Update app.py

app.py

@@ -18,35 +18,36 @@ if st.button("Connect to Search Engine Database", type="primary"):
     key = st.text_input("Enter a key:", "")
     namespace = st.text_input("Enter a table name:", "")
     # initialize connection to pinecone (get API key at app.pinecone.io)
-    api_key = os.environ.get('PINECONE_API_KEY') or key
-    
-    # configure client
-    pc = Pinecone(api_key=api_key)
-    
-    from pinecone import ServerlessSpec
-    
-    cloud = os.environ.get('PINECONE_CLOUD') or 'aws'
-    region = os.environ.get('PINECONE_REGION') or 'us-east-1'
-    
-    spec = ServerlessSpec(cloud=cloud, region=region)
-    
-    
-    # connect to index
-    index = pc.Index(index_name)
-    st.write('Successfully connected to your Search Engine DB!')
-    st.write('Start searching...')
-
-    
-    query = st.text_input("Enter a search query:", "")
+    if key:
+        api_key = os.environ.get('PINECONE_API_KEY') or key
+        
+        # configure client
+        pc = Pinecone(api_key=api_key)
+        
+        from pinecone import ServerlessSpec
+        
+        cloud = os.environ.get('PINECONE_CLOUD') or 'aws'
+        region = os.environ.get('PINECONE_REGION') or 'us-east-1'
+        
+        spec = ServerlessSpec(cloud=cloud, region=region)
+        
+        
+        # connect to index
+        index = pc.Index(index_name)
+        st.write('Successfully connected to your Search Engine DB!')
+        st.write('Start searching...')
     
-    # 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']}")