Update app.py

app.py

@@ -31,21 +31,22 @@ from langchain.vectorstores import FAISS
 from langchain.chains.question_answering import load_qa_chain
 from langchain.chains import RetrievalQA
 from langchain import HuggingFacePipeline
+from langchain.chains import ConversationalRetrievalChain
 
-from helper import pdf_loader,splitDoc,makeEmbeddings,create_flan_t5_base
+from helper import conversational_chat,pdf_loader,splitDoc,makeEmbeddings,create_flan_t5_base
 
+    
 def conversational_chat(chain,query):    
     result = chain({"question": query, 
     "chat_history": st.session_state['history']})
     st.session_state['history'].append((query, result["answer"]))
 
     return result["answer"]
-
     
 
 def ui(): 
     st.title('PDF Question Answer Bot')
-    # hugging_face_key = os.environ["HUGGINGFACE_HUB_TOKEN"]
+    hugging_face_key = os.environ["HUGGINGFACE_HUB_TOKEN"]
     llm = create_flan_t5_base(load_in_8bit=False)
     hf_llm = HuggingFacePipeline(pipeline=llm)
 
@@ -61,20 +62,20 @@ def ui():
         vector_database = makeEmbeddings(pdf_doc)
         #making the retriever of the vector database 
         retriever = vector_database.as_retriever(search_kwargs={"k":4})
-        qa_chain = RetrievalQA.from_chain_type(llm=hf_llm, chain_type="stuff",retriever=retriever)
+        qa_chain  = ConversationalRetrievalChain.from_llm(llm = hf_llm,
+            retriever=vector_database.as_retriever())
 
         # Create an empty container to hold the PDF loader section
         pdf_loader_container = st.empty()
         
         # Check if the PDF file is uploaded or not
         if uploaded_file is not None:
-            print("The file has been uploaded successfully")
+            st.text("The file has been uploaded successfully")
             # Hide the PDF loader interface when the file is uploaded
             pdf_loader_container.empty()
             # Show the chat interface
             show_chat_interface(qa_chain)
 
-
 def show_chat_interface(qa_chain):
     if 'history' not in st.session_state:
         st.session_state['history'] = []