Update app.py

app.py

@@ -1,14 +1,14 @@
 import gradio as gr
 import os
+import re
+from pathlib import Path
 
 from langchain_community.document_loaders import PyPDFLoader
 from langchain.text_splitter import RecursiveCharacterTextSplitter
 from langchain_community.vectorstores import Chroma
 from langchain.chains import ConversationalRetrievalChain
-from langchain_community.embeddings import HuggingFaceEmbeddings 
+from langchain_community.embeddings import HuggingFaceEmbeddings
 from langchain_community.llms import HuggingFaceEndpoint
-
-from pathlib import Path
 import chromadb
 from unidecode import unidecode
 
@@ -120,7 +120,7 @@ def conversation(qa_chain, message, history):
     response_sources = response["source_documents"]
     new_history = history + [(message, response_answer)]
     response_details = [(src.page_content.strip(), src.metadata["page"] + 1) for src in response_sources[:3]]
-    return qa_chain, gr.update(value=""), new_history, *sum(response_details, ())
+    return gr.update(value=""), new_history, *sum(response_details, ())
 
 # Gradio Interface
 def demo():
@@ -164,7 +164,7 @@ def demo():
         # Define Interactions
         db_btn.click(initialize_database, inputs=[document, slider_chunk_size, slider_chunk_overlap], outputs=[vector_db, collection_name, db_progress])
         qachain_btn.click(initialize_LLM, inputs=[llm_btn, slider_temperature, slider_maxtokens, slider_topk, vector_db], outputs=[qa_chain, llm_progress])
-        msg.submit(conversation, inputs=[qa_chain, msg, chatbot], outputs=[qa_chain, msg, chatbot] + [None] * 6)
+        msg.submit(conversation, inputs=[qa_chain, msg, chatbot], outputs=[msg, chatbot] + [None] * 6)
 
     demo.launch(debug=True)