chatgui.py

import nltk
from nltk.stem import WordNetLemmatizer
import pickle
import numpy as np
from keras.models import load_model
import json
import random


lemmatizer = WordNetLemmatizer()

model = load_model('chatbot_model.h5')


intents = json.loads(open('intents.json').read())
words = pickle.load(open('words.pkl', 'rb'))
classes = pickle.load(open('classes.pkl', 'rb'))

inputlist = []


def clean_up_sentence(sentence):
    sentence_words = nltk.word_tokenize(sentence)
    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 = 0.95  #
    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)
    if len(results) == 0:
        return None
        # gptbot(inputlist)
    # print(inputlist)
    return_list = []
    for r in results:
        return_list.append({"intents": classes[r[0]], "probability": str(r[1])})
    return return_list


def getResponse(ints, intents_json):
    result = None
    tag = ints[0]['intents']
    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):
    inputlist.append(msg)
    ints = predict_class(msg, model)
    if (ints == None):
        return None
    res = getResponse(ints, intents)
    inputlist.append(" " + res)
    return res


def faz_tudo(prompt):
    msg = prompt
    res = chatbot_response(msg)
    return res