Create streamlit_app.py

streamlit_app.py

@@ -0,0 +1,58 @@
+# 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.title("💬 Conversational Chat - Data Management & Personal Data Protection")
+
+    # Paths and constants
+    local_file = "PoliciesEn001.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": "👋 Hello! Ask me anything about Data Management & Personal Data Protection!"}
+        ]
+
+    # Display existing messages
+    for msg in st.session_state["messages"]:
+        with st.chat_message(msg["role"]):
+            st.markdown(msg["content"])
+
+    # Step 5: Chat Input
+    user_input = st.chat_input("Type your question...")
+
+    # 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(user_input)
+
+        # 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(answer)
+
+if __name__ == "__main__":
+    main()