Update app.py

app.py

@@ -12,54 +12,52 @@ model = SentenceTransformer('intfloat/e5-small')
 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.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")
+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")
+    
+    # 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
         
-        # 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)
+        # configure client
+        pc = Pinecone(api_key=api_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...')
+        from pinecone import ServerlessSpec
         
-            
-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)
+        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...')
     
-    # 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']}")