Update app.py

app.py

@@ -107,7 +107,7 @@ with st.sidebar:
         value="sagawa/ReactionT5v2-forward",
         help="Hugging Face Hub repo or local path",
     )
-    st.divider()
+    st.markdown("---")
 
     st.caption("Generation")
     num_beams = st.slider("num_beams", 1, 10, 5, 1)
@@ -334,14 +334,41 @@ if run:
         )
 
 # -----------------------------
-# Footer Note
+# Footer Note (replace this whole block)
 # -----------------------------
 st.markdown(
     """
     <hr/>
-    <small>
-    Built with ❤️ using Streamlit & 🤗 Transformers.
-    </small>
+    <div style="font-size:0.95rem; line-height:1.6">
+      <strong>Citation</strong><br/>
+      Sagawa, T., & Kojima, R. (2025).
+      <em>ReactionT5: a pre-trained transformer model for accurate chemical reaction prediction with limited data</em>.
+      <em>Journal of Cheminformatics</em>, 17(1), 126.
+      <a href="https://doi.org/10.1186/s13321-025-01075-4" target="_blank" rel="noopener">
+        https://doi.org/10.1186/s13321-025-01075-4
+      </a>
+
+      <details style="margin-top: .5rem;">
+        <summary style="cursor: pointer;">Show BibTeX</summary>
+        <pre style="white-space: pre-wrap; font-size:0.9rem; margin-top:.5rem;">
+@article{Sagawa2025,
+  title   = {ReactionT5: a pre-trained transformer model for accurate chemical reaction prediction with limited data},
+  author  = {Sagawa, Tatsuya and Kojima, Ryosuke},
+  journal = {Journal of Cheminformatics},
+  year    = {2025},
+  volume  = {17},
+  number  = {1},
+  pages   = {126},
+  doi     = {10.1186/s13321-025-01075-4},
+  url     = {https://doi.org/10.1186/s13321-025-01075-4}
+}
+        </pre>
+      </details>
+
+      <div style="margin-top:.75rem; color:#666;">
+        Built with Streamlit and Transformers.
+      </div>
+    </div>
     """,
     unsafe_allow_html=True,
 )