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# app.py
import streamlit as st
import os
# Local imports
from embedding import load_embeddings
from vectorstore import load_or_build_vectorstore
from chain_setup import build_conversational_chain
def main():
st.set_page_config(page_title="المحادثة التفاعلية", layout="wide")
st.title("💬 المحادثة التفاعلية - ادارة البيانات وحماية البيانات الشخصية")
# Add custom CSS to support right-to-left text
st.markdown(
"""
<style>
.rtl {
direction: rtl;
text-align: right;
}
</style>
""",
unsafe_allow_html=True,
)
# Paths and constants
local_file = "Policies001.pdf"
index_folder = "faiss_index"
# Step 1: Load Embeddings
embeddings = load_embeddings()
# Step 2: Build or load VectorStore
vectorstore = load_or_build_vectorstore(local_file, index_folder, embeddings)
# Step 3: Build the Conversational Retrieval Chain
qa_chain = build_conversational_chain(vectorstore)
# Step 4: Session State for UI Chat
if "messages" not in st.session_state:
st.session_state["messages"] = [
{"role": "assistant", "content": "👋 مرحبًا! اسألني أي شيء عن إدارة البيانات وحماية البيانات الشخصية"}
]
# Display existing messages
for msg in st.session_state["messages"]:
with st.chat_message(msg["role"]):
# Apply 'rtl' class for RTL text direction
st.markdown(f'<div class="rtl">{msg["content"]}</div>', unsafe_allow_html=True)
# Step 5: Chat Input
user_input = st.chat_input("اكتب سؤالك")
# Step 6: Process user input
if user_input:
# a) Display user message
st.session_state["messages"].append({"role": "user", "content": user_input})
with st.chat_message("user"):
st.markdown(f'<div class="rtl">{user_input}</div>', unsafe_allow_html=True)
# b) Run chain
response_dict = qa_chain({"question": user_input})
answer = response_dict["answer"]
# c) Display assistant response
st.session_state["messages"].append({"role": "assistant", "content": answer})
with st.chat_message("assistant"):
st.markdown(f'<div class="rtl">{answer}</div>', unsafe_allow_html=True)
if __name__ == "__main__":
main() |