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app.py

@@ -59,25 +59,41 @@ def _counter(text: str) -> str:
     )
 
 
+def _normalize(text: str | None, num_candidates: int | None, structured_fallback: bool | None) -> tuple[str, int, bool]:
+    text = (text or "").strip()
+    if num_candidates is None:
+        num_candidates = 3
+    if structured_fallback is None:
+        structured_fallback = True
+    return text, max(1, min(3, int(num_candidates))), bool(structured_fallback)
+
+
 def _estimate_duration(text, num_candidates=3, structured_fallback=True, *args, **kwargs):
     if structured_fallback and infer_kind((text or "").strip()) in ("html", "data"):
         return 10
-    return min(30, max(12, 8 + len(text or "") // 200 + 4 * (int(num_candidates) - 1)))
+    try:
+        candidates = int(num_candidates)
+    except (TypeError, ValueError):
+        candidates = 3
+    return min(30, max(12, 8 + len(text or "") // 200 + 4 * (candidates - 1)))
 
 
-def _run_harness(text: str, num_candidates: int, structured_fallback: bool) -> dict:
+def _run_harness(text: str | None, num_candidates: int | None, structured_fallback: bool | None) -> dict:
+    text, num_candidates, structured_fallback = _normalize(text, num_candidates, structured_fallback)
+    if not text:
+        raise ValueError("Paste a document to convert.")
     started = time.perf_counter()
     result = convert_to_markdown(
         model,
         tokenizer,
         text,
-        num_candidates=max(1, min(3, int(num_candidates))),
+        num_candidates=num_candidates,
         do_sample=True,
         temperature=0.3,
         top_p=0.95,
         seed=0,
         use_cache=True,
-        structured_fallback=bool(structured_fallback),
+        structured_fallback=structured_fallback,
         skip_model_for_structured=True,
     )
     result["elapsed"] = time.perf_counter() - started
@@ -85,7 +101,7 @@ def _run_harness(text: str, num_candidates: int, structured_fallback: bool) -> d
 
 
 @spaces.GPU(duration=_estimate_duration)
-def convert(text: str, num_candidates: int = 3, structured_fallback: bool = True) -> tuple[str, str, str]:
+def convert(text: str | None = None, num_candidates: int | None = None, structured_fallback: bool | None = None) -> tuple[str, str, str]:
     """Convert a plain-text document into faithful GitHub-Flavored Markdown.
 
     PASITA samples the document several times, every candidate is post-processed and
@@ -129,7 +145,7 @@ def convert(text: str, num_candidates: int = 3, structured_fallback: bool = True
 
 
 @spaces.GPU(duration=_estimate_duration)
-def gpu_convert(text: str, num_candidates: int = 3, structured_fallback: bool = True) -> dict:
+def gpu_convert(text: str | None = None, num_candidates: int | None = None, structured_fallback: bool | None = None) -> dict:
     """Convert a document and return the full conversion result.
 
     Same pipeline as the UI conversion. Used by the /v1 OpenAI-style API routes.
@@ -623,6 +639,14 @@ footer[aria-label="Gradio footer navigation"] { display: none !important; }
 #p-settings { background: transparent !important; border: 1px solid var(--hair-soft) !important; border-radius: 12px !important; margin-top: 4px; }
 #p-settings > .label-wrap { font-family: var(--font-mono) !important; font-size: 0.68rem !important; letter-spacing: 0.12em; text-transform: uppercase; color: var(--muted) !important; }
 #p-settings > .label-wrap .icon { color: var(--muted-2) !important; }
+#p-settings .info-text { font-size: 0.72rem !important; color: var(--muted-2) !important; }
+
+#p-candidates .wrap { display: inline-flex; gap: 4px; background: var(--panel); border: 1px solid var(--hair); border-radius: 100px; padding: 3px; }
+#p-candidates label { border-radius: 100px; padding: 7px 16px; margin: 0 !important; cursor: pointer; transition: background .2s ease, color .2s ease; }
+#p-candidates label span { font-family: var(--font-mono) !important; font-size: 11px !important; letter-spacing: 0.08em; color: var(--muted) !important; }
+#p-candidates label.selected { background: rgba(255,255,255,0.08) !important; }
+#p-candidates label.selected span { color: var(--warm) !important; }
+#p-candidates input[type="radio"] { accent-color: var(--warm); }
 #p-settings .p-label { margin-bottom: 4px; }
 
 .p-foot {
@@ -672,10 +696,13 @@ with gr.Blocks(title="PASITA: plain text to Markdown") as demo:
                 elem_id="p-text",
             )
             with gr.Accordion("SETTINGS", open=False, elem_id="p-settings"):
-                candidates = gr.Slider(
-                    minimum=1, maximum=3, value=3, step=1,
+                candidates = gr.Radio(
+                    choices=[1, 2, 3],
+                    value=3,
+                    type="value",
                     label="PASITA CANDIDATES",
                     info="More samples improve the reranker. 3 is the measured default.",
+                    elem_id="p-candidates",
                 )
                 structured = gr.Checkbox(
                     value=True,