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)
