Chatbot/predict_bot.py at main · StealthyFearZ/Chatbot · GitHub

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#Text Data Preprocessing Lib
import nltk
# add the below code
nltk.download('punkt')
import json
import pickle
import numpy as np
import random

ignore_words = ['?', '!',',','.', "'s", "'m"]

# Model Load Lib
import tensorflow
from data_preprocessing import get_stem_words

model = tensorflow.keras.models.load_model('chatbot_model.h5')

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


def preprocess_user_input(user_input):

    input_word_token_1 = nltk.word_tokenize(user_input)
    input_word_token_2 = get_stem_words(input_word_token_1, ignore_words)
    input_word_token_2 = sorted(list(set(input_word_token_2)))

    bag=[]
    bag_of_words = []

    # Input data encoding
    for word in words:
        if word in input_word_token_2:
            bag_of_words.append(1)
        else:
            bag_of_words.append(0)
    bag.append(bag_of_words)

    return np.array(bag)

def bot_class_prediction(user_input):

    inp = preprocess_user_input(user_input)
    prediction = model.predict(inp)
    predicted_class_label = np.argmax(prediction[0])
    return predicted_class_label


def bot_response(user_input):

   predicted_class_label =  bot_class_prediction(user_input)
   predicted_class = classes[predicted_class_label]

   for intent in intents['intents']:
    if intent['tag']==predicted_class:
        bot_response = random.choice(intent['responses'])
        return bot_response

print("Hi I am Stella, How Can I help you?")

while True:
    user_input = input("Type your message here:")
    print("User Input: ", user_input)

    response = bot_response(user_input)
    print("Bot Response: ", response)