Update app.py

app.py

@@ -13,53 +13,53 @@ st.set_page_config(page_title="Search Engine", layout="wide")
 
 # Set up the Streamlit app title and search bar
 st.title("Search Engine")
-
-with st.form("my_form", clear_on_submit=True):
-    st.write("Login to Search Engine")
-    index_name = st.text_input("Enter a database name:", "")
-    key = st.text_input("Enter a key:", "")
-    namespace = st.text_input("Enter a table name:", "")
-    # slider_val = st.slider("Form slider")
-    # checkbox_val = st.checkbox("Form checkbox")
-    
-    # Every form must have a submit button.
-    submitted = st.form_submit_button("Connect to My Search Engine")
-    if submitted:
-        # 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:", "")
-        #     # 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
+with st.sidebar:
+    with st.form("my_form":
+        st.write("Login to Search Engine")
+        index_name = st.text_input("Enter a database name:", "")
+        key = st.text_input("Enter a key:", "")
+        namespace = st.text_input("Enter a table name:", "")
+        # slider_val = st.slider("Form slider")
+        # checkbox_val = st.checkbox("Form checkbox")
         
-        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...')
-    
+        # Every form must have a submit button.
+        submitted = st.form_submit_button("Connect to My Search Engine")
+        if submitted:
+            # 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:", "")
+            #     # initialize connection to pinecone (get API key at app.pinecone.io)
         
-        query = st.text_input("Enter a search query:", "")
+            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']}")