import os import gradio as gr title = "Have Fun With MJ_Bot" description = """

The bot is for TBD. rick

""" article = "

Recipes for building an open-domain chatbot

Original PARLAI Code

" import torch from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, BlenderbotForConditionalGeneration, BlenderbotForCausalLM, BlenderbotTokenizer tokenizer = BlenderbotTokenizer.from_pretrained("facebook/blenderbot-400M-distill") model = BlenderbotForConditionalGeneration.from_pretrained("facebook/blenderbot-400M-distill",add_cross_attention=False) 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).tolist() # convert the tokens to text, and then split the responses into the right format response = tokenizer.decode(history[0]).replace("","").split("") response = [(response[i], response[i+1]) for i in range(0, len(response), 2)] # convert to tuples of list return response, history gr.Interface( fn = predict, inputs = ["textbox","state"], outputs = ["chatbot","state"], theme ="seafoam", title = title, description = description, # article = article ).launch(enable_queue=True)