chatbot_logic.py

# Import necessary libraries
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

# Load pre-trained model and tokenizer
model_name = 'microsoft/DialoGPT-medium'
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

def generate_response(user_input, chat_history_ids=None):
    # Encode the new user input, add the eos_token and return a tensor in Pytorch
    new_user_input_ids = tokenizer.encode(user_input + tokenizer.eos_token, return_tensors='pt')

    # Append the new user input tokens to the chat history, 
    # pass the tokens to the model, and get the response
    bot_input_ids = torch.cat([chat_history_ids, new_user_input_ids], dim=-1) if chat_history_ids is not None else new_user_input_ids
    chat_history_ids = model.generate(bot_input_ids, max_length=1000, pad_token_id=tokenizer.eos_token_id)

    # Decode the generated response from the model
    response = tokenizer.decode(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True)
    return response, chat_history_ids

# Example usage:
# response, chat_history_ids = generate_response("Hello, how are you?")
# print(response)