init

app.py

@@ -36,19 +36,19 @@ def app():
     button_clear = st.button("清空")
 
     def transformer(source_text, sentences):
+        #使用模型分别计算源字符串和对比字符串的embedding
         model = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2', device="cpu")
         source_emb = model.encode(source_text, convert_to_tensor=True)
         sent_embs = model.encode(sentences, convert_to_tensor=True)
+        #计算源字符串和对比字符串embedding的cos值
         cos_sim = util.cos_sim(source_emb, sent_embs)
 
-        st.write(source_emb)
-        st.write(sent_embs)
-        st.write(cos_sim) #output tensor([[1.0000, 0.3624]])
         cosin_dict = {}
 
         for i, cos in enumerate(torch.flatten(cos_sim)):
             cosin_dict[sentences[i]] = cos
         
+        #根据cos值降序排列
         sorted_dict = dict(sorted(cosin_dict.items(), key=lambda item: item[1],reverse = True))
         return sorted_dict
 
@@ -57,16 +57,11 @@ def app():
         # embeddings
         embeddings = transformer([source_text], st.session_state.inputs)
 
-        # 显示生成的文本
-        st.write(embeddings)
-        #output_text.success(generated_text)
-
+        # 显示对比的字符串、进度条、cos值
         with st.container():
             for sent, cos in embeddings.items():
                 col1, col2, col3 = st.columns(3)
-                
                 cos_value = round(float(cos.item()),4)
-                st.write(cos_value)
                 with col1:
                     st.text(sent)
                 with col2:
@@ -75,6 +70,7 @@ def app():
                     st.text(cos_value)
 
     if button_clear:
+        #清空对比字符串
         st.session_state.inputs.clear()
         del st.session_state["inputs"]
         st.session_state.inputs_index = 0