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import streamlit as st
from streamlit import session_state as ss
from langchain.memory import ConversationBufferWindowMemory, StreamlitChatMessageHistory
from streamlit_pdf_viewer import pdf_viewer
from utils.qa import chain
def get_answer(query):
response = chain.invoke(query)
return response['result']
def pdf_v():
# Declare variable.
if 'pdf_ref' not in ss:
ss.pdf_ref = None
# Access the uploaded ref via a key.
st.file_uploader("Upload PDF file", type=('pdf'), key='pdf')
if ss.pdf:
ss.pdf_ref = ss.pdf # backup
# Now you can access "pdf_ref" anywhere in your app.
if ss.pdf_ref:
binary_data = ss.pdf_ref.getvalue()
pdf_viewer(input=binary_data, width=700)
memory_storage = StreamlitChatMessageHistory(key="chat_messages")
memory = ConversationBufferWindowMemory(memory_key="chat_history", human_prefix="User", chat_memory=memory_storage, k=3)
for i, msg in enumerate(memory_storage.messages):
name = "user" if i % 2 == 0 else "assistant"
st.chat_message(name).markdown(msg.content)
if user_input := st.chat_input("User Input"):
with st.chat_message("user"):
st.markdown(user_input)
with st.spinner("Generating Response..."):
with st.chat_message("assistant"):
response = get_answer(user_input)
answer = response
st.markdown(answer)