upgrade gradio to 3.12.0

app.py

@@ -11,6 +11,7 @@ https://gradio.app/docs/#tabbedinterface-header
 -
 """
 
+import gradio_patch
 import gradio as gr
 from demo_sum import sum_iface
 from demo_mlm import mlm_iface

demo_chatbot_jddc.py

@@ -4,6 +4,7 @@
 
 import torch
 import gradio as gr
+from info import article
 from kplug import modeling_kplug_s2s_patch
 from transformers import BertTokenizer, BartForConditionalGeneration
 
@@ -50,13 +51,13 @@ jddc_iface = gr.Interface(
     inputs=[
         gr.Textbox(
             label="输入文本",
-            value="发什么快递"), # gr.State() 报错
+            value="发什么快递"),  # gr.State() 报错
         "state"
     ],
     outputs=["chatbot", "state"],
-
     examples=jddc_examples,
     title="电商客服--生成式对话（Response Generation）",
+    article=article,
 )
 
 if __name__ == "__main__":

demo_corrector.py

@@ -5,6 +5,7 @@
 import time
 import torch
 import gradio as gr
+from info import article
 from transformers import FillMaskPipeline
 from transformers import BertTokenizer
 from kplug.modeling_kplug import KplugForMaskedLM
@@ -79,7 +80,8 @@ corr_iface = gr.Interface(
     ],
     examples=error_sentences,
     title="文本纠错（Corrector）",
-    description='自动对汉语文本中的拼写、语法、标点等多种问题进行纠错校对，提示错误位置并返回修改建议'
+    description='自动对汉语文本中的拼写、语法、标点等多种问题进行纠错校对，提示错误位置并返回修改建议',
+    article=article
 )
 
 if __name__ == "__main__":

demo_mlm.py

@@ -18,6 +18,7 @@ interface = gr.Interface.load(
 """
 
 import gradio as gr
+from info import article
 from transformers import FillMaskPipeline
 from transformers import BertTokenizer
 from kplug.modeling_kplug import KplugForMaskedLM
@@ -50,9 +51,8 @@ mlm_iface = gr.Interface(
     ),
     examples=mlm_examples,
     title="文本填词（Fill Mask）",
-    description='基于KPLUG预训练语言模型，'
-                '<a href=""> K-PLUG: Knowledge-injected Pre-trained Language Model for Natural Language Understanding'
-                ' and Generation in E-Commerce (Findings of EMNLP 2021) </a>。'
+    description='基于KPLUG预训练语言模型',
+    article=article
 )
 
 if __name__ == "__main__":

demo_sum.py

@@ -12,6 +12,7 @@ promp参数
 
 import torch
 import gradio as gr
+from info import article
 from kplug import modeling_kplug_s2s_patch
 from transformers import BertTokenizer, BartForConditionalGeneration
 
@@ -47,10 +48,8 @@ sum_iface = gr.Interface(
     ),
     examples=sum_examples,
     title="生成式摘要（Abstractive Summarization）",
-    description='<div>这是一个生成式摘要的demo，用于电商领域的商品营销文案写作。'
-                '该demo基于KPLUG预训练语言模型，输入商品信息，输出商品的营销文案。</div>'
-                '<div><a href="https://aclanthology.org/2021.findings-emnlp.1/">pdf</a> </div>'
-                '<div><a href="https://github.com/xu-song/k-plug">code </a> </div>'
+    description='生成式摘要，用于电商领域的商品营销文案写作。输入商品信息，输出商品的营销文案。',
+    article=article
 )
 
 if __name__ == "__main__":

gradio_patch.py

@@ -0,0 +1,10 @@
+# coding=utf-8
+
+"""
+huggingface默认gradio版本是3.1.7。在多tab下的chatbot报错。因此需要更新到3.12.0
+https://www.kingname.info/2019/10/29/pip-in-code/
+"""
+
+from pip._internal import main
+
+main(['install', 'gradio==3.12.0'])

info.py

@@ -0,0 +1,2 @@
+
+article = "<p style='text-align: center'><a href='https://aclanthology.org/2021.findings-emnlp.1/'>K-PLUG: Knowledge-injected Pre-trained Language Model for Natural Language Understanding and Generation in E-Commerce</a> | <a href='https://github.com/xu-song/k-plug'>Github Repo</a></p>"