Spaces:
Sleeping
Sleeping
File size: 2,118 Bytes
36c0029 9c95d2e 36c0029 9c95d2e 36c0029 3ba2d0e 36c0029 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 |
import streamlit as st
from streamlit_option_menu import option_menu
from markup import app_intro
import langchain
from query_data import chat_chain
from memory import msgs
def tab1():
st.header("CIMA Chatbot")
col1, col2 = st.columns([1, 2])
with col1:
st.image("image.jpg", use_column_width=True)
with col2:
st.markdown(app_intro(), unsafe_allow_html=True)
metadata_list = []
unique_metadata_list = []
seen = set()
def tab4():
st.header("🗣️ Chat with the AI about the ingested documents! 📚")
for i, msg in enumerate(msgs.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 = chat_chain({"question": user_input})
answer = response['answer']
source_documents = response['source_documents']
for doc in source_documents:
if hasattr(doc, 'metadata'):
metadata = doc.metadata
metadata_list.append(metadata)
for metadata in metadata_list:
metadata_tuple = tuple(metadata.items())
if metadata_tuple not in seen:
unique_metadata_list.append(metadata)
seen.add(metadata_tuple)
st.write(answer)
st.write(unique_metadata_list)
if st.sidebar.button("Clear Chat History"):
msgs.clear()
def main():
st.set_page_config(page_title="CIMA Chat", page_icon=":memo:", layout="wide")
tabs = ["Intro", "Chat"]
with st.sidebar:
current_tab = option_menu("Select a Tab", tabs, menu_icon="cast")
tab_functions = {
"Intro": tab1,
"Chat": tab4,
}
if current_tab in tab_functions:
tab_functions[current_tab]()
if __name__ == "__main__":
main() |