Update app.py

app.py

@@ -50,11 +50,13 @@ def get_conversational_chain():
     return chain
 
 def user_input(user_question, api_key):
+    st.spinner("Processing...")
     embeddings = HuggingFaceInferenceAPIEmbeddings(api_key=api_key, model_name="sentence-transformers/all-MiniLM-l6-v2")
     new_db = FAISS.load_local("faiss_index", embeddings, allow_dangerous_deserialization=True)
     docs = new_db.similarity_search(user_question)
     chain = get_conversational_chain()
     response = chain({"input_documents": docs, "question": user_question}, return_only_outputs=True)
+    st.success("Processing Complete")
     st.write("Replies:")
     if isinstance(response["output_text"], str):
         response_list = [response["output_text"]]
@@ -89,12 +91,16 @@ def main():
             text_chunks = get_text_chunks(raw_text)
             get_vector_store(text_chunks, api_key)
             st.success("Processing Complete")
+            
     if pdf_docs and st.success("Processing Complete"):
         with col1:
             raw_text = get_pdf_text(pdf_docs)
             user_question = st.text_input("Ask a question from the Docs")
             if user_question:
                 user_input(user_question, api_key)
+    else:
+        with col1:
+            st.write("Please upload a document first to ask questions.")
 
     # Display extracted text and handle user interaction if raw_text is not None
     if raw_text is not None: