Update app.py

app.py

@@ -3,17 +3,14 @@ from setfit import SetFitModel
 from huggingface_hub import hf_hub_download
 from evidence import extract_evidence
 
-UNCERTAINTY_THRESHOLD = 0.516
-MARGIN_THRESHOLD = 0.387
-
 MODEL_ID = "DelaliScratchwerk/text-period-setfit"
 
-# thresholds – tweak later with validation
+# ---- thresholds (use your tuned values)
 TOP_K = 3
-UNCERTAINTY_THRESHOLD = 0.42   # if top1 prob below this → "uncertain"
-MARGIN_THRESHOLD = 0.08        # or if (top1 - top2) < this → "uncertain"
+UNCERTAINTY_THRESHOLD = 0.516   # from tune_thresholds.py
+MARGIN_THRESHOLD = 0.387        # from tune_thresholds.py
 
-# Load labels: try from model repo, else local labels.json in the Space
+# ---- load labels (Hub -> local fallback)
 try:
     labels_path = hf_hub_download(MODEL_ID, "labels.json")
     LABELS = json.load(open(labels_path))
@@ -40,20 +37,21 @@ def predict(txt: str):
         return "—", f"Label mismatch: model has {probs.size} classes, labels.json has {len(LABELS)}", {}
 
     order = np.argsort(probs)[::-1]
-    top1, top2 = probs[order[0]], probs[order[1]] if probs.size > 1 else 0.0
+    top1 = probs[order[0]]
+    top2 = probs[order[1]] if probs.size > 1 else 0.0
     ev = extract_evidence(txt)
 
     # uncertain mode
     if top1 < UNCERTAINTY_THRESHOLD or (top1 - top2) < MARGIN_THRESHOLD:
-        topk = [{ "label": LABELS[i], "score": float(probs[i]) } for i in order[:TOP_K]]
-        md = "**Uncertain** — here are the top candidates:\n" + "\n".join(
+        topk = [{"label": LABELS[i], "score": float(probs[i])} for i in order[:TOP_K]]
+        md = "**Uncertain** — top candidates:\n" + "\n".join(
             [f"- **{d['label']}**: {d['score']:.3f}" for d in topk]
         )
         return "uncertain", md + "\n\n" + format_evidence(ev), {LABELS[i]: float(probs[i]) for i in order}
 
     # confident
     best = LABELS[order[0]]
-    md = f"**Reasoning hints**\n\n" + format_evidence(ev)
+    md = "**Reasoning hints**\n\n" + format_evidence(ev)
     return best, md, {LABELS[i]: float(probs[i]) for i in order}
 
 with gr.Blocks(title="Text → Time Period (SetFit)") as demo: