KhantKyaw/Chat_GPT-2

from transformers import GPT2Tokenizer, GPT2LMHeadModel

def generate_response(input_text):

    inputs = tokenizer(input_text, return_tensors="pt")
    output_sequences = model.generate(
        input_ids=inputs['input_ids'],
        attention_mask=inputs['attention_mask'],
        max_length=100,  # Adjusted max_length
        temperature=0.3,
        top_k=40,
        top_p=0.85,
        num_return_sequences=1,
        no_repeat_ngram_size=2,
        pad_token_id=tokenizer.eos_token_id,
        early_stopping=True,
        do_sample=True,
        use_cache=True,
    )

    full_generated_text = tokenizer.decode(output_sequences[0], skip_special_tokens=True)

    bot_response_start = full_generated_text.find('[Bot]') + len('[Bot]')
    bot_response = full_generated_text[bot_response_start:]

    last_period_index = bot_response.rfind('.')
    if last_period_index != -1:
        bot_response = bot_response[:last_period_index + 1]

    return bot_response.strip()


model_name = 'KhantKyaw/Chat_GPT-2'
tokenizer = GPT2Tokenizer.from_pretrained(model_name)
model = GPT2LMHeadModel.from_pretrained(model_name)
response = generate_response(user_input)
print("Chatbot:", response)