app.py

import gradio as gr
import re
from transformers import TFGPT2LMHeadModel, GPT2Tokenizer
description = "Chatbots for customer service with GPT-2"
title = "Please enter the problem you are experiencing"
examples = [["I didn't get my order delivered for a long time."]]

from transformers import TFGPT2LMHeadModel, GPT2Tokenizer

model_name_or_path = "Jinpkk/ITproject_version4"  

tokenizer = GPT2Tokenizer.from_pretrained(model_name_or_path)
model = TFGPT2LMHeadModel.from_pretrained(model_name_or_path)

def clean_output(text):
    # Remove content within square brackets
    cleaned_text = re.sub(r'\[.*?\]', '', text)
    # Remove any [SEP] tokens
    cleaned_text = cleaned_text.replace('[SEP]', '').strip()
    return cleaned_text


def generate_response(input_text):
    input_ids = tokenizer.encode(input_text, return_tensors='tf')
    beam_output = model.generate(
        input_ids,
        max_length=128,
        num_beams=5,
        no_repeat_ngram_size=2,
        early_stopping=True,
        pad_token_id=tokenizer.eos_token_id
    )
    generated_text = tokenizer.decode(beam_output[0], skip_special_tokens=True)
    generated_text = generated_text.split('[PAD]')[0].strip()
    generated_text = generated_text.split('^')[0].strip()
    generated_text = generated_text.replace(input_text.strip(), '').strip()
    generated_text = generated_text.replace("<SEP>", "").strip()
    generated_text = generated_text.split('(')[0].strip()

    generated_text = clean_output(generated_text)
    return generated_text

    
def chatbot_response(input_text):
    # Extract previous messages and the new message
    previous_messages, new_message = input_text.split("\n")[:-1], input_text.split("\n")[-1]
    
    # Get the model's response
    response = generate_response(new_message)
    
    # Add the new message and the model's response to the previous messages
    updated_chat = "\n".join(previous_messages + [f"You: {new_message}", f"Bot: {response}"])
    
    return updated_chat

interface = gr.Interface(
            fn=generate_response,
            inputs=gr.inputs.Textbox(lines=10, placeholder="Enter your text..."),
            outputs=gr.outputs.Textbox(),
            description=description,
            title=title,
            examples=examples
)


interface_chat = gr.Interface(
    fn=chatbot_response,
    inputs=gr.components.Textbox(lines=15, placeholder="Type your message here..."),
    outputs=gr.components.Textbox(),
    live=False,
    description=description,
    examples=examples,
    title=title
)
interface_chat.launch()