Update app.py

app.py

@@ -13,7 +13,7 @@ st.set_page_config(page_title="Search Engine", layout="wide")
 
 # Set up the Streamlit app title and search bar
 st.title("Search Engine")
-if st.button("Connect to Search Engine Database", type="primary")
+if st.button("Connect to Search Engine Database", type="primary"):
     index_name = st.text_input("Enter a database name:", "")
     key = st.text_input("Enter a key:", "")
     namespace = st.text_input("Enter a table name:", "")
@@ -34,20 +34,19 @@ if st.button("Connect to Search Engine Database", type="primary")
     # connect to index
     index = pc.Index(index_name)
     st.write('Successfully connected to your Search Engine DB!')
-else:
-    st.write("Goodbye")
+    st.write('Start searching...')
 
     
-query = st.text_input("Enter a search query:", "")
-
-# If the user has entered a search query, search the Pinecone index with the query
-if query:
-    # Upsert the embeddings for the query into the Pinecone index
-    query_embeddings =  model.encode(query).tolist()
-    # now query
-    xc = index.query(vector=query_embeddings, top_k=10, namespace=namespace, include_metadata=True)
-    
-    # Display the search results
-    st.write(f"Search results for '{query}':")
-    for result in xc['matches']:
-        st.write(f"{round(result['score'], 2)}: {result['metadata']['meta_text']}")
+    query = st.text_input("Enter a search query:", "")
+    
+    # If the user has entered a search query, search the Pinecone index with the query
+    if query:
+        # Upsert the embeddings for the query into the Pinecone index
+        query_embeddings =  model.encode(query).tolist()
+        # now query
+        xc = index.query(vector=query_embeddings, top_k=10, namespace=namespace, include_metadata=True)
+        
+        # Display the search results
+        st.write(f"Search results for '{query}':")
+        for result in xc['matches']:
+            st.write(f"{round(result['score'], 2)}: {result['metadata']['meta_text']}")