Fix Urdu analysis reliability

Retry incomplete model JSON with a larger Urdu token budget, prevent trace failures from hiding successful assessments, show localized backend errors, and preserve LTR input direction for English messages in Urdu mode.

Co-authored-by: Codex <codex@openai.com>

app.py

@@ -356,7 +356,15 @@ def call_model(
                     {"role": "user", "content": content},
                 ],
                 temperature=0,
-                max_tokens=500 if image_data_url else 350,
+                max_tokens=(
+                    700
+                    if output_language == "ur" and image_data_url
+                    else 550
+                    if output_language == "ur"
+                    else 500
+                    if image_data_url
+                    else 350
+                ),
                 response_format={
                     "type": "json_schema",
                     "json_schema": {
@@ -391,6 +399,10 @@ def call_model(
             if attempt == retries:
                 raise
             time.sleep(retry_delay)
+        except ValueError:
+            if attempt == retries:
+                raise
+            time.sleep(retry_delay)
 
     raise RuntimeError("Model request ended without a response.")
 
@@ -415,13 +427,16 @@ def analyze_notice(
     ) -> dict[str, Any]:
         telemetry = telemetry or {}
         if save_trace:
-            trace_id, queued = queue_trace(
-                text=text,
-                image_data_url=image_data_url,
-                example_id=example_id,
-                assessment=response.get("assessment"),
-            )
-            response["trace"] = {"trace_id": trace_id, "status": queued}
+            try:
+                trace_id, queued = queue_trace(
+                    text=text,
+                    image_data_url=image_data_url,
+                    example_id=example_id,
+                    assessment=response.get("assessment"),
+                )
+                response["trace"] = {"trace_id": trace_id, "status": queued}
+            except Exception:
+                response["trace"] = {"trace_id": "", "status": "failed"}
         else:
             response["trace"] = {"trace_id": "", "status": "disabled"}
         return response
@@ -456,6 +471,7 @@ def analyze_notice(
                     "MODAL_PROXY_SECRET. Add them as environment variables or "
                     "Hugging Face Space secrets."
                 ),
+                "error_code": "modelCredentialsError",
                 "status": status,
             },
         )
@@ -481,16 +497,25 @@ def analyze_notice(
             if exc.status_code in {401, 403}
             else f"The Modal model returned HTTP {exc.status_code}. Try again shortly."
         )
+        error_code = (
+            "modelAuthError"
+            if exc.status_code in {401, 403}
+            else "modelServiceError"
+        )
     except APITimeoutError:
         message = "The Modal model is unavailable or still starting. Try again shortly."
+        error_code = "modelUnavailableError"
     except APIConnectionError:
         message = "The Modal model is unavailable or still starting. Try again shortly."
+        error_code = "modelUnavailableError"
     except (ValueError, RuntimeError):
         message = "The model returned an invalid response. Please try again."
+        error_code = "modelInvalidError"
     return finish(
         {
             "ok": False,
             "error": message,
+            "error_code": error_code,
             "status": {**status, "connected": False, "label": "Modal model unavailable"},
         },
         telemetry=telemetry,

static/app.js

@@ -84,6 +84,11 @@ const translations = {
     requestFailedError: "The request could not be completed.",
     noResultError: "The app returned no result.",
     analyzeError: "Unable to analyze this input.",
+    modelCredentialsError: "Modal credentials are required.",
+    modelAuthError: "The model rejected the configured credentials.",
+    modelServiceError: "The model service returned an error. Please try again.",
+    modelUnavailableError: "The model is unavailable or still starting. Please try again.",
+    modelInvalidError: "The model returned an incomplete response. Please try again.",
     imageTypeError: "Use a PNG, JPG, or WebP image.",
     imageSizeError: "Please choose an image smaller than 8 MB.",
     exampleImageError: "Could not load the example image.",
@@ -159,6 +164,11 @@ const translations = {
     requestFailedError: "درخواست مکمل نہیں ہو سکی۔",
     noResultError: "کوئی نتیجہ موصول نہیں ہوا۔",
     analyzeError: "اس مواد کی جانچ نہیں ہو سکی۔",
+    modelCredentialsError: "ماڈل تک رسائی کے لیے لاگ اِن معلومات درکار ہیں۔",
+    modelAuthError: "ماڈل نے موجودہ لاگ اِن معلومات قبول نہیں کیں۔",
+    modelServiceError: "ماڈل سروس میں خرابی آئی ہے۔ براہ کرم دوبارہ کوشش کریں۔",
+    modelUnavailableError: "ماڈل دستیاب نہیں یا ابھی شروع ہو رہا ہے۔ براہ کرم دوبارہ کوشش کریں۔",
+    modelInvalidError: "ماڈل کا جواب مکمل نہیں تھا۔ براہ کرم دوبارہ کوشش کریں۔",
     imageTypeError: "PNG، JPG یا WebP تصویر استعمال کریں۔",
     imageSizeError: "براہ کرم 8 MB سے چھوٹی تصویر منتخب کریں۔",
     exampleImageError: "مثالی تصویر لوڈ نہیں ہو سکی۔",
@@ -317,7 +327,12 @@ function renderList(selector, items) {
 }
 
 function renderResult(payload) {
-  if (!payload.ok) throw new Error(t("analyzeError"));
+  if (!payload.ok) {
+    const localizedError = payload.error_code
+      ? translations[currentLanguage][payload.error_code]
+      : "";
+    throw new Error(localizedError || payload.error || t("analyzeError"));
+  }
   const result = payload.assessment;
   setStatus(payload.status);
   elements.risk.className = `risk-badge risk-${result.risk_label.toLowerCase().replaceAll(" ", "-")}`;

static/index.html

@@ -72,7 +72,7 @@
 
           <div class="field-card">
             <label class="field-label" for="noticeText"><span>2</span><span class="field-label-text" data-i18n="pasteLabel">Or paste the message</span></label>
-            <textarea id="noticeText" maxlength="12000" placeholder="Paste the SMS, email, bill text, or notice here..." data-i18n-placeholder="textPlaceholder"></textarea>
+            <textarea id="noticeText" dir="auto" maxlength="12000" placeholder="Paste the SMS, email, bill text, or notice here..." data-i18n-placeholder="textPlaceholder"></textarea>
             <div class="field-meta"><span data-i18n="languageSupport">English, Urdu, and Roman Urdu supported by compatible models</span><span id="charCount">0 / 12,000</span></div>
             <div id="textHint" class="mode-hint"><span class="hint-icon">✎</span><span data-i18n="textMode">Text mode active — image upload is locked</span></div>
           </div>

static/styles.css

@@ -328,12 +328,15 @@ html[lang="ur"] .drop-zone small {
 html[lang="ur"] textarea {
   min-height: 250px;
   padding: 19px 20px 26px;
-  direction: rtl;
-  text-align: right;
+  text-align: start;
   font-size: 17px;
   line-height: 2.1;
 }
 html[lang="ur"] textarea::placeholder { line-height: 2.1; }
+html[lang="ur"] #charCount {
+  direction: ltr;
+  unicode-bidi: isolate;
+}
 html[lang="ur"] .field-meta {
   align-items: baseline;
   margin-top: 4px;

tests/test_tracing.py

@@ -298,6 +298,7 @@ class TraceTests(unittest.TestCase):
             result = app.analyze_notice("test message")
         self.assertFalse(result["ok"])
         model_mock.assert_not_called()
+        self.assertEqual(result["error_code"], "modelCredentialsError")
         self.assertNotIn("modal_called", queue_mock.call_args.kwargs)
 
     def test_success_uses_existing_model_call_once(self) -> None:
@@ -334,6 +335,27 @@ class TraceTests(unittest.TestCase):
         self.assertNotIn("modal_called", queue_mock.call_args.kwargs)
         self.assertNotIn("retry_count", queue_mock.call_args.kwargs)
 
+    def test_trace_failure_does_not_hide_successful_assessment(self) -> None:
+        assessment = {
+            "risk_label": "Verify first",
+            "simple_explanation": "Check independently.",
+            "red_flags": ["Unverified sender"],
+            "safe_next_steps": ["Use an official channel."],
+            "reply_draft": "Please confirm through an official channel.",
+        }
+        with patch(
+            "app.model_status",
+            return_value={"connected": True, "label": "ready"},
+        ), patch("app.call_model", return_value=assessment), patch(
+            "app.queue_trace",
+            side_effect=RuntimeError("trace publisher failed"),
+        ):
+            result = app.analyze_notice("test message", save_trace=True)
+
+        self.assertTrue(result["ok"])
+        self.assertEqual(result["assessment"], assessment)
+        self.assertEqual(result["trace"]["status"], "failed")
+
     def test_timeout_is_sanitized(self) -> None:
         timeout = APITimeoutError(request=httpx.Request("POST", "https://example.invalid"))
         with patch(
@@ -345,6 +367,7 @@ class TraceTests(unittest.TestCase):
         ) as queue_mock:
             result = app.analyze_notice("test message")
         self.assertFalse(result["ok"])
+        self.assertEqual(result["error_code"], "modelUnavailableError")
         self.assertNotIn("failure_category", queue_mock.call_args.kwargs)
 
     def test_http_failure_is_sanitized(self) -> None:
@@ -363,6 +386,7 @@ class TraceTests(unittest.TestCase):
         ) as queue_mock:
             result = app.analyze_notice("test message")
         self.assertFalse(result["ok"])
+        self.assertEqual(result["error_code"], "modelServiceError")
         self.assertNotIn("private", json.dumps(queue_mock.call_args.kwargs))
 
     def test_malformed_output_is_sanitized(self) -> None:
@@ -375,6 +399,7 @@ class TraceTests(unittest.TestCase):
         ) as queue_mock:
             result = app.analyze_notice("test message")
         self.assertFalse(result["ok"])
+        self.assertEqual(result["error_code"], "modelInvalidError")
         self.assertNotIn("PRIVATE RAW OUTPUT", json.dumps(queue_mock.call_args.kwargs))
 
     def test_normalization_failure_uses_normalize_stage(self) -> None:
@@ -426,6 +451,50 @@ class TraceTests(unittest.TestCase):
         self.assertEqual(completions.calls, 2)
         self.assertEqual(telemetry["retry_count"], 1)
 
+    def test_invalid_model_json_is_retried_with_larger_urdu_budget(self) -> None:
+        valid = {
+            "risk_label": "Verify first",
+            "simple_explanation": "آزاد ذریعے سے تصدیق کریں۔",
+            "red_flags": ["بھیجنے والے کی تصدیق نہیں ہوئی۔"],
+            "safe_next_steps": ["سرکاری ذریعے سے رابطہ کریں۔"],
+            "reply_draft": "براہ کرم سرکاری ذریعے سے تصدیق کریں۔",
+        }
+
+        class Completions:
+            def __init__(self):
+                self.calls = 0
+                self.max_tokens: list[int] = []
+
+            def create(self, **kwargs):
+                self.calls += 1
+                self.max_tokens.append(kwargs["max_tokens"])
+                content = "{" if self.calls == 1 else json.dumps(valid)
+                message = type("Message", (), {"content": content})()
+                choice = type("Choice", (), {"message": message})()
+                return type("Completion", (), {"choices": [choice]})()
+
+        completions = Completions()
+        client = type(
+            "Client",
+            (),
+            {"chat": type("Chat", (), {"completions": completions})()},
+        )()
+        telemetry: dict = {}
+        with patch("app.create_model_client", return_value=(client, "model")), patch.dict(
+            "os.environ",
+            {"MODEL_MAX_ATTEMPTS": "2", "MODEL_RETRY_DELAY_SECONDS": "0"},
+        ):
+            result = app.call_model(
+                "test",
+                "",
+                telemetry,
+                output_language="ur",
+            )
+
+        self.assertEqual(result["risk_label"], "Verify first")
+        self.assertEqual(completions.calls, 2)
+        self.assertEqual(completions.max_tokens, [550, 550])
+
     def test_publisher_persists_batch(self) -> None:
         publisher = trace_runtime.TracePublisher()
         with tempfile.TemporaryDirectory() as directory, patch.object(