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import argparse
import tensorflow as tf
import model
from dataset import get_dataset, preprocess_sentence
def inference(hparams, chatbot, tokenizer, sentence):
sentence = preprocess_sentence(sentence)
sentence = tf.expand_dims(
hparams.start_token + tokenizer.encode(sentence) + hparams.end_token, axis=0
)
output = tf.expand_dims(hparams.start_token, 0)
for _ in range(hparams.max_length):
predictions = chatbot(inputs=[sentence, output], training=False)
predictions = predictions[:, -1:, :]
predicted_id = tf.cast(tf.argmax(predictions, axis=-1), tf.int32)
if tf.equal(predicted_id, hparams.end_token[0]):
break
output = tf.concat([output, predicted_id], axis=-1)
return tf.squeeze(output, axis=0)
def predict(hparams, chatbot, tokenizer, sentence):
prediction = inference(hparams, chatbot, tokenizer, sentence)
predicted_sentence = tokenizer.decode(
[i for i in prediction if i < tokenizer.vocab_size]
)
return predicted_sentence
def read_file(file_path):
with open(file_path, 'r', encoding='utf-8') as file:
lines = file.readlines()
return lines
def append_to_file(file_path, line):
with open(file_path, 'a', encoding='utf-8') as file:
file.write(f"{line}\n")
def get_last_ids(lines_file, conversations_file):
lines = read_file(lines_file)
conversations = read_file(conversations_file)
last_line = lines[-1]
last_conversation = conversations[-1]
last_line_id = int(last_line.split(" +++$+++ ")[0][1:])
last_user_id = int(last_conversation.split(" +++$+++ ")[1][1:])
last_movie_id = int(last_conversation.split(" +++$+++ ")[2][1:])
return last_line_id, last_user_id, last_movie_id
def update_data_files(user_input, bot_response, lines_file='data/lines.txt', conversations_file='data/conversations.txt'):
last_line_id, last_user_id, last_movie_id = get_last_ids(lines_file, conversations_file)
new_line_id = f"L{last_line_id + 1}"
new_bot_line_id = f"L{last_line_id + 2}"
new_user_id = f"u{last_user_id + 1}"
new_bot_user_id = f"u{last_user_id + 2}"
new_movie_id = f"m{last_movie_id + 1}"
append_to_file(lines_file, f"{new_line_id} +++$+++ {new_user_id} +++$+++ {new_movie_id} +++$+++ Ben +++$+++ {user_input}")
append_to_file(lines_file, f"{new_bot_line_id} +++$+++ {new_bot_user_id} +++$+++ {new_movie_id} +++$+++ Bot +++$+++ {bot_response}")
new_conversation = f"{new_user_id} +++$+++ {new_bot_user_id} +++$+++ {new_movie_id} +++$+++ ['{new_line_id}', '{new_bot_line_id}']"
append_to_file(conversations_file, new_conversation)
def get_feedback():
feedback = input("Bu cevap yardımcı oldu mu? (Evet/Hayır): ").lower()
return feedback == "Evet"
def chat(hparams, chatbot, tokenizer):
print("\nCHATBOT")
for _ in range(5):
sentence = input("Sen: ")
output = predict(hparams, chatbot, tokenizer, sentence)
print(f"\nBOT: {output}")
user_input = sentence
bot_response = output
feedback = get_feedback()
if feedback:
update_data_files(user_input, bot_response)
else:
pass
def main(hparams):
_, token = get_dataset(hparams)
tf.keras.backend.clear_session()
chatbot = tf.keras.models.load_model(
hparams.save_model,
custom_objects={
"PositionalEncoding": model.PositionalEncoding,
"MultiHeadAttention": model.MultiHeadAttention,
},
compile=False,
)
chat(hparams, chatbot, token)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument(
"--save_model", default="model.h5", type=str, help="path save the model"
)
parser.add_argument(
"--max_samples",
default=25000,
type=int,
help="maximum number of conversation pairs to use",
)
parser.add_argument(
"--max_length", default=40, type=int, help="maximum sentence length"
)
parser.add_argument("--batch_size", default=64, type=int)
parser.add_argument("--num_layers", default=2, type=int)
parser.add_argument("--num_units", default=512, type=int)
parser.add_argument("--d_model", default=256, type=int)
parser.add_argument("--num_heads", default=8, type=int)
parser.add_argument("--dropout", default=0.1, type=float)
parser.add_argument("--activation", default="relu", type=str)
parser.add_argument("--epochs", default=80, type=int)
main(parser.parse_args())
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