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| import gradio as gr | |
| from transformers import AutoModelForCausalLM, AutoTokenizer | |
| import torch | |
| tokenizer = AutoTokenizer.from_pretrained("microsoft/DialoGPT-medium") | |
| model = AutoModelForCausalLM.from_pretrained("microsoft/DialoGPT-medium") | |
| def user(message, history): | |
| return "", history + [[message, None]] | |
| def bot(history): | |
| user_message = history[-1][0] | |
| new_user_input_ids = tokenizer.encode( | |
| user_message + tokenizer.eos_token, return_tensors="pt" | |
| ) | |
| # append the new user input tokens to the chat history | |
| bot_input_ids = torch.cat([torch.LongTensor([]), new_user_input_ids], dim=-1) | |
| # generate a response | |
| response = 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 lines | |
| response = tokenizer.decode(response[0]).split("<|endoftext|>") | |
| response = [ | |
| (response[i], response[i + 1]) for i in range(0, len(response) - 1, 2) | |
| ] # convert to tuples of list | |
| history[-1] = response[0] | |
| return history | |
| with gr.Blocks() as demo: | |
| chatbot = gr.Chatbot() | |
| msg = gr.Textbox() | |
| clear = gr.Button("Clear") | |
| msg.submit(user, [msg, chatbot], [msg, chatbot], queue=False).then( | |
| bot, chatbot, chatbot | |
| ) | |
| clear.click(lambda: None, None, chatbot, queue=False) | |
| if __name__ == "__main__": | |
| demo.launch() | |