Update app.py

app.py

@@ -4,7 +4,7 @@ from pinecone import Pinecone
 from sentence_transformers import SentenceTransformer
 
 # Title of the Streamlit App
-st.title("Pinecone Query Search on 'pubmed-splade' Index")
+st.title("Medical Hybrid Search")
 
 # Initialize Pinecone globally
 index = None
@@ -14,7 +14,8 @@ def initialize_pinecone():
     api_key = os.getenv('PINECONE_API_KEY')  # Get Pinecone API key from environment variable
     if api_key:
         # Initialize Pinecone client using the new class instance method
-        return Pinecone(api_key=api_key)
+        pc = Pinecone(api_key=api_key)
+        return pc
     else:
         st.error("Pinecone API key not found! Please set the PINECONE_API_KEY environment variable.")
         return None
@@ -22,9 +23,10 @@ def initialize_pinecone():
 # Function to connect to the 'pubmed-splade' index
 def connect_to_index(pc):
     index_name = 'pubmed-splade'  # Hardcoded index name
+    # Connect to the 'pubmed-splade' index
     if index_name in pc.list_indexes().names():
-        st.info(f"Successfully connected to index '{index_name}'")
-        return pc.Index(index_name)
+        index = pc.Index(index_name)
+        return index
     else:
         st.error(f"Index '{index_name}' not found!")
         return None
@@ -38,9 +40,10 @@ pc = initialize_pinecone()
 
 # If Pinecone initialized successfully, proceed with index management
 if pc:
+    # Connect directly to 'pubmed-splade' index
     index = connect_to_index(pc)
 
-    # Load model for query encoding
+    # Model for query encoding
     model = SentenceTransformer('msmarco-bert-base-dot-v5')
 
     # Query input
@@ -50,25 +53,18 @@ if pc:
     if st.button("Search Query"):
         if query_text and index:
             dense_vector = encode_query(model, query_text)
-
+            
             # Search the index
             results = index.query(
                 vector=dense_vector,
-                top_k=5,
+                top_k=3,
                 include_metadata=True
             )
             
             st.write("### Search Results:")
-            if results.matches:
-                for match in results.matches:
-                    score = match.score
-                    context = match.metadata.get("context", "No context available")
-                    
-                    # Display score and context in a formatted way
-                    st.markdown(f"**Score**: `{score}`")
-                    st.markdown(f"**Context**: {context}")
-                    st.markdown("---")  # Divider for each result
-            else:
-                st.warning("No results found for this query.")
+            for match in results.matches:
+                st.markdown(f"#### Score: **{match.score:.4f}**")
+                st.write(f"**Context:** {match.metadata.get('context', 'No context available.')}")
+                st.write("---")
         else:
             st.error("Please enter a query and ensure the index is initialized.")