changed CV to cross validation and increased font size in first page

streamlit_hf/home.py

@@ -91,7 +91,7 @@ html, body {{
   margin: 0;
   padding: 0;
   background: transparent;
-  overflow: visible;
+  overflow: hidden;
   box-sizing: border-box;
 }}
 .ff-experiment-svg-wrap {{ width: {width_px}px; max-width: 100%; overflow: visible; }}
@@ -399,11 +399,11 @@ st.markdown(
 )
 
 with st.container(border=True):
-    # Wider text column → fewer wrapped lines; tighter gap; center figure vs text when heights differ.
+    # Slightly narrower text column; keep balanced spacing and centered vertical alignment.
     try:
-        fig_col, text_col = st.columns([0.33, 0.67], gap="medium", vertical_alignment="center")
+        fig_col, text_col = st.columns([0.38, 0.62], gap="medium", vertical_alignment="center")
     except TypeError:
-        fig_col, text_col = st.columns([0.33, 0.67], gap="medium")
+        fig_col, text_col = st.columns([0.38, 0.62], gap="medium")
     with fig_col:
         if _EXPERIMENT_SVG.is_file():
             _render_experiment_schematic(_EXPERIMENT_FIGURE_WIDTH_PX)

streamlit_hf/lib/formatters.py

@@ -35,7 +35,7 @@ LATENT_TABLE_RENAME: dict[str, str] = {
     "modality_label": "Available modalities",
     "dataset_idx": "Dataset index",
     "batch_no": "Batch",
-    "fold": "CV fold",
+    "fold": "Cross Validation fold",
     "clone_id": "Clone ID",
     "clone_size": "Clone size",
     "cell_type": "Cell type",

streamlit_hf/lib/plots.py

@@ -129,7 +129,7 @@ def latent_scatter(
         "modality_label": "Available modalities",
         "dataset_idx": "Dataset index",
         "batch_no": "Batch",
-        "fold": "CV fold",
+        "fold": "Cross Validation fold",
     }
     labels_map = {c: _disp[c] for c in _disp if c in d.columns}
 

streamlit_hf/lib/ui.py

@@ -178,7 +178,7 @@ section[data-testid="stMain"] .ff-hero .ff-hero-text h1 {
 }
 .ff-hero-sub {
     margin: 0;
-    max-width: 52rem;
+    max-width: 100%;
     font-size: 0.98rem;
     line-height: 1.55;
     color: rgba(226, 232, 240, 0.95);

streamlit_hf/pages/1_Single_Cell_Explorer.py

@@ -27,9 +27,9 @@ _UMAP_EXPLORER_SUBTITLE = "Hover points for details · drag on the plot to selec
 _UMAP_EXPLORER_HELP = f"""
 **What this is:** The same **2‑D UMAP** as on **Home**: validation **single cells** in **FateFormer**’s **latent space** (**context vector token representation**), summarised across **5-fold cross-validation** (**2,110** cells before filters). Here you **choose what to colour** and **filter** the cloud.
 
-**How to read it:** Each point is one cell. **Colour** comes from **Colour by**: e.g. [**CellTag-Multi**]({_CELLTAG_MULTI_ARTICLE_URL}) **label**, **predicted fate**, **prediction correct / wrong**, **CV fold**, **batch**, or **dominant fate %**. **Axes are unitless** (UMAP preserves *local* neighbourhoods only). **Hover** a point for per-cell fields.
+**How to read it:** Each point is one cell. **Colour** comes from **Colour by**: e.g. [**CellTag-Multi**]({_CELLTAG_MULTI_ARTICLE_URL}) **label**, **predicted fate**, **prediction correct / wrong**, **Cross Validation fold**, **batch**, or **dominant fate %**. **Axes are unitless** (UMAP preserves *local* neighbourhoods only). **Hover** a point for per-cell fields.
 
-**Using this page:** Use **Filters** to keep modality combinations, restrict **prediction outcome** (all / correct only / wrong only), choose **CV folds**, and set a **dominant fate %** range. In the plot **toolbar** (top right), pick **Box select** or **Lasso select**, then **drag** on the canvas; the app **reruns** and the **Selected points** table fills with those rows. To inspect **one** cell without a selection, scroll to **Inspect by dataset index**.
+**Using this page:** Use **Filters** to keep modality combinations, restrict **prediction outcome** (all / correct only / wrong only), choose **Cross Validation folds**, and set a **dominant fate %** range. In the plot **toolbar** (top right), pick **Box select** or **Lasso select**, then **drag** on the canvas; the app **reruns** and the **Selected points** table fills with those rows. To inspect **one** cell without a selection, scroll to **Inspect by dataset index**.
 """
 
 st.title("Single-Cell Explorer")
@@ -64,7 +64,7 @@ with left:
             "label": "CellTag-Multi label",
             "predicted_class": "Predicted fate",
             "correct": "Prediction correct",
-            "fold": "CV fold",
+            "fold": "Cross Validation fold",
             "batch_no": "Batch",
             "pct": "Dominant fate %",
         }[x],
@@ -87,7 +87,7 @@ with left:
     )
     folds = sorted(df["fold"].unique())
     fold_pick = st.multiselect(
-        "CV folds",
+        "Cross Validation folds",
         folds,
         default=folds,
         help="Validation cross-validation folds to include (each fold’s held-out cells).",