUB_TOKEN = os.environ["GITHUB_ACCESS_TOKEN"] _RELEASE_COMMANDS = [ "create new minor release", "create new major release", "create new patch release" ] async def make_release_issue(request: dict): """Create a release issue on Github for Thoth Repos.""" repo_name, text = request.get("repo_name"), request.get("text") issue_title = " ".join(text.split(" ")[1:-2]) web_url = f"https://api.github.com/repos/thoth-station/{repo_name}/issues" json_payload = { "title": issue_title,
def train_data(): trainer = ChatterBotCorpusTrainer(chat_bot) #trainer=ListTrainer(chat_bot) trainer.train("./Data_1.4.yml") print("training completed, use bot.py to talk to bot.")
from flask import Flask, render_template, request from chatterbot import ChatBot from chatterbot.trainers import ChatterBotCorpusTrainer from tkinter import * from flaskC.CB import CB import yaml app = Flask(__name__) app.secret_key = b'\x0f~\xcfK\x08XML\x8f!\xb5\x05?\x1a9yE\x18"L\xf2\x08\x84o' idioms = "" chat_bot_storage = ChatBot("Chatterbot", storage_adapter="chatterbot.storage.SQLStorageAdapter") trainer = ChatterBotCorpusTrainer(chat_bot_storage) trainer.train("chatterbot.corpus.italian") def startIdiomCombo(): with open("comboIdiom.yaml", 'r') as stream: idioms = yaml.load(stream) return idioms @app.route("/") def home(): idioms = startIdiomCombo() return render_template("index.html", idioms=idioms) @app.route("/get") def get_bot_response(): userText = request.args.get('msg')
def bot(self): chatbot = ChatBot("Tob") trainer = ChatterBotCorpusTrainer(chatbot)
# database_uri = "mysql://*****:*****@localhost:3306/chatbot?charset=utf8", database='tchatter.sqlite3', logic_adapters=[{ 'import_path': 'chatterbot.logic.BestMatch', 'statement_comparison_function': LevenshteinDistance, # "statement_comparison_function": SentimentComparison # "statement_comparison_function": SynsetDistance # "statement_comparison_function": JaccardSimilarity, 'response_selection_method': get_random_response, 'default_response': default_response, 'maximum_similarity_threshold': 0.90 }], preprocessors=['chatterbot.preprocessors.clean_whitespace'], ) trainer = ChatterBotCorpusTrainer(taklip_bot) @app.route("/") def home(): return render_template("index.html") @app.route("/get") def get_bot_response(): userText = request.args.get('msg') return str(taklip_bot.get_response(userText)) @app.route("/chat")
trainer.train([ "who made you?", "Sahithi Madhumitha Rudhira created me!", "What all can you do?", "I can chat with you in different Languages:English,Telugu,Hindi,etc..to know about the topics type all Topics", "all topics", "1.AI 2.Bot Profile 3.Computers 4.Conversation 5.Emotion 6.Food 7.Gossip 8.Greetings 9.Health10.History Type'More'", "More", "11.Humor 12.Literature 13.Money 14.Movies 15.Politics 16.Psychology 17.Science 18.Sports 19.Trivia Type 'Next'", "Next", "Greetings in Telugu,Greetings in Hindi", "Tell me, what can you do? ", "My name is Leo. I am a Chat bot!", ]) trainer = ChatterBotCorpusTrainer(bot) trainer.train("chatterbot.corpus.english") trainer1 = ChatterBotCorpusTrainer(bot) trainer1.train("chatterbot.corpus.telugu") trainer2 = ChatterBotCorpusTrainer(bot) trainer2.train("chatterbot.corpus.hindi") trainer3 = ChatterBotCorpusTrainer(bot) trainer3.train("chatterbot.corpus.bangla") trainer4 = ChatterBotCorpusTrainer(bot) trainer4.train("chatterbot.corpus.chinese") trainer5 = ChatterBotCorpusTrainer(bot) trainer5.train("chatterbot.corpus.custom") trainer6 = ChatterBotCorpusTrainer(bot) trainer6.train("chatterbot.corpus.french") trainer7 = ChatterBotCorpusTrainer(bot) trainer7.train("chatterbot.corpus.german")
class Window: r = sr.Recognizer() conversation = [ "Hi!! how are you doing?", "I am doing Great.", "That is good to hear", "Thank you.", "You're welcome." ] # # football conversation conversation_football = [ "What is your favorite football team?", "Do you play fantasy football?", "Who do you like on the raiders?", ] # Conversation about games conversation_Gaming = [ "What is your favorite game", "What games do you play", "I play games, do you?", "My favorite type of games are RTS, Shooter, RPG, MMO.", "You play PC games?", "Yes I play games.", ] # # conversation about Star Wars conversation_StarWars = [ "Do you believe in the force?", "Who is your favorite Stars Wars character?", "My favorite Star Wars Jedi is Luke Skywalker?", "Do you like the new Star Wars movies?", "I do not like the new Star Wars movies that recently came out.", "The Star Wars animated series between 2003 and 2008 where the best animated series baseed on the Clone Wars. ", "Yes I believe in the force. I learned from master Yoda.", "The Trench Run is my favorite part in Star Wars New Hope.", ] # # conversation about personal stuff conversation_Personal_Info = [ "What is your number?", "How old are you?", "My age is none of your business!!!", "Do you have family?", "I do not have family :(.", "I wish I had family.", "Do you like living?", "I am a computer their for I am not living?", "Are you married are in a relationship?", "I am not in a relationship or married.", "Do you go to school or work?", "I go to school and work with you?", "How are you feeling?" ] # # Which Chatbot to train trainer = ListTrainer(chatbot) trainers = ChatterBotCorpusTrainer(chatbot) # here is where train the data based o the conversation depending upon what you and Chatquisha was talking about trainer.train(conversation) trainer.train(conversation_football) trainer.train(conversation_Gaming) trainer.train(conversation_StarWars) trainer.train(conversation_Personal_Info) # # # All the corpus yml file on conversation trainers.train("chatterbot.corpus.english") # # Corpus data based on movies stuff trainers.train("chatterbot.corpus.english.movies") # # Corpus data based on science questions and facts trainers.train("chatterbot.corpus.english.science") # # Corpus data based on computer question trainers.train("chatterbot.corpus.english.computers") # # Corpus data based on psychology!!!! VERY INTERESTING THINGS IN HERE trainers.train("chatterbot.corpus.english.psychology") # # Corpus data based on jokes so the AI tells jokes lol!! trainers.train("chatterbot.corpus.english.humor") # Corpus data for greeting when user says a intro. trainer.train("chatterbot.corpus.english.greetings") def __init__(self): # Used for changing the language and accent. self.language = 'fr' # Build window self.window = tk.Tk() # Main Title self.window.title("Sample Window Title") # Window Size self.window.geometry("1080x1920") # Window Tabs # Set style of tabs style = ttk.Style(self.window) # Set location of tabs # wn = West North # ws = West South style.configure('lefttab.TNotebook', tabpostition='wn') # tab control self.tab_control = ttk.Notebook(self.window) # Create tabs self.tab1 = ttk.Frame(self.tab_control) self.tab2 = ttk.Frame(self.tab_control) # Add tabs to window self.tab_control.add(self.tab1, text='Chatquisha') self.tab_control.add(self.tab2, text='Tic Tac Toe') # Create Labels # Place Labels # Chatquishas tab label2 = Label(self.tab1, text='Welcome to Chatquisha!!', padx=55, pady=20, font='Times 32') label2.grid(column=0, row=0) # Tab for Facial Recognition label5 = Label(self.tab2, text='Tic Tac Toe', padx=55, pady=55, font='Times 32') label5.grid(column=0, row=0) self.tab_control.pack(expand=1, fill='both') self.label3 = Label(self.tab2, text='') # Display Screen for Result # --------------------------------------------------------TAB#2 CHATBOT----------------------------------------- self.l2 = Label(self.tab1, text='Enter Text to talk to Chatquisha...', padx=20, pady=20) self.l2.grid(row=1, column=0) self.input_box2 = ScrolledText(self.tab1, height=12) self.input_box2.grid(row=2, column=0, columnspan=2, padx=5, pady=5) # talk button self.button_process_stuff2 = Button(self.tab1, text="Talk", command=self.run_ai, width=12, bg='#25d366', fg='purple') self.button_process_stuff2.grid(row=4, column=0, padx=10, pady=10) # end Convo Button self.button_process_stuff3 = Button(self.tab1, text="Clear", command=self.end_convo, width=12, bg='#337FFF', fg='#000000') self.button_process_stuff3.grid(row=5, column=0, padx=10, pady=10) # size of the bottom display self.tab2_display2 = ScrolledText(self.tab1, height=18) self.tab2_display2.grid(row=7, column=0, padx=10, pady=10) self.button_process_stuff4 = Button(self.tab1, text="Face Detect", command=self.run_facial_rec, width=12, bg="#ff9900", fg="#000000") self.button_process_stuff4.grid(row=3, column=0, padx=10, pady=10) self.convo_started = False # ---------------------------------------TAB 3 WIKIPEDIA ----------------------------------------------- # For running the loop for everything def run(self): self.window.mainloop() # For ending the convo def end_convo(self): self.tab2_display2.insert(tk.END, "Have a nice day!") self.convo_started = False # Chatquishas data made into a function # # Sample rate is how often values are recorded sample_rate = 48000 # # Chunk is like a buffer. It stores 2048 samples (bytes of data) # # # here. # # # it is advisable to use powers of 2 such as 1024 or 2048 chunk_size = 2048 # # # the following loop aims to set the device ID of the mic that # # # we specifically want to use to avoid ambiguity. # # # init device ID device_id = 1 def run_ai(self): self.r = sr.Recognizer() with sr.Microphone(device_index=self.device_id, sample_rate=self.sample_rate, chunk_size=self.chunk_size) as source: # wait for a second to let the recognizer adjust the # energy threshold based on the surrounding noise level self.r.adjust_for_ambient_noise(source) print("\n\nPlease say something to me now: ") # Listening for the users input/sentence self.audio = self.r.listen(source) # try Block try: text = self.r.recognize_google(self.audio) # When the user speaks it goes into the input box. self.input_box2.insert(tk.END, text) user_sentiment = TextBlob(text) # Here is where it prints out the subjectivity and polarity of the users statement self.input_box2.insert(tk.END, "\n"+str(user_sentiment.sentiment)) if not self.convo_started: self.tab2_display2.delete("1.0", tk.END) hertext = " " self.tab2_display2.insert(tk.END, "Chatquisha says: " + hertext) # clear button self.convo_started = True if self.convo_started: self.wiki_talk() self.text = self.input_box2.get("1.0", tk.END) # when you type in your response and display self.tab2_display2.insert(tk.END, "\nYou: " + self.text) # chat bot will responsed when talked to with a voice self.mytext = str(chatbot.get_response(text)) # Chatquishas response when talking self.tab2_display2.insert(tk.END, "\nChatquisha says: " + self.mytext) # Passing the text and language to the engine, # here we have marked slow=False. Which tells # the module that the converted audio should # have a high speed self.myobj = gTTS(text=self.mytext, lang=self.language, slow=False) # Saving the converted audio in a mp3 file named # welcome self.myobj.save("welcome.mp3") # Playing the converted file os.system("welcome.mp3") # EXCEPTIONS NOT MY FAVORITES BUT.THEY ARE USED FOR CATCHING ERRORS AND THINGS THAT MIGHT HURT YOUR PROGRAM except sr.UnknownValueError: # If it did bot hear anything. print("Google Speech Recognition could not understand audio") # except sr.RequestError as e: print("Could not request results from Google Speech Recognition service; {0}".format(e)) # Used for clearing the chat. def clear_input_box(self): self.input_box.delete(1.0, tk.END) # used for running the code on the tabs such as the input boxes. def run_code_on_tab_1(self): self.input_text = self.input_box.get('1.0', tk.END) self.output_text = self.input_text self.processed_text = self.input_text self.tab1_display.insert(tk.END, self.processed_text) # clearing display def clear_display_result(self): self.tab1_display.delete(1.0, tk.END) # Used for running the facial rec stuff def run_facial_rec(self): Facial_Recog() # This is going to run the Wikipedia summarizer def wiki_talk(self): text = self.input_box2.get("1.0", tk.END).splitlines()[0] words = text.split(" ") if words[0] == 'search': search_words = '' for i in range(1, len(words)): search_words = search_words + " " + words[i] print(search_words) print(wikipedia.summary(search_words)) self.myobj = gTTS(text=wikipedia.summary(search_words), lang=self.language, slow=False) # Saving the converted audio in a mp3 file named # welcome self.myobj.save("search.mp3") # Playing the converted file os.system("search.mp3")
# -*- coding: utf-8 -*- """ Created on Thu May 7 19:06:42 2020 @author: SheilaCarolina """ from chatterbot import ChatBot from chatterbot.trainers import ChatterBotCorpusTrainer dhory = ChatBot("Dhory") trainer = ChatterBotCorpusTrainer(dhory) trainer.train("chatterbot.corpus.portuguese") resposta = dhory.get_response("Perfeito") print("Dhory: ", resposta)
from chatterbot import ChatBot from chatterbot.trainers import ChatterBotCorpusTrainer if __name__ == '__main__': chatbot = ChatBot(name='house') ChatterBotCorpusTrainer(chatbot).train('chatterbot.corpus.english') done = False while not done: user_input = input('?: ') if user_input not in {'quit', 'bye', 'cya'}: print(chatbot.get_response(user_input)) else: done = True
'chatterbot.logic.MathematicalEvaluation', 'chatterbot.logic.TimeLogicAdapter', 'chatterbot.logic.BestMatch', { 'import_path': 'chatterbot.logic.BestMatch', 'default_response': 'I am sorry, but I do not understand. I am still learning.', 'maximum_similarity_threshold': 0.90 } ], database_uri='sqlite:///database.sqlite3' ) from chatterbot.trainers import ListTrainer trainer = ListTrainer(yemenchatbot) training_data_quesans = open('training_data/YemenBot_QA.txt').read().splitlines() training_data_personal = open('training_data/YemenBot_PQ.txt').read().splitlines() training_data = training_data_quesans + training_data_personal trainer.train(training_data) from chatterbot.trainers import ChatterBotCorpusTrainer trainer_corpus = ChatterBotCorpusTrainer(yemenchatbot) trainer_corpus.train( 'chatterbot.corpus.english' )
bot_ = ChatBot( 'Anokha', storage_adapter='chatterbot.storage.MongoDatabaseAdapter', logic_adapters=[ "chatterbot.logic.BestMatch", "chatterbot.logic.MathematicalEvaluation", ], filters=[ # 'chatterbot.filters.RepetitiveResponseFilter' ], database_uri='mongodb://mongo:27017/chatterbot-database') # bot_.set_trainer(ChatterBotCorpusTrainer) # bot_.train("chatterbot.corpus.english") trainer = ChatterBotCorpusTrainer(bot_) trainer.train('chatterbot.corpus.english') # 'chatterbot.corpus.english' # In[2]: import telepot import time # In[3]: bot = telepot.Bot('258599010:AAEi9pqVhiP3h-wVw1tzCiq_elG5RuBefVc') prevText = {} prevReply = {}
def action(): conversation=[ "hello", "Hi there!", "How are you doing", "I am doing great", "That is good to hear", "Thank you" ] chatbot=ChatBot("Bisp") trainer = ChatterBotCorpusTrainer(chatbot) trainer.train( "chatterbot.corpus.english.greetings", "chatterbot.corpus.english.conversations" ) apiemp='http://127.0.0.1:5000' jsonemp=requests.get(apiemp).json() api_address='https://api.openweathermap.org/data/2.5/weather?appid=6e47f682a6c6a11adaf2df87a1812a96&q=Jamshedpur' json_data=requests.get(api_address).json() f=json_data['main']['temp'] t=(f-272.15) f1=json_data['weather'][0]['main'] f2=json_data['weather'][0]['description'] x=[] column=["Id","First Name","Middle Name","Last Name","Address","Email","Mobile Number","Vehicle","Vehicle Number"] colum=["id","fn","mn","ln","address","Email","Mobile","Vehicle","Vehicle_Number"] window = Tk() window.title("Amps") window.configure(background='black') screen_width = window.winfo_screenwidth() screen_height = window.winfo_screenheight() window.geometry("500x500+{0}+{1}".format(screen_width-500, screen_height-570)) messages = Text(window,background='lightgreen') messages.pack() messages.tag_config('amps',foreground="blue") messages.tag_config('you',foreground="red") input_user = StringVar() input_field = Entry(window, text=input_user,font=(None,20),background='lightblue') input_field.insert(0,'Type something here') input_field.pack(side=BOTTOM, fill=X) label=Label(window,text="Amps : Type information if you want to rummage through the database",height="20",background='lightyellow') label.pack() def Enter_pressed(event): userInput = input_field.get() messages.insert(INSERT, 'You:%s\n' % userInput,'you') input_user.set('') messages.see(END) if userInput.strip()=='bye' or userInput.strip()=='goodbye': messages.insert(INSERT, 'Amps:Bye\n','amps') messages.see(END) exit elif('weather' in userInput.strip()): messages.insert(INSERT, "The weather is "+f1+" and described as "+f2+"\n",'amps') messages.see(END) elif('temperature' in userInput.strip()): messages.insert(INSERT, "The temperature is "+str(t)+"\n",'amps') messages.see(END) elif('information' in userInput.strip()): messages.insert(INSERT, "Give me something to work with:\n",'amps') messages.insert(INSERT, "1 for Full Name\n2 for ID\n3 for Email\n4 for Mobile Number\n5 for Vehicle\n6 for Vehicle Number\n",'amps') messages.see(END) ch=int(easygui.enterbox("Your Choice?")) c=[] if(ch==1): messages.insert(INSERT, "Enter the full name:\n",'amps') messages.see(END) names=easygui.enterbox("Enter full name").split() for i in range(len(jsonemp["myCollection"])): if(jsonemp["myCollection"][i]["fn"]==names[0] and jsonemp["myCollection"][i]["mn"]==names[1] and jsonemp["myCollection"][i]["ln"]==names[2]): z={} for j in range(len(column)): z[colum[j].lower()]=jsonemp["myCollection"][i][colum[j].lower()] c.append(z) elif(ch==2): messages.insert(INSERT,"Enter id:\n",'amps') messages.see(END) name=int(easygui.enterbox("Enter ID")) for i in range(len(jsonemp["myCollection"])): if(jsonemp["myCollection"][i]["id"]==name): z={} for j in range(len(column)): z[colum[j].lower()]=jsonemp["myCollection"][i][colum[j].lower()] c.append(z) elif(ch==3): messages.insert(INSERT,"Enter Email:\n",'amps') messages.see(END) name=easygui.enterbox("Enter Email") for i in range(len(jsonemp["myCollection"])): if(jsonemp["myCollection"][i]["email"]==name): z={} for j in range(len(column)): z[colum[j].lower()]=jsonemp["myCollection"][i][colum[j].lower()] c.append(z) elif(ch==4): messages.insert(INSERT,"Enter Mobile Number:\n",'amps') messages.see(END) name=easygui.enterbox("Enter Mobile Number") for i in range(len(jsonemp["myCollection"])): if(jsonemp["myCollection"][i]["mobile"]==name): z={} for j in range(len(column)): z[colum[j].lower()]=jsonemp["myCollection"][i][colum[j].lower()] c.append(z) elif(ch==5): messages.insert(INSERT,"Enter Vehicle:\n",'amps') messages.see(END) name=easygui.enterbox("Enter Vehicle Name") for i in range(len(jsonemp["myCollection"])): if(jsonemp["myCollection"][i]["vehicle"]==name): z={} for j in range(len(column)): z[colum[j].lower()]=jsonemp["myCollection"][i][colum[j].lower()] c.append(z) elif(ch==6): messages.insert(INSERT,"Enter Vehicle Number:\n",'amps') messages.see(END) name=easygui.enterbox("Enter Vehicle Number") for i in range(len(jsonemp["myCollection"])): if(jsonemp["myCollection"][i]["vehicle_number"]==name): z={} for j in range(len(column)): z[colum[j].lower()]=jsonemp["myCollection"][i][colum[j].lower()] c.append(z) messages.insert(INSERT,"These are the results I found.\n",'amps') messages.see(END) for i in range(len(c)): messages.insert(INSERT,"%s" % c[i]["id"]+" "+c[i]["fn"]+" "+c[i]["ln"]+"\n") messages.see(END) messages.insert(INSERT,"Enter the id of the person : \n",'amps') messages.see(END) id=int(easygui.enterbox("Enter ID of the person you want details on")) for i in range(len(c)): if(c[i]["id"]==id): for j in range(len(column)): messages.insert(INSERT,"%s" % column[j]+" : "+str(c[i][colum[j].lower()])+"\n") messages.see(END) messages.see(END) else: response=chatbot.get_response(userInput) messages.configure(foreground='red') messages.insert(INSERT,'Amps: %s\n' % response,'amps') messages.see(END) return "break" frame = Frame(window,background="black") # , width=300, height=300) input_field.bind("<Return>", Enter_pressed) frame.pack() window.mainloop()
def trainEnglishGreetings(chatbot): trainer = ChatterBotCorpusTrainer(chatbot) trainer.train("chatterbot.corpus.english.greetings")
def setUp(self): super(ChatterBotCorpusTrainingTestCase, self).setUp() self.chatbot = ChatBot(**settings.CHATTERBOT) self.trainer = ChatterBotCorpusTrainer(self.chatbot, show_training_progress=False)
from chatterbot import ChatBot from chatterbot.trainers import ListTrainer from chatterbot.trainers import ChatterBotCorpusTrainer # Creating ChatBot Instance chatbot = ChatBot('CoronaBot') # Training with Personal Ques & Ans conversation = [ "Hello", "Hi there!", "How are you doing?", "I'm doing great.", "That is good to hear", "Thank you.", "You're welcome." ] trainer = ListTrainer(chatbot) trainer.train(conversation) # Training with English Corpus Data trainer_corpus = ChatterBotCorpusTrainer(chatbot) trainer_corpus.train('chatterbot.corpus.english')
from chatterbot import ChatBot from chatterbot.trainers import ListTrainer from chatterbot.trainers import ChatterBotCorpusTrainer # Creating ChatBot Instance chatbot = ChatBot('BankBot') training_data_quesans = open( 'training_data/personal_ques.txt').read().splitlines() training_data = training_data_quesans trainer = ListTrainer(chatbot.storage) trainer.train(training_data) trainer = ChatterBotCorpusTrainer(chatbot.storage) trainer.train("chatterbot.corpus.english") chatbot.set_trainer(ChatterBotCorpusTrainer) chatbot.train("chatterbot.corpus.english")
from chatterbot import ChatBot from chatterbot.trainers import ChatterBotCorpusTrainer #initializing chatbot chatbot = ChatBot('Chatterbot', trainer='chatterbot.trainers.CorpusTrainer', storage_adapter='chatterbot.storage.SQLStorageAdapter', database_uri='sqlite:///django_chatterbot_statement.sqlite3') import logging #initializing trainer trainer = ChatterBotCorpusTrainer(chatbot) #training the chatbot with yml files present in the location specified trainer.train('./training_data/') #logging to the console when the data is entered logging.basicConfig(filename="./log.txt", format='%(asctime)s - %(message)s', datefmt="%d-%b-%y %H:%M:%S", level=logging.INFO) logging.info('Training data added to database')
def train(self): self.trainer = ChatterBotCorpusTrainer(self.bot) self.trainer.train(self.train_data_path)