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| import transformers | |
| import gradio as gr | |
| import torch | |
| from transformers import GPT2LMHeadModel, GPT2Tokenizer | |
| model_name = 'dennis-fast/DialoGPT-ElonMusk' | |
| #model_name = 'luca-martial/DialoGPT-Elon' | |
| 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, | |
| #max_length=200, | |
| pad_token_id=tokenizer.eos_token_id, | |
| no_repeat_ngram_size=3, | |
| #do_sample=True, | |
| #temperature = 0.8 | |
| top_p = 0.92, | |
| top_k = 50 | |
| ).tolist() | |
| # convert the tokens to text, and then split the responses into the right format | |
| 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 | |
| if len(response) > 4: | |
| response.pop(0) | |
| return response, history | |
| gr.Interface(fn=predict, | |
| theme="default", | |
| css=".footer {display:none !important}", | |
| inputs=["text", "state"], | |
| examples=[['Hi, please introduce yourself.'],['Where do you live?'],['What is meaning of life?'],['Should I buy Dogecoin?']], | |
| outputs=["chatbot", "state"]).launch() |