Complete ChatBot Using Machine Learning 2024

                                       INTRODUCTION

A chatbot is a computer program that simulates human conversation with an end user. Not all chatbots are equipped with artificial intelligence (AI), but modern chatbots increasingly use conversational AI techniques such as natural language processing (NLP) to understand user questions and automate responses to them.

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COMPLETE CODE 😃👇

import json 

import numpy as np 

import tensorflow as tf

from tensorflow import keras

from tensorflow.keras.models import Sequential

from tensorflow.keras.layers import Dense, Embedding, GlobalAveragePooling1D

from tensorflow.keras.preprocessing.text import Tokenizer

from tensorflow.keras.preprocessing.sequence import pad_sequences

from sklearn.preprocessing import LabelEncoder

with open('intents.json') as file:

    data = json.load(file)

    

training_sentences = []

training_labels = []

labels = []

responses = []

for intent in data['intents']:

    for pattern in intent['patterns']:

        training_sentences.append(pattern)

        training_labels.append(intent['tag'])

    responses.append(intent['responses'])

    

    if intent['tag'] not in labels:

        labels.append(intent['tag'])

        

num_classes = len(labels)

lbl_encoder = LabelEncoder()

lbl_encoder.fit(training_labels)

training_labels = lbl_encoder.transform(training_labels)

vocab_size = 1000

embedding_dim = 16

max_len = 20

oov_token = "<OOV>"

tokenizer = Tokenizer(num_words=vocab_size, oov_token=oov_token)

tokenizer.fit_on_texts(training_sentences)

word_index = tokenizer.word_index

sequences = tokenizer.texts_to_sequences(training_sentences)

padded_sequences = pad_sequences(sequences, truncating='post', maxlen=max_len)

model = Sequential()

model.add(Embedding(vocab_size, embedding_dim, input_length=max_len))

model.add(GlobalAveragePooling1D())

model.add(Dense(16, activation='relu'))

model.add(Dense(16, activation='relu'))

model.add(Dense(num_classes, activation='softmax'))

model.compile(loss='sparse_categorical_crossentropy', 

              optimizer='adam', metrics=['accuracy'])

model.summary()

epochs = 500

history = model.fit(padded_sequences, np.array(training_labels), epochs=epochs)

# to save the trained model

model.save("chat_model")

import pickle

# to save the fitted tokenizer

with open('tokenizer.pickle', 'wb') as handle:

    pickle.dump(tokenizer, handle, protocol=pickle.HIGHEST_PROTOCOL)

    

# to save the fitted label encoder

with open('label_encoder.pickle', 'wb') as ecn_file:

    pickle.dump(lbl_encoder, ecn_file, protocol=pickle.HIGHEST_PROTOCOL)

import json 

import numpy as np

from tensorflow import keras

from sklearn.preprocessing import LabelEncoder

import colorama 

colorama.init()

from colorama import Fore, Style, Back

import random

import pickle

with open("intents.json") as file:

    data = json.load(file)

def chat():

    # load trained model

    model = keras.models.load_model('chat_model')

    # load tokenizer object

    with open('tokenizer.pickle', 'rb') as handle:

        tokenizer = pickle.load(handle)

    # load label encoder object

    with open('label_encoder.pickle', 'rb') as enc:

        lbl_encoder = pickle.load(enc)

    # parameters

    max_len = 20

    

    while True:

        print(Fore.LIGHTBLUE_EX + "User: " + Style.RESET_ALL, end="")

        inp = input()

        if inp.lower() == "quit":

            break

        result = model.predict(keras.preprocessing.sequence.pad_sequences(tokenizer.texts_to_sequences([inp]),

                                             truncating='post', maxlen=max_len))

        tag = lbl_encoder.inverse_transform([np.argmax(result)])

        for i in data['intents']:

            if i['tag'] == tag:

                print(Fore.GREEN + "ChatBot:" + Style.RESET_ALL , np.random.choice(i['responses']))

        # print(Fore.GREEN + "ChatBot:" + Style.RESET_ALL,random.choice(responses))

print(Fore.YELLOW + "Start messaging with the bot (type quit to stop)!" + Style.RESET_ALL)

chat()

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