app.py

import streamlit as st
from streamlit_chat import message as st_message
import pandas as pd
import numpy as np
import datetime
import gspread
import torch
from langchain.text_splitter import RecursiveCharacterTextSplitter


# from langchain.vectorstores import Chroma
from langchain.vectorstores import FAISS
from langchain.embeddings import HuggingFaceInstructEmbeddings


from langchain import HuggingFacePipeline
from langchain.chains import RetrievalQA
from langchain.prompts import PromptTemplate
from langchain.memory import ConversationBufferWindowMemory


from langchain.chains import LLMChain
from langchain.chains import ConversationalRetrievalChain
from langchain.chains.question_answering import load_qa_chain
from langchain.chains.conversational_retrieval.prompts import CONDENSE_QUESTION_PROMPT


st.set_page_config(
    page_title="Hello",
    page_icon="👋",
)

# st.markdown("main page")    


col1, col2, col3 = st.columns([0.2, 0.5, 0.2])
col2.image("https://cdn-icons-png.flaticon.com/512/2040/2040946.png", use_column_width=True )


row2_col1, row2_col2, row2_col3 = st.columns([0.2, 0.5, 0.2])
row2_col2.markdown("<h3 style='text-align: center; color: black;'>Chatbot for Thai Corporate Document Q&A</h3>", unsafe_allow_html=True )




# st.markdown("Please start customizing dataset and downloading the model on the next pages")


st.markdown("<p style='text-align: center; color: black;'>Please start customizing dataset and downloading the model on the next pages</p>", unsafe_allow_html=True)