Update app.py

app.py

@@ -100,32 +100,8 @@ def get_conversation_chain():
 
 
     return rag_chain
-        
-         
-    
-        
- 
-     
-
-          
-
-
-
-def main():
-    st.set_page_config(page_title="Chat with multiple PDFs", page_icon=":books:")
-    st.write(css, unsafe_allow_html=True)
-
-    st.header("Chat with multiple PDFs :books:")
-
-    vectorstore = None
-    conversation = None   
-
-
-    if user_question := st.text_input("Ask a question about your documents:"):
-        handle_userinput(user_question, vectorstore, conversation)
-    
-   
 
+def sidebar():
     with st.sidebar:
         st.subheader("Your documents")
         pdf_docs = st.file_uploader("For Chatbot to get alive, upload your PDFs here and click on 'Process'", accept_multiple_files=True)
@@ -138,18 +114,42 @@ def main():
                 # get pdf text
                     raw_text = get_pdf_text(pdf_docs)
 
-                # get the text chunks
+                
                     text_chunks = get_text_chunks(raw_text)
 
                 # create vector store
                     vectorstore = get_vectorstore(text_chunks)
 
-                # create conversation chain
+                
                     conversation = get_conversation_chain()
 
                     st.success("Files have been processed into a vector store.")
 
-                    return vectorstore , conversation
+    return vectorstore , conversation
+
+         
+    
+        
+ 
+     
+
+          
+
+
+
+def main():
+    st.set_page_config(page_title="Chat with multiple PDFs", page_icon=":books:")
+    st.write(css, unsafe_allow_html=True)
+
+    st.header("Chat with multiple PDFs :books:")
+
+    vectorstore , conversation =  sidebar()
+
+
+    if user_question := st.text_input("Ask a question about your documents:"):
+        handle_userinput(user_question, vectorstore, conversation)
+    
+   
 
 
     
@@ -176,11 +176,8 @@ def handle_userinput(user_question,vectorstore,conversation ):
     
     retriever = vectorstore.as_retriever(search_type = 'mmr', search_kwargs={"k": 7})
     docs = retriever.invoke(user_question)
-    with st.sidebar:
-        st.subheader("Your documents")
-        with st.spinner("Processing"):
-            for doc in docs:
-                st.write(f"Document: {doc}")
+    
+        
     
     doc_txt = [doc.page_content for doc in docs]
     
@@ -196,11 +193,10 @@ def handle_userinput(user_question,vectorstore,conversation ):
             st.write(bot_template.replace(
                 "{{MSG}}", message['content']), unsafe_allow_html=True)
 
-    # if 'source_documents' in response:
-        # st.subheader("Retrieved Documents")
-        # for doc in response['source_documents']:
-         #    st.write(f"Document: {doc.metadata['source']}")
-          #   st.write(doc.page_content)
+    st.subheader("Your documents")
+        
+    for doc in docs:
+        st.write(f"Document: {doc}")