Update app.py

app.py

@@ -3,7 +3,7 @@ import os
 from langchain_community.vectorstores import FAISS
 from langchain_community.document_loaders import PyPDFLoader
 from langchain.text_splitter import RecursiveCharacterTextSplitter
-from langchain_community.embeddings import HuggingFaceEmbeddings 
+from langchain_community.embeddings import HuggingFaceEmbeddings
 from langchain_huggingface import HuggingFaceEndpoint  # Updated import
 from langchain.chains import ConversationalRetrievalChain
 from langchain.memory import ConversationBufferMemory
@@ -27,7 +27,7 @@ def load_doc(uploaded_files):
         for loader in loaders:
             pages.extend(loader.load())
         
-        text_splitter = RecursiveCharacterTextSplitter(chunk_size=1024, chunk_overlap=64)  
+        text_splitter = RecursiveCharacterTextSplitter(chunk_size=1024, chunk_overlap=64)
         doc_splits = text_splitter.split_documents(pages)
 
         for temp_file in temp_files:
@@ -66,7 +66,7 @@ def initialize_llmchain(llm_model, vector_db):
         qa_chain = ConversationalRetrievalChain.from_llm(
             llm,
             retriever=retriever,
-            chain_type="stuff", 
+            chain_type="stuff",
             memory=memory,
             return_source_documents=True,
             verbose=False,
@@ -122,10 +122,10 @@ def conversation(qa_chain, message, history):
         response_source3 = response_sources[2].page_content.strip()
         response_source3_page = response_sources[2].metadata["page"] + 1
         new_history = history + [(message, response_answer)]
-        return qa_chain, new_history, response_source1, response_source1_page, response_source2, response_source2_page, response_source3, response_source3_page
+        return qa_chain, new_history, response_answer, response_source1, response_source1_page, response_source2, response_source2_page, response_source3, response_source3_page
     except Exception as e:
         st.error(f"Error in conversation: {e}")
-        return qa_chain, history, "", 0, "", 0, "", 0
+        return qa_chain, history, "", "", 0, "", 0, "", 0
 
 def main():
     st.sidebar.title("PDF Chatbot")
@@ -163,17 +163,19 @@ def main():
 
         if st.button("Submit"):
             with st.spinner("Generating response..."):
-                qa_chain, history, response_source1, source1_page, response_source2, source2_page, response_source3, source3_page = conversation(st.session_state['qa_chain'], message, history)
+                qa_chain, history, response_answer, response_source1, source1_page, response_source2, source2_page, response_source3, source3_page = conversation(st.session_state['qa_chain'], message, history)
                 st.session_state['qa_chain'] = qa_chain
 
                 st.markdown("### Chatbot Response")
-                st.text_area("Chatbot Response", value=response_source1, height=100)
-                st.text_area("Source 1", value=response_source1, height=100)
-                st.text(f"Page: {source1_page}")
-                st.text_area("Source 2", value=response_source2, height=100)
-                st.text(f"Page: {source2_page}")
-                st.text_area("Source 3", value=response_source3, height=100)
-                st.text(f"Page: {source3_page}")
+                st.write(response_answer)
+
+                with st.expander("Relevant context from the source document"):
+                    st.text_area("Source 1", value=response_source1, height=100)
+                    st.text(f"Page: {source1_page}")
+                    st.text_area("Source 2", value=response_source2, height=100)
+                    st.text(f"Page: {source2_page}")
+                    st.text_area("Source 3", value=response_source3, height=100)
+                    st.text(f"Page: {source3_page}")
 
 if __name__ == "__main__":
     main()