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from transformers import AutoModel, AutoTokenizer
import gradio as gr
import json
model_path = 'THUDM/chatglm-6b'
tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
model = AutoModel.from_pretrained(model_path, trust_remote_code=True).bfloat16()
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.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)
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