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import os
import time
import streamlit as st
from qa_loader import load_qa_and_create_vectorstore
from rag_chain import generate_response
from dotenv import load_dotenv
# 🔹 Load environment variables
load_dotenv()
# 🔹 Streamlit Page Configuration
st.set_page_config(page_title="Vistula University AI Assistant", layout="centered")
# 🔹 Title and Description
st.title("📚 Vistula University AI Assistant")
st.write("🚀 Ask me anything about Vistula University!")
# 🔹 Retrieve Data (Cached for Performance)
@st.cache_resource
def get_retriever():
return load_qa_and_create_vectorstore()
retriever = get_retriever()
if isinstance(retriever, tuple):
retriever = retriever[0]
# 🔹 Start or Load Chat History
if "chat_history" not in st.session_state:
st.session_state.chat_history = []
# 🔹 Display Chat History
st.write("### 🗂️ Chat History")
for entry in st.session_state.chat_history:
with st.chat_message("user"):
st.write(entry["question"])
with st.chat_message("assistant"):
st.write(entry["answer"])
# 🔹 User Input
query = st.chat_input("Ask your question about Vistula University!")
# 🔹 Process When User Submits a Question
if query:
with st.spinner("🤖 Thinking..."):
response = generate_response(retriever, query)
# 🔹 Add to Chat History
st.session_state.chat_history.append({
"question": query,
"answer": response
})
# 🔹 Display User Question and AI Response
with st.chat_message("user"):
st.write(query)
with st.chat_message("assistant"):
placeholder = st.empty()
current_text = ""
# Typing Effect
for word in response.split():
current_text += word + " "
placeholder.write(current_text)
time.sleep(0.05)
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