bot.py

import requests as requests
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 - split words into array
    sentence_words = nltk.word_tokenize(sentence)
    # stem each word - create short form for word
    sentence_words = [lemmatizer.lemmatize(word.lower()) for word in sentence_words]
    return sentence_words

# return bag of words array: 0 or 1 for each word in the bag that exists in the sentence

def bow(sentence, words, show_details=True):
    # tokenize the pattern
    sentence_words = clean_up_sentence(sentence)
    # bag of words - matrix of N words, vocabulary matrix
    bag = [0]*len(words)  
    for s in sentence_words:
        for i,w in enumerate(words):
            if w == s: 
                # assign 1 if current word is in the vocabulary position
                bag[i] = 1
                if show_details:
                    print ("found in bag: %s" % w)
    return(np.array(bag))

def predict_class(sentence, model):
    # filter out predictions below a threshold
    p = bow(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]
    # sort by strength of 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

def chatbot_response(msg):
    ints = predict_class(msg, model)
    res = getResponse(ints, intents)
    return res

url = "https://api.telegram.org/bot1538490967:AAFkQpEuFEAHCkpLr7_CGwpjDyt9rKnph38/"

# create function that get chat id
def get_chat_id(update):
    chat_id = update['message']["chat"]["id"]
    return chat_id

# create function that get message text
def get_message_text(update):
    message_text = update['message']["text"]
    return message_text


# create function that get last_update
def last_update(req):
    response = requests.get(req + "getUpdates")
    response = response.json()
    result = response["result"]
    total_updates = len(result) - 1
    return result[total_updates]


# create function that let bot send message to user
def send_message(chat_id, message_text):
    params = {"chat_id": chat_id, "text": message_text}
    response = requests.post(url + "sendMessage", data=params)
    return response

# create main function for navigate or reply message back
def main():
    update_id = last_update(url)["update_id"]
    while True:
        update = last_update(url)
        if update_id == update["update_id"]:
            if get_message_text(update) != "/start":
                send_message(get_chat_id(update), chatbot_response(get_message_text(update)))
            # if get_message_text(update).lower() == "hi" or get_message_text(update).lower() == "hello":
            #     send_message(get_chat_id(update), 'Hello Welcome to our bot.')
            # else:
            #     send_message(get_chat_id(update), "Sorry Not Understant what you inputted:(")
            update_id += 1
        

# call the function to make it reply
main()