Sync from GitHub (preserve manual model files)

StreamlitApp/StreamlitApp.py

@@ -155,7 +155,6 @@ if page == "Predict":
         if long_cnt:
             st.caption(f"Warning: {long_cnt} sequence(s) unusually long (> 50 aa).")
 
-    st.divider()
     run = st.button("Run Prediction")
 
     if run:
@@ -194,14 +193,11 @@ if page == "Predict":
     # If user hasn't just run predictions, show the last saved results (if any)
     if st.session_state.predictions and not (run and st.session_state.predict_ran is False):
         st.divider()
-        st.write("**Predictions (last run)**")
 
         top_candidate = choose_top_candidate(st.session_state.predictions)
         if top_candidate:
             with st.container():
-                st.markdown(
-                    "<div style='border:1px solid #e6e6e6; border-radius:0.6rem; padding:0.8rem; background:#fafafa;'>"
-                , unsafe_allow_html=True)
+
                 st.write("**Top Candidate**")
                 seq = top_candidate.get("Sequence", "")
                 cc = st.columns([9, 1])
@@ -217,7 +213,6 @@ if page == "Predict":
                             st.success("Copied to clipboard")
                 st.write(f"Confidence: **{format_conf_percent(top_candidate['predicted_confidence'], digits=1)}**")
                 st.write(f"Reason: {top_candidate['Reason']}")
-                st.markdown("</div>", unsafe_allow_html=True)
 
         st.divider()
         # Keep the original dataframe for full overview/download compatibility.
@@ -474,15 +469,10 @@ elif page == "Optimize":
         # Optimization Summary box
         summary = optimization_summary(orig_seq, orig_conf, improved_seq, improved_conf)
         delta_str = f"{summary['delta_conf_pct']:+.2f}%"
-        st.markdown(
-            "<div style='border:1px solid #e6e6e6; border-radius:0.6rem; padding:0.8rem; background:#fafafa;'>",
-            unsafe_allow_html=True,
-        )
         st.subheader("Optimization Summary")
         st.write(f"Confidence: **{delta_str}** (final - original)")
         st.write(f"Charge: **{summary['charge_change']}** (orig {summary['charge_orig']}, final {summary['charge_final']})")
         st.write(f"Hydrophobicity: **{summary['hydro_change']}** (orig {summary['hydro_orig']}, final {summary['hydro_final']})")
-        st.markdown("</div>", unsafe_allow_html=True)
 
         st.divider()
         # Mutation Heatmap

StreamlitApp/utils/ui_helpers.py

@@ -198,6 +198,10 @@ def sequence_health_label(conf_prob: float, charge: float, hydro_fraction: float
     """
     Returns: (label, color_css)
     """
+    # If the model is *extremely* confident, treat it as strong regardless
+    # of charge/hydrophobicity heuristics (prevents "moderate" at ~99%).
+    if conf_prob >= 0.9:
+        return "Strong AMP candidate", "#2ca02c"
     if conf_prob > 0.75 and charge >= 2 and 0.3 <= hydro_fraction <= 0.6:
         return "Strong AMP candidate", "#2ca02c"
     if conf_prob > 0.5: