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| import os | |
| os.system('pip install gradio==2.3.5b0') | |
| from transformers import AutoModelForCausalLM, AutoTokenizer | |
| import torch | |
| import gradio as gr | |
| tokenizer = AutoTokenizer.from_pretrained("microsoft/DialoGPT-small") | |
| model = AutoModelForCausalLM.from_pretrained("microsoft/DialoGPT-small") | |
| def dialogpt(text): | |
| history = gr.get_state() or [] | |
| # encode the new user input, add the eos_token and return a tensor in Pytorch | |
| for step in range(50000): | |
| new_user_input_ids = tokenizer.encode(text + 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=100, pad_token_id=tokenizer.eos_token_id) | |
| response = tokenizer.decode(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True) | |
| history.append((text, response)) | |
| gr.set_state(history) | |
| # pretty print last ouput tokens from bot | |
| html = "<div class='chatbot'>" | |
| for user_msg, resp_msg in history: | |
| html += f"<div class='user_msg'>{user_msg}</div>" | |
| html += f"<div class='resp_msg'>{resp_msg}</div>" | |
| html += "</div>" | |
| return html | |
| inputs = gr.inputs.Textbox(lines=1, label="Input Text") | |
| outputs = gr.outputs.Textbox(label="DialoGPT") | |
| title = "DialoGPT" | |
| description = "Gradio demo for Microsoft DialoGPT: A State-of-the-Art Large-scale Pretrained Response Generation Model. To use it, simply input text or click one of the examples text to load them. Read more at the links below." | |
| article = "<p style='text-align: center'><a href='https://arxiv.org/abs/1911.00536'>DialoGPT: Large-Scale Generative Pre-training for Conversational Response Generation</a> | <a href='https://github.com/microsoft/DialoGPT'>Github Repo</a></p>" | |
| examples = [ | |
| ["Hi, how are you?"], | |
| ["How far away is the moon?"], | |
| ] | |
| gr.Interface(dialogpt, inputs, "html", title=title, description=description, article=article, examples=examples,css=""" | |
| .chatbox {display:flex;flex-direction:column} | |
| .user_msg, .resp_msg {padding:4px;margin-bottom:4px;border-radius:4px;width:80%} | |
| .user_msg {background-color:cornflowerblue;color:white;align-self:start} | |
| .resp_msg {background-color:lightgray;align-self:self-end} | |
| """).launch(debug=True) |