from transformers import AutoModel, AutoTokenizer import gradio as gr import json model_path = 'THUDM/chatglm-6b-int4' tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True) model = AutoModel.from_pretrained(model_path, trust_remote_code=True).half().float() model = model.eval() MAX_TURNS = 20 MAX_BOXES = MAX_TURNS * 2 def predict(input, max_length, top_p, temperature, history=None, state=None): if state is None: state = [] if history is None or history == "": history = state else: history = json.loads(history) for response, history in model.stream_chat(tokenizer, input, history, max_length=max_length, top_p=top_p, temperature=temperature): updates = [] for query, response in history: updates.append(gr.update(visible=True, value=query)) updates.append(gr.update(visible=True, value=response)) if len(updates) < MAX_BOXES: updates = updates + [gr.Textbox.update(visible=False)] * (MAX_BOXES - len(updates)) yield [history] + updates with gr.Blocks() as demo: state = gr.State([]) text_boxes = [] for i in range(MAX_BOXES): if i % 2 == 0: text_boxes.append(gr.Text(visible=False, label="提问:")) else: text_boxes.append(gr.Text(visible=False, label="回复:")) with gr.Row(): with gr.Column(scale=4): txt = gr.Textbox(show_label=False, placeholder="Enter text and press enter", lines=11).style( container=False) with gr.Column(scale=1): max_length = gr.Slider(0, 4096, value=2048, step=1.0, label="Maximum length", interactive=True) top_p = gr.Slider(0, 1, value=0.7, step=0.01, label="Top P", interactive=True) temperature = gr.Slider(0, 1, value=0.95, step=0.01, label="Temperature", interactive=True) history = gr.TextArea(visible=False) button = gr.Button("Generate") button.click(predict, [txt, max_length, top_p, temperature, history, state], [state] + text_boxes, queue=True) demo.queue(concurrency_count=10).launch(enable_queue=True, max_threads=2)