Update app.py

app.py

@@ -40,16 +40,24 @@ newest_date = max(dates)
 
 # streamlit
 st.title("HF Daily Papers Search")
-st.markdown(f"Search papers from [HF daily papers](https://huggingface.co/papers).\n\nNmber of documents: {len(docs)}\n\nfrom {oldest_date.strftime('%Y-%m-%d')} to {newest_date.strftime('%Y-%m-%d')}")
-
-user_query = st.text_input("Search anything...")
-if st.button('→'):
-    results = retriever.invoke(user_query)
-    st.text(f"hit {len(results)} papers")
-
-    for result in results:
-        with st.expander(label=result.metadata['title'], expanded=False):
-            for k in result.metadata:
-                st.write(f"{k}: {result.metadata[k]}")
-            st.divider()
-            st.markdown(result.page_content)
+st.markdown(f"Search papers from [HF daily papers](https://huggingface.co/papers).")
+st.markdown(f"Nmber of documents: `{len(docs)}`.")
+st.markdown(f"From `{oldest_date.strftime('%Y-%m-%d')}` to `{newest_date.strftime('%Y-%m-%d')}`.")
+st.markdown("This app uses BM25, allowing you to search not only with keywords like \"machine learning\" but also with documents like \"How to generate synthetic data using LLM.\"")
+
+col1, col2 = st.columns([4, 1])
+
+with col1:
+    user_query = st.text_input("Search anything...")
+
+with col2:
+    if st.button('→'):
+        results = retriever.invoke(user_query)
+        st.text(f"Top n {len(results)} related papers")
+
+        for result in results:
+            with st.expander(label=result.metadata['title'], expanded=False):
+                for k in result.metadata:
+                    st.write(f"{k}: {result.metadata[k]}")
+                st.divider()
+                st.markdown(result.page_content)