Sync from GitHub (preserve manual model files)

StreamlitApp/StreamlitApp.py

@@ -5,9 +5,7 @@ import matplotlib.pyplot as plt
 import matplotlib.patches as mpatches
 import torch
 import plotly.express as px
-import json
 import html as _html
-import streamlit.components.v1 as components
 from sklearn.manifold import TSNE
 
 # modular imports
@@ -52,9 +50,9 @@ def _tooltip_label(label: str, tooltip_text: str) -> None:
 
 def _try_copy_to_clipboard(text: str) -> None:
     """
-    Best-effort clipboard copy.
-    - If `pyperclip` is available, try server-side copy.
-    - Also attempts browser-side copy using JS.
+    Best-effort clipboard copy (server-side only).
+    Avoids streamlit.components.html — iframe/JS can fail on Hugging Face Spaces
+    (TypeError: Failed to fetch dynamically imported module for static/js chunks).
     """
     if pyperclip is not None:
         try:
@@ -62,24 +60,6 @@ def _try_copy_to_clipboard(text: str) -> None:
         except Exception:
             pass
 
-    safe = json.dumps(text)
-    # JS copy (browser-side). Clipboard access may be blocked by browser policies.
-    components.html(
-        f"""
-        <script>
-        (function() {{
-          try {{
-            const t = {safe};
-            if (navigator.clipboard && navigator.clipboard.writeText) {{
-              navigator.clipboard.writeText(t);
-            }}
-          }} catch (e) {{}}
-        }})();
-        </script>
-        """,
-        height=0,
-    )
-
 # APP CONFIG
 st.set_page_config(page_title="AMP Predictor", layout="wide")
 
@@ -161,7 +141,7 @@ model = load_model()
 
 #  PREDICT PAGE
 if page == "Predict":
-    st.header("AMP Prediction")
+    st.header("AMP Predictor")
 
     preset_cols = st.columns(2)
     with preset_cols[0]:
@@ -243,9 +223,9 @@ if page == "Predict":
                         _try_copy_to_clipboard(seq)
                         toast_fn = getattr(st, "toast", None)
                         if toast_fn is not None:
-                            toast_fn("Copied to clipboard")
+                            toast_fn("Copied — or select the sequence above (Ctrl+C)")
                         else:
-                            st.success("Copied to clipboard")
+                            st.success("Copied — or select the sequence above (Ctrl+C)")
                 label = top_candidate.get("Prediction", "")
                 conf_str = format_conf_percent(top_candidate["predicted_confidence"], digits=1)
                 st.write(f"**{label} with {conf_str} confidence**")
@@ -259,7 +239,7 @@ if page == "Predict":
 
 #  ANALYZE PAGE
 elif page == "Analyze":
-    st.header("Sequence Analysis")
+    st.header("Peptide Analyzer")
 
     # show the last saved analyze output if user navigated back
     last_seq = st.session_state.analyze_input
@@ -511,25 +491,27 @@ elif page == "Analyze":
 
 #  OPTIMIZE PAGE
 elif page == "Optimize":
-    st.header("AMP Sequence Optimizer")
+    st.header("Peptide Optimizer")
 
-    # Single entry point: text input retained across navigation
-    seq = st.text_input(
-        "Enter a peptide sequence to optimize:",
-        value=st.session_state.get("optimize_input", ""),
-    )
+    # Form: Enter in the text field submits the form (same as clicking Run Optimization).
+    with st.form("optimize_form", clear_on_submit=False):
+        seq = st.text_input(
+            "Enter a peptide sequence to optimize:",
+            value=st.session_state.get("optimize_input", ""),
+        )
+        submitted = st.form_submit_button("Run Optimization")
 
-    warn_opt = sequence_length_warning(seq)
+    warn_opt = sequence_length_warning(seq) if seq else None
     if warn_opt:
         st.caption(f"Warning: {warn_opt}")
 
-    # Run optimization when user changes input and clicks button
-    if seq and st.button("Run Optimization"):
+    if submitted and seq and str(seq).strip():
+        seq = str(seq).strip()
         st.session_state.optimize_input = seq
         progress = st.progress(0.0, text="Optimizing...")
         with st.spinner("Optimizing sequence..."):
             improved_seq, improved_conf, history = optimize_sequence(seq, model)
-            orig_label, orig_conf = predict_amp(seq, model)
+            _ol, orig_conf = predict_amp(seq, model)
             st.session_state.optimize_output = (seq, orig_conf, improved_seq, improved_conf, history)
         progress.progress(1.0, text="Optimization complete")
         st.success("Optimization finished.")
@@ -587,7 +569,7 @@ elif page == "Optimize":
 
 #  VISUALIZE PEPTIDE PAGE
 elif page == "Visualize Peptide":
-    st.header("Visualize Peptide")
+    st.header("Peptide Visualizer")
     # Tighter legend expanders (summary row + scrollable body)
     st.markdown(
         """
@@ -605,15 +587,11 @@ elif page == "Visualize Peptide":
         """,
         unsafe_allow_html=True,
     )
-    st.caption(
-        "High-impact single-sequence view. **Blue / red / green / gray** match the 3D model, helical wheel, "
-        "and functional residue map."
-    )
 
     st.checkbox("Auto-run when sequence changes", value=False, key="viz_peptide_auto_run")
 
     st.text_input(
-        "Peptide sequence",
+        "Enter a peptide sequence to visualize:",
         key="visualize_peptide_input",
         placeholder="Paste or type a one-letter amino-acid sequence",
     )
@@ -622,7 +600,6 @@ elif page == "Visualize Peptide":
     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 = ""
-        st.info("Enter a sequence above, then click **Run visualization** (or enable **Auto-run**).")
     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"))
@@ -683,7 +660,7 @@ elif page == "Visualize Peptide":
 
 #  VISUALIZE t-SNE PAGE
 elif page == "Visualize t-SNE":
-    st.header("Visualize t-SNE")
+    st.header("t-SNE Visualizer")
     st.write("Upload peptide sequences (FASTA or plain list) to embed sequences and explore clusters with t-SNE.")
 
     uploaded_file = st.file_uploader("Upload FASTA or text file", type=["txt", "fasta"])