Chatbot using Huggingface Transformers

Question

I would like to use Huggingface Transformers to implement a chatbot. Currently, I have the code shown below. The transformer model already takes into account the history of past user input.

Is there something else (additional code) I have to take into account for building the chatbot?

Second, how can I modify my code to run with TensorFlow instead of PyTorch?

Later on, I also plan to fine-tune the model on other data. I also plan to test different models such as BlenderBot and GPT2. I think to test this different models it should be as easy as replacing the corresponding model in AutoTokenizer.from_pretrained("microsoft/DialoGPT-small") and AutoModelForCausalLM.from_pretrained("microsoft/DialoGPT-small")

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

tokenizer = AutoTokenizer.from_pretrained("microsoft/DialoGPT-small")
model = AutoModelForCausalLM.from_pretrained("microsoft/DialoGPT-small")

for step in range(5):
    # encode the new user input, add the eos_token and return a tensor in Pytorch
    new_user_input_ids = tokenizer.encode(input(">> User:") + tokenizer.eos_token, return_tensors='pt')

    # append the new user input tokens to the chat history
    bot_input_ids = torch.cat([chat_history_ids, new_user_input_ids], dim=-1) if step > 0 else new_user_input_ids

    # generated a response while limiting the total chat history to 1000 tokens, 
    chat_history_ids = model.generate(bot_input_ids, max_length=1000, pad_token_id=tokenizer.eos_token_id)

    # pretty print last ouput tokens from bot
    print("DialoGPT: {}".format(tokenizer.decode(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True)))