user.py

from utils.qa import chain
import streamlit as st
from langchain.memory import ConversationBufferWindowMemory
from langchain_community.chat_message_histories import StreamlitChatMessageHistory

path = "mm_vdb2"
client = chromadb.PersistentClient(path=path)
image_collection = client.get_collection(name="image")
video_collection = client.get_collection(name='video_collection')


memory_storage = StreamlitChatMessageHistory(key="chat_messages")
memory = ConversationBufferWindowMemory(memory_key="chat_history", human_prefix="User", chat_memory=memory_storage, k=3)

def get_answer(query):
    response = chain.invoke(query)
    #return response["result"]
    return response

def home():
    st.header("Welcome")
    #st.set_page_config(layout='wide', page_title="Virtual Tutor")
    st.markdown("""
        <svg width="600" height="100">
            <text x="50%" y="50%" font-family="San serif" font-size="42px" fill="Black" text-anchor="middle" stroke="white"
             stroke-width="0.3" stroke-linejoin="round">Virtual Tutor - CHAT
            </text>
        </svg>
    """, unsafe_allow_html=True)

    if "messages" not in st.session_state:
        st.session_state.messages = [
            {"role": "assistant", "content": "Hi! How may I assist you today?"}
        ]

    st.markdown("""
        <style> 
        .stChatInputContainer > div {
        background-color: #000000;
        }
        </style>
        """, unsafe_allow_html=True)

    for message in st.session_state.messages: # Display the prior chat messages
        with st.chat_message(message["role"]):
            st.write(message["content"])

    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['result']
                st.markdown(answer)
                message = {"role": "assistant", "content": answer}
                message_u = {"role": "user", "content": user_input}
                st.session_state.messages.append(message_u)
                st.session_state.messages.append(message)