Update app.py

app.py

@@ -21,8 +21,11 @@ if api_key:
     # Step 2: Upload PDF Document
     uploaded_file = st.file_uploader("📂 Upload a PDF document", type=["pdf"])
 
-    if uploaded_file:
-        # Load and process the document
+    # Use session state to persist the vector_store
+    if "vector_store" not in st.session_state:
+        st.session_state.vector_store = None
+
+    if uploaded_file and st.session_state.vector_store is None:
         try:
             with st.spinner("Processing document..."):
                 # Save the uploaded file temporarily
@@ -43,21 +46,22 @@ if api_key:
 
                 # Generate embeddings and store them in a vector database
                 embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
-                vector_store = FAISS.from_documents(chunks, embeddings)
+                st.session_state.vector_store = FAISS.from_documents(chunks, embeddings)
 
             st.success("Document processed successfully!")
         except Exception as e:
             st.error(f"Error processing document: {e}")
             st.stop()
 
-        # Step 3: Ask Questions About the Document
+    # Step 3: Ask Questions About the Document
+    if st.session_state.vector_store:
         st.subheader("💬 Chat with Your Document")
         user_query = st.text_input("Ask a question:")
 
         if user_query:
             try:
                 # Set up the RAG pipeline with DeepSeek LLM
-                retriever = vector_store.as_retriever()
+                retriever = st.session_state.vector_store.as_retriever()
                 llm = ChatOpenAI(
                     model="deepseek-chat",
                     openai_api_key=api_key,