Rename chatbot.py to app

chatbot.py → app

@@ -1,141 +1,142 @@
-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())
-        
+import argparse
+import streamlit as st
+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 = st.text_area("Sen: ")
+        output = predict(hparams, chatbot, tokenizer, sentence)
+        st.json(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())
+