Updated file

app.py

@@ -15,14 +15,12 @@ def read_pdf(uploaded_file):
     return text
 
 def querying(query_engine):
-    progress_container = st.empty()
     query = st.text_input("Enter the Query for PDF:")
     submit = st.button("Generate The response for the query")
-
     if submit:
-         progress_container.text("Fetching the response...")
-         response = query_engine.query(query)
-         st.write(f"**Response:** {response}")
+        with st.spinner("Fetching the response..."):
+            response = query_engine.query(query)
+            st.write(f"**Response:** {response}")
         
         
         # docs = document_search.similarity_search(query_text)
@@ -40,20 +38,20 @@ def main():
         documents = Document(text=file_contents)
         documents = [documents]
         st.success("Documents loaded successfully!")
-         
-        embed_model_uae = HuggingFaceEmbedding(model_name="WhereIsAI/UAE-Large-V1")
-        service_context = ServiceContext.from_defaults(llm=llm, chunk_size=800, chunk_overlap=20, embed_model=embed_model_uae)
-         
-         # Indexing the documents
-        progress_container = st.empty()
-        progress_container.text("Creating VectorStoreIndex...")
-        # Download embeddings from OpenAI
         
-        index = VectorStoreIndex.from_documents(documents, service_context=service_context, show_progress=True)
-        index.storage_context.persist()
-        query_engine = index.as_query_engine()
-        st.success("VectorStoreIndex created successfully!")
         
+        with st.spinner("Created Embedding model..."):
+            embed_model_uae = HuggingFaceEmbedding(model_name="WhereIsAI/UAE-Large-V1")
+            service_context = ServiceContext.from_defaults(llm=llm, chunk_size=800, chunk_overlap=20, embed_model=embed_model_uae)
+            st.success("Embedding model created successfully!")
+         
+        # Download embeddings from OpenAI
+        with st.spinner("Created VectorStoreIndex..."):
+            index = VectorStoreIndex.from_documents(documents, service_context=service_context, show_progress=True)
+            index.storage_context.persist()
+            query_engine = index.as_query_engine()
+            st.success("VectorStoreIndex created successfully!")
+            
         querying(query_engine)