app.py

import gradio as gr

from transformers import AutoConfig
from transformers import GPT2Tokenizer, GPT2LMHeadModel

config = AutoConfig.from_pretrained('gorkemgoknar/gpt2chatbotenglish')
model = GPT2LMHeadModel.from_pretrained('gorkemgoknar/gpt2chatbotenglish', config=config)

tokenizer = GPT2Tokenizer.from_pretrained('gorkemgoknar/gpt2chatbotenglish')
tokenizer.model_max_length = 1024


def get_chat_response(name, input_txt = "Hello , what is your name?"):
  personality = "My name is " + "Gandalf"

  bot_input_ids = tokenizer.encode(personality + tokenizer.eos_token + input_txt + tokenizer.eos_token  , return_tensors='pt')

  #optimum response and speed
  chat_history_ids = model.generate(
          bot_input_ids, max_length=50,
          pad_token_id=tokenizer.eos_token_id,  
          no_repeat_ngram_size=3,       
          do_sample=True, 
          top_k=60, 
          top_p=0.8,
          temperature = 1.3
      )

  out_str = tokenizer.decode(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True)
  return out_str


personality_choices = ["Gandalf", "Riddick", "Macleod", "Morpheus", "Neo","Spock","Vader","Indy"]

examples= ["Gandalf", "What is your name?"]


interface = gr.Interface(fn=get_chat_response, inputs=[gr.inputs.Dropdown(personality_choices) ,"text"], outputs="text")


if __name__ == "__main__":
    interface.launch()