import gradio as gr from typing import List, Optional from transformers import BertTokenizer, BartForConditionalGeneration title = "HIT-TMG/dialogue-bart-large-chinese" description = """ This is a seq2seq model fine-tuned on several Chinese dialogue datasets, from bart-large-chinese. \n See some details of model card at https://huggingface.co/HIT-TMG/dialogue-bart-large-chinese . \n\n Besides starting the conversation from scratch, you can also input the whole dialogue history utterance by utterance seperated by '[SEP]'. \n (e.g. "可以认识一下吗[SEP]当然可以啦,你好。[SEP]嘿嘿你好,请问你最近在忙什么呢?[SEP]我最近养了一只狗狗,我在训练它呢。") \n """ tokenizer = BertTokenizer.from_pretrained("HIT-TMG/dialogue-bart-large-chinese-DuSinc") model = BartForConditionalGeneration.from_pretrained("HIT-TMG/dialogue-bart-large-chinese-DuSinc") tokenizer.truncation_side = 'left' max_length = 512 def chat_func(input_utterance: str, history: Optional[List[str]] = None): if history is not None: history.extend(input_utterance.split(tokenizer.sep_token)) else: history = input_utterance.split(tokenizer.sep_token) history_str = "[history] " + tokenizer.sep_token.join(history) input_ids = tokenizer(history_str, return_tensors='pt', truncation=True, max_length=max_length).input_ids output_ids = model.generate(input_ids, max_new_tokens=30)[0] response = tokenizer.decode(output_ids, skip_special_tokens=True) history.append(response) if len(history) % 2 == 0: display_utterances = [(history[i], history[i + 1]) for i in range(0, len(history) - 1, 2)] else: display_utterances = [("", history[0])] + [(history[i], history[i + 1]) for i in range(1, len(history) - 1, 2)] return display_utterances, history demo = gr.Interface(fn=chat_func, title=title, description=description, inputs=[gr.Textbox(lines=1, placeholder="Input current utterance"), "state"], outputs=["chatbot", "state"]) if __name__ == "__main__": demo.launch()