Sync from GitHub (preserve manual model files)

StreamlitApp/StreamlitApp.py

@@ -89,8 +89,6 @@ if "visualize_df" not in st.session_state:
     st.session_state.visualize_df = None
 if "visualize_peptide_input" not in st.session_state:
     st.session_state.visualize_peptide_input = ""
-if "viz_peptide_last_computed" not in st.session_state:
-    st.session_state.viz_peptide_last_computed = ""
 
 # SIDEBAR: navigation + global clear 
 st.sidebar.header("Navigation")
@@ -120,8 +118,6 @@ if st.sidebar.button("Clear All Fields"):
         "visualize_sequences",
         "visualize_df",
         "visualize_peptide_input",
-        "viz_peptide_last_computed",
-        "viz_peptide_auto_run",
     ]
     for k in keys:
         if k in st.session_state:
@@ -361,7 +357,7 @@ elif page == "Analyze":
             "left:50%;"
             "top:125%;"
             "transform:translateX(-50%);"
-            "max-width:560px;"
+            "max-width:860px;"
             "white-space:normal;"
             "padding:8px 10px;"
             "background:rgba(30,30,30,0.95);"
@@ -588,8 +584,6 @@ elif page == "Visualize Peptide":
         unsafe_allow_html=True,
     )
 
-    st.checkbox("Auto-run when sequence changes", value=False, key="viz_peptide_auto_run")
-
     st.text_input(
         "Enter a peptide sequence to visualize:",
         key="visualize_peptide_input",
@@ -598,23 +592,8 @@ elif page == "Visualize Peptide":
 
     seq_viz = (st.session_state.get("visualize_peptide_input") or "").strip()
     clean_viz = "".join(c for c in seq_viz.upper() if not c.isspace())
-    if not clean_viz:
-        st.session_state.viz_peptide_last_computed = ""
-    else:
-        run_viz = st.button("Run visualization", type="primary", key="viz_peptide_run_btn")
-        auto_on = bool(st.session_state.get("viz_peptide_auto_run"))
-        last_c = st.session_state.get("viz_peptide_last_computed", "") or ""
-        need_compute = False
-        if run_viz:
-            need_compute = True
-        elif auto_on and clean_viz != last_c:
-            need_compute = True
-
-        if need_compute:
-            with st.spinner("Building 3D view and helical wheel..."):
-                st.session_state.viz_peptide_last_computed = clean_viz
-
-        if clean_viz == st.session_state.get("viz_peptide_last_computed"):
+    if clean_viz:
+        with st.spinner("Building 3D view and helical wheel..."):
             warn_len = sequence_length_warning(clean_viz)
             if warn_len:
                 st.warning(warn_len)
@@ -655,9 +634,6 @@ elif page == "Visualize Peptide":
             with st.expander("Map · legend", expanded=False):
                 st.markdown(COMPACT_MAP_LEGEND)
 
-        elif clean_viz != (st.session_state.get("viz_peptide_last_computed") or ""):
-            st.caption("Click **Run visualization** or enable **Auto-run** to update the figures.")
-
 #  VISUALIZE t-SNE PAGE
 elif page == "Visualize t-SNE":
     st.header("t-SNE Visualizer")
@@ -760,11 +736,11 @@ elif page == "About":
 PeptideAI is a lightweight Streamlit app for exploring antimicrobial peptide (AMP) sequences.
 
 It uses a trained neural network to estimate whether a peptide is likely to be antimicrobial, then helps you interpret and improve candidates:
-- **Predict**: batch predictions from multi-line or FASTA input, length warnings, persisted results, top-candidate highlight, and CSV export.
-- **Analyze**: single-sequence numerical & textual analysis — AMP prediction, composition, physicochemical table + radar, similarity to known AMPs, and exportable report (no 3D on this page).
-- **Optimize**: guided sequence optimization with mutation heatmap, step table, and confidence vs. step plot.
-- **Visualize Peptide**: one sequence with consistent coloring across functional map, detailed helical wheel, and optional 3D approximation.
-- **Visualize t-SNE**: upload many sequences, embed with the model, run t-SNE, and explore clusters with filters and hover metadata.
+- **AMP Predictor**: batch predictions from multi-line or FASTA input, length warnings, persisted results, top-candidate highlight, and CSV export.
+- **Peptide Analyzer**: single-sequence numerical and textual analysis — AMP prediction, composition, physicochemical table + radar, similarity to known AMPs, and report export.
+- **Peptide Optimizer**: guided sequence optimization with Enter-to-run input, mutation heatmap, step table, and confidence-vs-step trend.
+- **Peptide Visualizer**: single-sequence 3D approximation + detailed helical wheel + functional region map with consistent residue coloring and concise legend dropdowns.
+- **t-SNE Visualizer**: upload many sequences, embed with the model, run t-SNE, and explore clusters with filters and hover metadata.
 - **About**: this overview and disclaimer.
 
 **Disclaimer:** Predictions are model-based heuristics and are **not** a substitute for wet-lab validation or regulatory use.

StreamlitApp/utils/peptide_extras.py

@@ -173,17 +173,27 @@ Residues are placed using a **100° step** per position (common α-helical wheel
 """
 
 # Short blurbs for compact UI expanders (Visualize Peptide page)
-COMPACT_3D_LEGEND: str = (
-    "**Blue** K,R,H · **Red** D,E · **Green** hydrophobic (AVLIMFWY) · **Gray** other. "
-    "Helix trace is an approximation, not an experimental structure."
-)
-COMPACT_WHEEL_LEGEND: str = (
-    "**Radial lines:** each residue around the wheel. **Black segments:** connect residue i → i+1 in sequence order "
-    "(classic helical-wheel “star” pattern). **Colors** match 3D: **blue** K,R,H · **red** D,E · **green** hydrophobic · **gray** other."
-)
-COMPACT_MAP_LEGEND: str = (
-    "Same scheme as 3D and wheel: charged vs hydrophobic distribution along the sequence."
-)
+COMPACT_3D_LEGEND: str = """
+**How to read this 3D view**
+- **Blue:** positively charged residues (K, R, H)
+- **Red:** negatively charged residues (D, E)
+- **Green:** hydrophobic residues (A, V, I, L, M, F, W, Y)
+- **Gray:** other / polar residues
+- Backbone is a **helix-like approximation**, not an experimental structure.
+"""
+COMPACT_WHEEL_LEGEND: str = """
+**How to read this helical wheel**
+- **Radial spokes:** residue positions around the helix (100 degrees per residue)
+- **Black connectors:** sequence order (`i -> i+1`) across the wheel
+- **Colored circles:** residue chemistry classes
+- Color mapping matches the 3D view (**blue / red / green / gray**)
+"""
+COMPACT_MAP_LEGEND: str = """
+**How to read this sequence map**
+- Uses the same residue color mapping as 3D and helical wheel
+- Highlights where charged vs hydrophobic residues cluster along the sequence
+- Useful for quick amphipathic pattern checks
+"""
 
 
 def plot_helical_wheel(sequence: str, figsize: Tuple[float, float] = (6.2, 6.2)) -> Any: