from api.ai import Agent
import json
#initialize the agent
agent = Agent(
'<subscription-key>',
'<client-access-token>',
'<developer-access-token>',
)
# actions defined in the API.AI console that fire locally when an intent is
# recognized
def saveType(flowerType):
print 'do something here'
def saveColor(color):
print 'do something here'
def createOrder(address):
print 'do something here'
def main():
user_input = ''
#loop the queries to API.AI so we can have a conversation client-side
while user_input != 'exit':
#parse the user input
user_input = raw_input("me: ")
#query the console with the user input, retrieve the response
from api.ai import Agent
import json
#initialize the agent
agent = Agent(
'<subscription-key>',
'<7a8781b3255c4780bcf7e383a752fe87>',
'<8f9d048c2c504a3db06f813bd6b2bc24>',
)
# actions defined in the API.AI console that fire locally when an intent is
# recognized
def saveType(flowerType):
print 'do something here'
def saveColor(color):
print 'do something here'
def createOrder(address):
print 'do something here'
def main():
user_input = ''
#loop the queries to API.AI so we can have a conversation client-side
while user_input != 'exit':
#parse the user input
user_input = raw_input("me: ")
#query the console with the user input, retrieve the response
response = agent.query(user_input)
import cv2
import subprocess
import pyaudio
import wikipedia
import speech_recognition as sr
import webbrowser as wb
wake_words = ["wake", "john"]
greeting_phrases = [
'howdy sir', 'whassup sir', "what's up sir", 'hi sir', 'hello sir',
'hey sir'
]
agent = Agent(
'<subscription-key>',
'd7b00ed0ee08464c860a9f6e8eb7164e',
'6fef320b63224ce6a42b443ec8428db1',
)
def chat(inp):
response = agent.query(inp)
result = response['result']
fulfillment = result['fulfillment']
response = fulfillment['speech']
speak(response)
def exec_command(command):
os.system(command)
import kivy
from kivy.app import App
from kivy.uix.gridlayout import GridLayout
from api.ai import Agent
import json
#initialize the agent
agent = Agent(
'<subscription-key>',
'9eb285851f6d47a5a1d970e77b16f3de',
'f1d3838773f04e4bb94dc3706a94647e',
)
class CalcGridLayout(GridLayout):
def __init__(self):
super(CalcGridLayout, self).__init__()
def API_call(self, user_input):
response = agent.query(user_input)
result = response['result']
fulfillment = result['fulfillment']
return fulfillment['speech']
class calculatorApp(App):
def build(self):
return CalcGridLayout()
calcApp = calculatorApp()
from flask import Flask, render_template, url_for, redirect, request
import os.path, sqlite3
import datetime, time
from nltk.stem.wordnet import WordNetLemmatizer
import nltk, string, random
from api.ai import Agent
import json
from summary import summary
#initialize the agent
agent = Agent(
'sakd',
'04f67374d4b14ed68d9f13f70ddfdca8',
'7b62bcd174784e09ab76acc96be378ed',
)
global MedicalFlag, RetrieveRecordsFlag, UpdateRecordsFlag
MedicalFlag, RetrieveRecordsFlag, UpdateRecordsFlag, NoRecords = False, False, False, True
app = Flask(__name__)
def preprocessing(input_text):
lst_stop_words = open("stop_words_and_singlish.txt", "r")
stop_words = []
for line in lst_stop_words:
stop_words.append(''.join(line.strip().split("\n")))
lst_stop_words.close()
translator_punc = str.maketrans('', '', string.punctuation)
words = input_text.translate(translator_punc)
words = words.split()
#etc
from packages.etc.memes import get_meme_msg
from packages.etc.wisdomsearch import wisdom_search
from packages.etc.weather import weather_msg
#internal libraries
from packages.internal.postbacks import intro_reply, health_reply, bot_menu, subscriptions_reply, current_features_msg
MAX_MESSAGE_LENGTH = 640
app = Flask(__name__)
app.config.update(SECRET_KEY=os.environ['SECRET_KEY'])
agent = Agent(
'thecolumbialion',
os.environ['CLIENT_ACCESS_TOKEN'],
os.environ['DEVELOPER_ACCESS_TOKEN'],
)
page = fbmq.Page(os.environ['ACCESS_TOKEN'], api_ver="v2.11")
page.greeting(
"Welcome to LionBot! Click below to learn more about what I can do.")
page.show_starting_button("GET_STARTED")
#THE DICT
""" Dictionary of all module interface functions."""
Msg_Fn_Dict = {
'clubs': clubs_msg,
'printers': printers_msg,
'mta_subway_info': mta_subway_info_msg,
'campus_news_updates': news_msg,
'tv_network': tv_network_msg,
import threading
from telebot import types
from pymongo import MongoClient
import traceback
import apiai, json
from api.ai import Agent
token = ''
bot = telebot.TeleBot(token)
neiro = apiai.ApiAI('')
parent = ''
agent = Agent(
'cipraded',
'',
'',
)
training = False
teachers = [268486177, 792414733, 441399484]
@bot.message_handler(commands=['train'])
def ctrain(m):
pass
@bot.message_handler()
def txt(m):
response = react(m)
from api.ai import Agent
import json
#initialize the agent
agent = Agent(
'<subscription-key>',
'841d0b494b1c4abbb587f2bddacd87fc',
'377275e794464e5cbe74eb66f8ca98b2',
)
# actions defined in the API.AI console that fire locally when an intent is
# recognized
def saveType(flowerType):
print 'do something here'
def saveColor(color):
print 'do something here'
def createOrder(address):
print 'do something here'
def main():
user_input = ''
#loop the queries to API.AI so we can have a conversation client-side
while user_input != 'exit':
#parse the user input
user_input = raw_input("me: ")
#query the console with the user input, retrieve the response