test.py

import numpy as np
from keras.saving import load_model
from keras.preprocessing.text import Tokenizer
from keras_self_attention import SeqSelfAttention
from model_settings import *


with open("responses.txt", "r") as f:
    lines = [x.rstrip("\n") for x in f.readlines()]

tokenizer = Tokenizer() # a tokenizer is a thing to split text into words, it might have some other stuff like making all the letters lowercase, etc.
tokenizer.fit_on_texts(lines)

model = load_model("chatbot.keras", custom_objects={"SeqSelfAttention": SeqSelfAttention})

def find_line_number(array):
    return sorted(zip(list(array), [x for x in range(len(array))]), key=lambda x:x[0], reverse=True)[0][1] # yeah, one big line, find the biggest value and return the number of the line

def generate(text, verbose=1):
    tokens = list(tokenizer.texts_to_sequences([text,])[0]) # text into tokens (almost words)
    tokens = (tokens+[0,]*inp_len)[:inp_len] # cutting off the sentence after inp_len words
    prediction = model.predict(np.array([tokens,]), verbose=verbose)[0]
    line = find_line_number(prediction)
    return lines[line]

if __name__ == "__main__": # if this code is not being imported, open the chat
    while True:
        inp = input("User: ")
        print(f"Bot: {generate(inp)}")