Update app.py

app.py

@@ -1,25 +1,16 @@
 # -*- coding: utf-8 -*-
 """
 @author:XuMing([email protected])
-@description:
+@description: text similarity example, fine-tuned by CoSENT model
 """
 import gradio as gr
-from text2vec import SBert, cos_sim, semantic_search, Similarity
+from text2vec import Similarity
 
 # 中文句向量模型(CoSENT)
 sim_model = Similarity(model_name_or_path='shibing624/text2vec-base-chinese',
-                           similarity_type='cosine', embedding_type='sbert')
+                       similarity_type='cosine', embedding_type='sbert')
 
 
-sentences1 = ['如何更换花呗绑定银行卡',
-              'The cat sits outside',
-              'A man is playing guitar',
-              'The new movie is awesome']
-
-sentences2 = ['花呗更改绑定银行卡',
-              'The dog plays in the garden',
-              'A woman watches TV',
-              'The new movie is so great']
 def ai_text(sentence1, sentence2):
     score = sim_model.get_score(sentence1, sentence2)
     print("{} \t\t {} \t\t Score: {:.4f}".format(sentence1, sentence2, score))
@@ -29,13 +20,13 @@ def ai_text(sentence1, sentence2):
 
 if __name__ == '__main__':
     examples = [
-        ['import torch.nn as'],
-        ['parser.add_argument("--num_train_epochs",'],
-        ['torch.device('],
-        ['def set_seed('],
+        ['如何更换花呗绑定银行卡', '花呗更改绑定银行卡'],
+        ['我在北京打篮球', '我是北京人，我喜欢篮球'],
+        ['一个女人在看书。', '一个女人在揉面团'],
+        ['一个男人在车库里举重。', '一个人在举重。'],
     ]
-    input1 = gr.inputs.Textbox(placeholder="Enter First Sentence")
-    input2 = gr.inputs.Textbox(placeholder="Enter Second Sentence")
+    input1 = gr.inputs.Textbox(lines=2, placeholder="Enter First Sentence")
+    input2 = gr.inputs.Textbox(lines=2, placeholder="Enter Second Sentence")
 
     output_text = gr.outputs.Textbox()
     gr.Interface(ai_text,
@@ -43,7 +34,7 @@ if __name__ == '__main__':
                  outputs=[output_text],
                  theme="grass",
                  title="Chinese Text to Vector Model shibing624/text2vec-base-chinese",
-                 description="Copy or input python code here. Submit and the machine will calculate the cosine score.",
+                 description="Copy or input Chinese text here. Submit and the machine will calculate the cosine score.",
                  article="Link to <a href='https://github.com/shibing624/text2vec' style='color:blue;' target='_blank\'>Github REPO</a>",
-                 # examples=examples
-                 ).launch()
+                 examples=examples
+                 ).launch()