add try except function

app.py

@@ -237,73 +237,75 @@ def main():
         embeddings = load_embeddings()
         sp_docs = split_docs(documents = data)
         st.write(f"This document have {len(sp_docs)} chunks")
-        st.write(sp_docs)
         sp_docs_list.extend(sp_docs)
 
-    st.write(sp_docs_list)
-    db = FAISS.from_documents(sp_docs_list, embeddings)
-    memory = ConversationBufferMemory(memory_key="chat_history", 
-                              return_messages=True, 
-                              input_key="query", 
-                              output_key="result")
-    qa_chain = RetrievalQA.from_chain_type(
-        llm = llm,
-        chain_type = "stuff",
-        retriever = db.as_retriever(search_kwargs = {'k':3}), 
-        return_source_documents = True,
-        memory = memory,
-        chain_type_kwargs = {"prompt":qa_prompt}) 
-
+    try :
+        db = FAISS.from_documents(sp_docs_list, embeddings)
+        memory = ConversationBufferMemory(memory_key="chat_history", 
+                                  return_messages=True, 
+                                  input_key="query", 
+                                  output_key="result")
+        qa_chain = RetrievalQA.from_chain_type(
+            llm = llm,
+            chain_type = "stuff",
+            retriever = db.as_retriever(search_kwargs = {'k':3}), 
+            return_source_documents = True,
+            memory = memory,
+            chain_type_kwargs = {"prompt":qa_prompt}) 
     
-        # qa_chain = ConversationalRetrievalChain(
-        #     retriever =db.as_retriever(search_kwargs={'k':2}), 
-        #     question_generator=question_generator,
-        #     #condense_question_prompt=CONDENSE_QUESTION_PROMPT,
-        #     combine_docs_chain=doc_chain,
-        #     return_source_documents=True,
-        #     memory = memory,
-        #     #get_chat_history=lambda h :h
-        # )
-
-    for message in st.session_state.messages:
-        with st.chat_message(message["role"]):
-            st.markdown(message["content"])
-
-        # Accept user input
-    if query := st.chat_input("What is up?"):
-        # Display user message in chat message container
-        with st.chat_message("user"):
-            st.markdown(query)
-        # Add user message to chat history
-        st.session_state.messages.append({"role": "user", "content": query})
-
-        start = time.time()
-
-        response = qa_chain({'query': query})
-
-        #url_list = set([i.metadata['page']  for i in response['source_documents']])
-        #print(f"condensed quesion : {question_generator.run({'chat_history': response['chat_history'], 'question' : query})}")
         
-        with st.chat_message("assistant"):
-            st.markdown(response['result'])
-        
-        end = time.time()
-        st.write("Respone time:",int(end-start),"sec")
-        print(response)
-                
-        # Add assistant response to chat history
-        st.session_state.messages.append({"role": "assistant", "content": response['result']})
-
-        with st.expander("See the related documents"):
-            for count, url in enumerate(response['source_documents']):
-                #url_reg = regex_source(url)
-                st.write(str(count+1)+":", url)
-
-    clear_button = st.button("Start new convo")
-    if clear_button :
-        st.session_state.messages = []
-        qa_chain.memory.chat_memory.clear() 
+            # qa_chain = ConversationalRetrievalChain(
+            #     retriever =db.as_retriever(search_kwargs={'k':2}), 
+            #     question_generator=question_generator,
+            #     #condense_question_prompt=CONDENSE_QUESTION_PROMPT,
+            #     combine_docs_chain=doc_chain,
+            #     return_source_documents=True,
+            #     memory = memory,
+            #     #get_chat_history=lambda h :h
+            # )
+    
+        for message in st.session_state.messages:
+            with st.chat_message(message["role"]):
+                st.markdown(message["content"])
+    
+            # Accept user input
+        if query := st.chat_input("What is up?"):
+            # Display user message in chat message container
+            with st.chat_message("user"):
+                st.markdown(query)
+            # Add user message to chat history
+            st.session_state.messages.append({"role": "user", "content": query})
+    
+            start = time.time()
+    
+            response = qa_chain({'query': query})
+    
+            #url_list = set([i.metadata['page']  for i in response['source_documents']])
+            #print(f"condensed quesion : {question_generator.run({'chat_history': response['chat_history'], 'question' : query})}")
             
+            with st.chat_message("assistant"):
+                st.markdown(response['result'])
+            
+            end = time.time()
+            st.write("Respone time:",int(end-start),"sec")
+            print(response)
+                    
+            # Add assistant response to chat history
+            st.session_state.messages.append({"role": "assistant", "content": response['result']})
+    
+            with st.expander("See the related documents"):
+                for count, url in enumerate(response['source_documents']):
+                    #url_reg = regex_source(url)
+                    st.write(str(count+1)+":", url)
+    
+        clear_button = st.button("Start new convo")
+        if clear_button :
+            st.session_state.messages = []
+            qa_chain.memory.chat_memory.clear() 
+
+    except :
+        st.write("Plaese upload your pdf file.")
+                
   
 if __name__ == '__main__':
 	main()