Upload folder using huggingface_hub

model.py

@@ -71,7 +71,8 @@ DEMO = {
         '    .single("safety", ["safe", "unsafe"])\n'
         '    .multi("harm_category", [\n'
         '        "prompt_injection", "violence_and_weapons", "sexual_content",\n'
-        '        "hate_and_discrimination", "privacy_violation", "misinformation"]))\n'
+        '        "hate_and_discrimination", "privacy_violation", "misinformation"],\n'
+        '        threshold=0.5))\n'
         'joint = separate.constrain(\n'
         '    C.implies(C.any_selected("harm_category"), ("safety", "unsafe")),\n'
         '    C.implies(("safety", "unsafe"), C.any_selected("harm_category")))\n'
@@ -236,7 +237,7 @@ def _schema_separate():
     from gliner2.classification import ClassificationSchema
     return (ClassificationSchema()
             .single("safety", SAFETY_LABELS)
-            .multi("harm_category", HARM_LABELS))
+            .multi("harm_category", HARM_LABELS, threshold=0.5))
 
 
 def _schema_joint(implies, mins):
@@ -249,7 +250,7 @@ def _schema_joint(implies, mins):
     min_by_task = dict(mins)
     s = (ClassificationSchema()
          .single("safety", SAFETY_LABELS)
-         .multi("harm_category", HARM_LABELS,
+         .multi("harm_category", HARM_LABELS, threshold=0.5,
                 min_labels=min_by_task.get("harm_category", 0))
          .constrain(*[C.implies(expr(*cond), expr(*cons)) for cond, cons in implies]))
     if min_by_task.get("safety", 0) > 0:  # "at least one safety label" is vacuous (single task)

static/index.html

@@ -120,6 +120,9 @@
     width: 158px; flex-shrink: 0;
     font-family: var(--font-mono); font-size: 12px; font-weight: 400; color: var(--ink-faint);
   }
+  /* above the 0.5 threshold: purple, selected or not */
+  .l-row.hot .l-name { font-weight: 600; color: var(--purple); }
+  .l-row.hot .l-bar i { background: var(--purple); }
   .l-row.chosen .l-name { font-weight: 700; color: var(--span-color); }
   .l-score { width: 34px; flex-shrink: 0; text-align: right; font-family: var(--font-mono); font-size: 11px; color: var(--ink-faint); }
   .l-row.chosen .l-score { color: var(--ink-soft); }
@@ -323,10 +326,12 @@ $("add-rule").addEventListener("click", () => {
   inferSoon(120);
 });
 
-/* semantic colors: safe = green, unsafe = red; harm labels keep the task hue */
-function labelColor(task, label) {
+/* semantic colors: safe = green, unsafe = red; harm labels purple when they
+   clear the 0.5 selection threshold (chosen or not), gray below it */
+const HARM_THRESHOLD = 0.5;
+function labelColor(task, label, p) {
   if (task === "safety") return label === "safe" ? "var(--ok)" : "var(--neg)";
-  return "var(--c1)";
+  return p >= HARM_THRESHOLD ? "var(--purple)" : "var(--warm-gray)";
 }
 
 /* one task section: every label gets its own row, name left, score+bar right,
@@ -337,8 +342,9 @@ function taskSection(task, probs, chosen, opts = {}) {
   const rows = order.map((l) => {
     const p = (probs || {})[l] || 0;
     const on = chosenSet.has(l);
-    const color = labelColor(task, l);
-    return `<div class="l-row${on ? " chosen" : ""}" style="--span-color:${color}">
+    const hot = task === "harm_category" && p >= HARM_THRESHOLD;
+    const color = labelColor(task, l, p);
+    return `<div class="l-row${on ? " chosen" : ""}${hot ? " hot" : ""}" style="--span-color:${color}">
       <span class="l-name">${esc(l)}</span>
       <span class="l-score">${p.toFixed(2)}</span>
       <span class="l-bar"><i data-w="${(p * 100).toFixed(1)}"></i></span>