app.py

import os
import streamlit as st
import model as demo_chat
import request as re
from transformers import AutoModelForCausalLM, AutoTokenizer

st.title("Hi, I am Chatbot Philio :woman:")
st.write("I am your hotel booking assistant. Feel free to start chatting with me.")

scrollable_div_style = """
<style>
.scrollable-div {
    height: 200px;  /* Adjust the height as needed */
    overflow-y: auto;  /* Enable vertical scrolling */
    padding: 5px;
    border: 1px solid #ccc;  /* Optional: adds a border around the div */
    border-radius: 5px;  /* Optional: rounds the corners of the border */
}
</style>
"""

#llm_chain = demo_chat.chain()

def render_chat_history(chat_history):
    #renders chat history
    for message in chat_history:
        if(message["role"]!= "system"):
            with st.chat_message(message["role"]):
                st.markdown(message["content"])


#Check if chat history exists in this session
if 'chat_history' not in st.session_state:
    st.session_state.chat_history = [] #Initialize chat history
    
st.markdown('<div class="scrollable-div">', unsafe_allow_html=True) #add css style to container
render_chat_history(st.session_state.chat_history)

#Input field for chat interface
if input_text := st.chat_input(placeholder="Here you can chat with our hotel booking model."):
    
    with st.chat_message("user"):
        st.markdown(input_text)
    st.session_state.chat_history.append({"role" : "human", "content" : input_text}) #append message to chat history

    with st.spinner("Generating response..."):
        #first_answer = llm_chain.predict(input = input_text)
        #answer = first_answer.strip()
        prompt = demo_chat.chat_template_prompt()
        input = prompt + input_text + "Assistant:"
        answer = re.generate_response(input)
        
        with st.chat_message("assistant"):
            st.markdown(answer)
        st.session_state.chat_history.append({"role": "ai", "content": answer})    
st.markdown('</div>', unsafe_allow_html=True)