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import traceback
import gradio as gr
from apps.components import chat_accordion
IDEA_TITLE = "论文速览"
prompt_tmpl = """下面是一篇论文的标题和摘要，请从以下几个方面进行总结：
1. 介绍本文的主要工作
2. 本文工作的主要亮点
3. 核心关键词（最多5个技术，能够代表本文核心技术，格式：`英文` (`中文`) ）
4. 从实用性、创新性和推荐度进行打分（各项满分5分）。
===
标题：{title}
===
摘要：{abstract}
===
注意：生成内容要简练，语言的组织要通顺、容易阅读和理解，并能够快速获取信息。
"""
def paper_preview_demo(client):
def preview(title, abstract, temperature, top_p):
if not title or not abstract:
return None
content = prompt_tmpl.format(title=title, abstract=abstract)
try:
stream = client.simple_chat(
content,
[],
temperature=temperature,
top_p=top_p,
)
for resp, _ in stream:
yield resp
except Exception:
yield traceback.format_exc()
def clear_data():
return None, None
with gr.Row():
with gr.Column():
title = gr.Textbox(label="论文标题")
abstract = gr.Textbox(label="摘要", lines=5)
with gr.Row():
with gr.Column():
submit = gr.Button("速览", variant="primary")
with gr.Column():
clear = gr.Button("清空")
temperature, top_p = chat_accordion()
with gr.Column():
outputs = gr.Textbox(label="速览内容", lines=5)
gr.Examples(
[
[
"GLM: General Language Model Pretraining with Autoregressive Blank Infilling",
"There have been various types of pretraining architectures including autoencoding models "
"(e.g., BERT), autoregressive models (e.g., GPT), and encoder-decoder models (e.g., T5). "
"However, none of the pretraining frameworks performs the best for all tasks of three main "
"categories including natural language understanding (NLU), unconditional generation, and "
"conditional generation. We propose a General Language Model (GLM) based on autoregressive "
"blank infilling to address this challenge. GLM improves blank filling pretraining by adding 2D"
" positional encodings and allowing an arbitrary order to predict spans, which results in "
"performance gains over BERT and T5 on NLU tasks. Meanwhile, GLM can be pretrained for "
"different types of tasks by varying the number and lengths of blanks. On a wide range of tasks"
" across NLU, conditional and unconditional generation, GLM outperforms BERT, T5, and GPT given"
" the same model sizes and data, and achieves the best performance from a single pretrained "
"model with 1.25x parameters of BERT Large , demonstrating its generalizability to different"
" downstream tasks.",
]
],
[title, abstract],
label="样例",
)
submit.click(
preview, inputs=[title, abstract, temperature, top_p], outputs=outputs
)
clear.click(clear_data, inputs=None, outputs=[title, abstract])
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