Update app.py

app.py

@@ -1,10 +1,10 @@
 import os
 import streamlit as st
-from pinecone import Pinecone, ServerlessSpec
+from pinecone import Pinecone
 from sentence_transformers import SentenceTransformer
 
 # Title of the Streamlit App
-st.title("Pinecone Index Management with Streamlit")
+st.title("Pinecone Query Search on 'pubmed-splade' Index")
 
 # Initialize Pinecone globally
 index = None
@@ -20,41 +20,29 @@ def initialize_pinecone():
         st.error("Pinecone API key not found! Please set the PINECONE_API_KEY environment variable.")
         return None
 
-# Function to create or connect to an index
-def create_or_connect_index(pc, index_name, dimension):
-    if index_name not in pc.list_indexes().names():
-        # Create index if it doesn't exist
-        pc.create_index(
-            name=index_name,
-            dimension=dimension,
-            metric='dotproduct',  # Change this based on your use case
-            spec=ServerlessSpec(cloud='aws', region='us-west-2')  # Change to your cloud provider and region
-        )
-        st.success(f"Created new index '{index_name}'")
+# 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}'")
+        index = pc.Index(index_name)
+        return index
     else:
-        st.info(f"Index '{index_name}' already exists.")
-    # Connect to the index
-    index = pc.Index(index_name)
-    return index
+        st.error(f"Index '{index_name}' not found!")
+        return None
 
 # Function to encode query using sentence transformers model
 def encode_query(model, query_text):
     return model.encode(query_text).tolist()
 
 # Initialize Pinecone
-pc = initialize_pinecone()
+pc = initialize_ppinecone()
 
 # If Pinecone initialized successfully, proceed with index management
 if pc:
-    index_name = st.text_input("Enter Index Name", "my_index")
-    dimension = st.number_input("Enter Vector Dimension", min_value=1, value=768)
-    
-    # Button to create or connect to index
-    if st.button("Create or Connect to Index"):
-        global index  # Make index a global variable
-        index = create_or_connect_index(pc, index_name, dimension)
-        if index:
-            st.success(f"Successfully connected to index '{index_name}'")
+    # Connect directly to 'pubmed-splade' index
+    index = connect_to_index(pc)
 
     # Model for query encoding
     model = SentenceTransformer('msmarco-bert-base-dot-v5')
@@ -80,11 +68,3 @@ if pc:
                 st.write(f"ID: {match.id}, Score: {match.score}, Metadata: {match.metadata}")
         else:
             st.error("Please enter a query and ensure the index is initialized.")
-
-# Option to delete index
-if st.button("Delete Index"):
-    if pc and index_name in pc.list_indexes().names():
-        pc.delete_index(index_name)
-        st.success(f"Index '{index_name}' deleted successfully.")
-    else:
-        st.error("Index not found.")