import transformers
import gradio as gr
import torch

from transformers import GPT2LMHeadModel, GPT2Tokenizer

model_name = 'microsoft/DialoGPT-large'
tokenizer = GPT2Tokenizer.from_pretrained(model_name)
model = GPT2LMHeadModel.from_pretrained(model_name)

def predict(input, history=[]):
    # tokenize the new input sentence
    new_user_input_ids = tokenizer.encode(input + tokenizer.eos_token, return_tensors='pt')

    # append the new user input tokens to the chat history
    bot_input_ids = torch.cat([torch.LongTensor(history), new_user_input_ids], dim=-1)

    # generate a response 
    history = model.generate(
                            bot_input_ids,
                            max_length=1000,
                            pad_token_id=tokenizer.eos_token_id,
                            no_repeat_ngram_size=3,
                            top_p = 0.92,
                            top_k = 50
                            ).tolist()
                            
    # convert the tokens to text, and then split the responses into lines
    response = tokenizer.decode(history[0]).split("<|endoftext|>")
    response = [(response[i], response[i+1]) for i in range(0, len(response)-1, 2)]  # convert to tuples of list
    
    return response, history
    
gr.Interface(fn=predict,
    title="DialoGPT-large",
    inputs=["text", "state"],
    outputs=["chatbot", "state"],
    ).launch()