Create app.py (Streamlit interface)

app.py (Streamlit interface)

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+import streamlit as st
+from pipeline import process_pdf, enrich_metadata, semantic_split, build_vector_db, load_vector_db, get_qa_chain
+import tempfile, os
+
+st.set_page_config(page_title="Bangladesh Law RAG", layout="wide")
+
+st.title("📜 Bangladesh Law Assistant")
+st.markdown("Ask questions about Bangladesh ICT, Labour, Penal Code, and Constitution documents.")
+
+# Upload and process PDF
+with st.sidebar:
+    st.header("📂 Upload Legal PDF")
+    uploaded_file = st.file_uploader("Choose a PDF", type="pdf")
+    if uploaded_file:
+        with tempfile.NamedTemporaryFile(delete=False, suffix=".pdf") as tmp:
+            tmp.write(uploaded_file.read())
+            path = tmp.name
+        st.success("PDF uploaded. Processing...")
+        pages = process_pdf(path)
+        docs = enrich_metadata(pages)
+        chunks = semantic_split(docs)
+        build_vector_db(chunks)
+        st.success("Vector DB updated successfully.")
+
+# Load vector DB
+vectorstore = load_vector_db()
+qa_chain = get_qa_chain(vectorstore)
+
+# Query box
+query = st.text_input("🔎 Enter your legal query:")
+if query:
+    result = qa_chain({"query": query})
+    answer = result["result"]
+    sources = result["source_documents"]
+
+    st.markdown("### 🧠 Answer")
+    st.write(answer)
+
+    st.markdown("### 📚 Sources")
+    for doc in sources:
+        meta = doc.metadata
+        st.markdown(f"- `{meta.get('law_name', 'Unknown')} - {meta.get('section_heading', 'Unknown')}` ({meta.get('source')})")