Create app.py

app.py

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+# -*- coding: utf-8 -*-
+"""
+@author:XuMing([email protected])
+@description:
+"""
+import gradio as gr
+from text2vec import SBert, cos_sim, semantic_search, 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')
+
+
+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))
+
+    return score
+
+
+if __name__ == '__main__':
+    examples = [
+        ['import torch.nn as'],
+        ['parser.add_argument("--num_train_epochs",'],
+        ['torch.device('],
+        ['def set_seed('],
+    ]
+    input1 = gr.inputs.Textbox(placeholder="Enter First Sentence")
+    input2 = gr.inputs.Textbox(placeholder="Enter Second Sentence")
+
+    output_text = gr.outputs.Textbox()
+    gr.Interface(ai_text,
+                 inputs=[input1, input2],
+                 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.",
+                 article="Link to <a href='https://github.com/shibing624/text2vec' style='color:blue;' target='_blank\'>Github REPO</a>",
+                 # examples=examples
+                 ).launch()