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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("π£οΈ Chat with the AI about the ingested documents! π") | |
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() |