init

app.py

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+import streamlit as st
+from sentence_transformers import SentenceTransformer, util
+
+def app():
+    # 创建Streamlit应用程序
+
+    st.title("对比句子的相似度")
+
+    source_text = st.text_input("源句子", value="")
+
+    st.write("待比较的句子：")
+
+    if "inputs" not in st.session_state:
+	    # 创建一个空列表来存储输入框列表
+        st.session_state.inputs = []
+        st.session_state.inputs_index = 0
+
+    # 创建一个添加输入框的按钮
+    add_input_button = st.button("添加输入框")
+
+    with st.container():
+        # 在容器中渲染已经存在的输入框列表
+        for i in range(0, st.session_state.inputs_index):
+            st.session_state.inputs[i]= st.text_input(f"请输入第 {i+1} 个句子", "", key=i)
+
+	    # 当用户点击按钮时往容器中添加新的输入框
+        if add_input_button:
+            i = st.session_state.inputs_index
+            st.session_state.inputs.append(st.text_input(f"请输入第 {i+1} 个句子", "", key=i))
+            # 自增输入框的key
+            st.session_state.inputs_index += 1
+    
+    button_generate = st.button("计算")
+
+    def transformer(sentences):
+        model = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2', device="cpu")
+        source_emb = model.encode(source_text, convert_to_tensor=True)
+
+        cosin_dict = []
+
+        for sent in sentences:
+            sent_emb = model.encode(sent, convert_to_tensor=True)
+            cosine_scores = util.cosine_similarity(source_emb, sent_emb)
+            cosin_dict[sent] = cosine_scores
+        
+        sorted_dict = dict(sorted(cosin_dict.items(), key=lambda item: item[1],reverse = True))
+        return sorted_dict
+
+
+    if button_generate:
+        # embeddings
+        embeddings = transformer(st.session_state.inputs)
+
+        # 显示生成的文本
+        st.write(embeddings)
+        #output_text.success(generated_text)
+
+if __name__ == "__main__":
+    # 运行应用程序
+    app()

requirements.txt

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+streamlit
+sentence_transformers