|
|
from transformers import AutoModel, AutoTokenizer
|
|
|
import gradio as gr
|
|
|
|
|
|
tokenizer = AutoTokenizer.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True)
|
|
|
model = AutoModel.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True).half().cuda()
|
|
|
model = model.eval()
|
|
|
|
|
|
MAX_TURNS = 20
|
|
|
MAX_BOXES = MAX_TURNS * 2
|
|
|
|
|
|
|
|
|
def predict(input, max_length, top_p, temperature, history=None):
|
|
|
if history is None:
|
|
|
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="用户：" + query))
|
|
|
updates.append(gr.update(visible=True, value="ChatGLM-6B：" + 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.Markdown(visible=False, label="提问："))
|
|
|
else:
|
|
|
text_boxes.append(gr.Markdown(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)
|
|
|
button = gr.Button("Generate")
|
|
|
button.click(predict, [txt, max_length, top_p, temperature, state], [state] + text_boxes)
|
|
|
demo.queue().launch(share=True, inbrowser=True) |
