Create app.py

app.py

@@ -0,0 +1,51 @@
+# -*- coding: utf-8 -*-
+"""
+@author:XuMing([email protected])
+@description: text similarity example, fine-tuned by CoSENT model
+"""
+import gradio as gr
+from similarities import Similarity
+
+# 中文句向量模型
+sim_model = Similarity(model_name_or_path='shibing624/text2vec-base-chinese')
+
+
+def load_file(path):
+    with open(path, 'r', encoding='utf-8') as f:
+        return f.read().split('\n')
+
+
+sim_model.add_corpus(load_file('corpus.txt'))
+
+
+def ai_text(query):
+    res = sim_model.most_similar(queries=query, topn=5)
+    print(res)
+    for q_id, c in res.items():
+        print('query:', query)
+        print("search top 5:")
+        for corpus_id, s in c.items():
+            print(f'\t{sim_model.corpus[corpus_id]}: {s:.4f}')
+    res_show = '\n'.join(['search top5：'] + [f'text: {sim_model.corpus[corpus_id]} score: {s:.4f}' for corpus_id, s in
+                                             list(res.values())[0].items()])
+    return res_show
+
+
+if __name__ == '__main__':
+    examples = [
+        ['星巴克被嘲笑了'],
+        ['西班牙失业率超过50%'],
+        ['她在看书'],
+        ['一个人弹琴'],
+    ]
+    input = gr.inputs.Textbox(lines=2, placeholder="Enter Query")
+    output_text = gr.outputs.Textbox()
+    gr.Interface(ai_text,
+                 inputs=[input],
+                 outputs=[output_text],
+                 theme="grass",
+                 title="Chinese Text Semantic Search Model",
+                 description="Copy or input Chinese text here. Submit and the machine will find the most similarity texts.",
+                 article="Link to <a href='https://github.com/shibing624/similarities' style='color:blue;' target='_blank\'>Github REPO</a>",
+                 examples=examples
+                 ).launch()