Update app.py

app.py

@@ -4,7 +4,6 @@ This script uses the LangChain Language Model API to answer questions using Retr
 and FAISS vector stores. It also uses the Mistral huggingface inference endpoint to 
 generate responses.
 """
-
 import os
 import streamlit as st
 from dotenv import load_dotenv
@@ -15,22 +14,9 @@ from langchain.vectorstores import FAISS
 from langchain.chat_models import ChatOpenAI
 from langchain.memory import ConversationBufferMemory
 from langchain.chains import ConversationalRetrievalChain
-from langchain.schema import BaseOutputParser, OutputParserException
 from htmlTemplates import css, bot_template, user_template
 from langchain.llms import HuggingFaceHub
 
-class ReferenceOutputParser(BaseOutputParser[ChatGeneration]):
-    def parse(self, text: str) -> ChatGeneration:
-        try:
-            result, references = text.split("References:")
-            return ChatGeneration(
-                result=result.strip(),
-                extra_info={"references": [ref.strip() for ref in references.split("\n") if ref.strip()]}
-            )
-        except ValueError:
-            raise OutputParserException(f"Could not parse output: {text}")
-    
-
 def get_pdf_text(pdf_docs):
     text = ""
     for pdf in pdf_docs:
@@ -79,7 +65,7 @@ def get_conversation_chain(vectorstore):
         )
         memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)
         conversation_chain = ConversationalRetrievalChain.from_llm(
-            llm=llm, retriever=vectorstore.as_retriever(), memory=memory, output_parser=ReferenceOutputParser()
+            llm=llm, retriever=vectorstore.as_retriever(), memory=memory
         )
     except Exception as e:
         st.error(f"Error creating conversation chain: {e}")
@@ -95,14 +81,11 @@ def handle_userinput(user_question):
         response = st.session_state.conversation({"question": user_question})
         st.session_state.chat_history = response["chat_history"]
 
-        result = response.result
-        references = response.extra_info["references"]
-
-        st.write("//_^ User: " + user_question)
-        st.write("🤖 ChatBot: " + result)
-        st.write("References:")
-        for ref in references:
-            st.write("- " + ref)
+        for i, message in enumerate(st.session_state.chat_history):
+            if i % 2 == 0:
+                st.write("//_^ User: " + message.content)
+            else:
+                st.write("🤖 ChatBot: " + message.content)
     except Exception as e:
         st.error(f"Error handling user input: {e}")