Update app.py

app.py

@@ -1,53 +1,55 @@
 import streamlit as st
-import tempfile
-
-# Use DuckDB instead of SQLite for ChromaDB
-import chromadb
-from chromadb.config import Settings
-# Import your RAG components
-from rag import build_chroma_store, pre_processing_csv, ask_query
+from rag import pre_processing_csv, build_pinecone_store, ask_query
 from sentence_transformers import SentenceTransformer
+from dotenv import load_dotenv
+import os
 
-# Temporary directory for persistence in Streamlit Cloud
-temp_dir = tempfile.TemporaryDirectory()
+# Load environment variables from .env file
+load_dotenv()
+PINECONE_API_KEY = os.getenv("PINECONE_API_KEY")
+PINECONE_ENV = os.getenv("PINECONE_ENV")  # e.g., "us-west-2"
+PINECONE_INDEX_NAME = os.getenv("PINECONE_INDEX_NAME", "shl-test-index")
 
+# Cache data processing and vector store creation for efficiency
 @st.cache_resource
-def load_data(csv_path):
-    """Load and process data, caching the results."""
-    docs, metas = pre_processing_csv(csv_path)
-
-    # Use DuckDB + Parquet instead of SQLite
-    client = chromadb.Client(Settings(
-        persist_directory=temp_dir.name  
-    ))
-
-    collection, model = build_chroma_store(docs, metas, client=client)
-    return collection, model
-
-# Load your CSV data
-csv_path = "shl_products.csv"  # Update path if needed
-collection, model = load_data(csv_path)
+def load_data():
+    csv_path = "shl_products.csv"  # Update the path if necessary
+    # Step 1: Preprocess the CSV and create document chunks
+    documents, metadatas = pre_processing_csv(csv_path)
+    
+    # Load the SentenceTransformer model (this may take some time at first run)
+    model = SentenceTransformer("all-MiniLM-L6-v2")
+    
+    # Step 2: Build the Pinecone vector store with embeddings
+    index, model, embeddings, documents, metadatas = build_pinecone_store(
+        documents, metadatas, model, PINECONE_INDEX_NAME, PINECONE_API_KEY, PINECONE_ENV
+    )
+    return index, model
+
+# Load and cache the data, index, and model
+index, model = load_data()
 
 # Streamlit UI
 st.title("🧠 RAG Model Query Interface")
-st.write("Enter a query to get relevant SHL test assessments.")
+st.write("Enter a query to retrieve relevant SHL assessments.")
 
-# Query input
-user_query = st.text_input("Enter your query:")
+# Query input widget
+user_query = st.text_input("Your query:")
 
-if st.button("Submit"):
+if st.button("Submit Query"):
     if user_query:
-        results = ask_query(user_query, model, collection)
+        # Step 3: Query the RAG model using the Pinecone index
+        results = ask_query(user_query, model, index, k=10)
+        
         if results:
-            st.write(f"📊 Results for query: {user_query}")
-            st.write("=" * 80)
+            st.markdown(f"### Results for: `{user_query}`")
             for i, (doc, meta) in enumerate(results, 1):
-                st.markdown(f"🔹 **Result {i}**")
-                st.markdown(f"🧪 **Test Name:** {meta['Test Name']}")
-                st.markdown(f"🔗 **Link:** [https://www.shl.com{meta['Test Link']}]")
-                st.markdown(f"📄 **Chunk:** {doc}")
-                st.write("-" * 80)
+                st.markdown(f"**Result {i}:**")
+                st.markdown(f"**Test Name:** {meta.get('Test Name', '')}")
+                st.markdown(f"**Test Link:** [SHL Link](https://www.shl.com{meta.get('Test Link', '')})")
+                st.markdown(f"**Chunk:** {doc}")
+                st.markdown("---")
         else:
-            st.warning("No results found.")
+            st.info("No results found. Please try a different query.")
     else:
-        st.warning("Please enter a query.")
+        st.warning("Please enter a query to search.")