Create app.py

app.py

@@ -0,0 +1,53 @@
+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 sentence_transformers import SentenceTransformer
+
+# Temporary directory for persistence in Streamlit Cloud
+temp_dir = tempfile.TemporaryDirectory()
+
+@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)
+
+# Streamlit UI
+st.title("🧠 RAG Model Query Interface")
+st.write("Enter a query to get relevant SHL test assessments.")
+
+# Query input
+user_query = st.text_input("Enter your query:")
+
+if st.button("Submit"):
+    if user_query:
+        results = ask_query(user_query, model, collection)
+        if results:
+            st.write(f"📊 Results for query: {user_query}")
+            st.write("=" * 80)
+            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)
+        else:
+            st.warning("No results found.")
+    else:
+        st.warning("Please enter a query.")