Expose rejected ASL candidates in intent

signspeak/asl/pipeline.py

@@ -52,6 +52,10 @@ def process_asl_frames(
 def build_intent_input(asl: dict[str, Any], emotion: dict[str, Any]) -> dict[str, Any]:
     glosses = asl.get("gloss_sequence", []) or []
     dominant_emotion = emotion.get("dominant_emotion", "unknown")
+    confidence = float(asl.get("confidence", 0.0) or 0.0)
+    threshold = float(asl.get("confidence_threshold", 0.0) or 0.0)
+    top_prediction = asl.get("top_prediction")
+    top_predictions = asl.get("top_predictions", [])
     return {
         "detected_glosses": glosses,
         "detected_facial_expression": dominant_emotion,
@@ -61,12 +65,26 @@ def build_intent_input(asl: dict[str, Any], emotion: dict[str, Any]) -> dict[str
             "scores": emotion.get("emotion_scores", {}),
         },
         "communication_intent": "derived_from_asl_video",
-        "sign_confidence": float(asl.get("confidence", 0.0) or 0.0),
+        "sign_confidence": confidence,
+        "sign_detection": {
+            "accepted": bool(glosses),
+            "status": asl.get("status", "unknown"),
+            "top_prediction": top_prediction,
+            "confidence": confidence,
+            "confidence_threshold": threshold,
+            "top_predictions": top_predictions,
+            "landmarks_status": asl.get("landmarks_status"),
+            "landmarks_detector": asl.get("landmarks_detector"),
+        },
         "pipeline_stage": "asl_video_to_llama_cpp_intent",
         "diagnostics": {
             "asl_status": asl.get("status", "unknown"),
             "emotion_status": emotion.get("status", "unknown"),
             "frames_used": int(asl.get("frames_used", 0) or 0),
+            "top_prediction": top_prediction,
+            "sign_confidence": confidence,
+            "confidence_threshold": threshold,
+            "accepted": bool(glosses),
         },
     }
 

tests/test_asl_pipeline.py

@@ -26,6 +26,35 @@ def test_build_intent_input_matches_llm_schema():
     assert intent["emotion_profile"]["confidence"] == 0.82
     assert intent["sign_confidence"] == 0.91
     assert intent["diagnostics"]["asl_status"] == "ok"
+    assert intent["sign_detection"]["accepted"] is True
+
+
+def test_build_intent_input_exposes_rejected_top_prediction():
+    asl = {
+        "status": "low_confidence",
+        "gloss_sequence": [],
+        "top_prediction": "talk",
+        "confidence": 0.138,
+        "confidence_threshold": 0.70,
+        "top_predictions": [{"label": "talk", "confidence": 0.138}],
+        "frames_used": 30,
+        "landmarks_status": "ok",
+        "landmarks_detector": "holistic",
+    }
+    emotion = {
+        "status": "ok",
+        "dominant_emotion": "neutral",
+        "intensity": 0.29,
+        "emotion_scores": {"neutral": 0.29},
+    }
+
+    intent = build_intent_input(asl, emotion)
+
+    assert intent["detected_glosses"] == []
+    assert intent["sign_detection"]["accepted"] is False
+    assert intent["sign_detection"]["top_prediction"] == "talk"
+    assert intent["sign_detection"]["confidence_threshold"] == 0.70
+    assert intent["diagnostics"]["top_prediction"] == "talk"
 
 
 def test_process_asl_frames_preserves_detector_diagnostics(monkeypatch):
@@ -61,6 +90,7 @@ def test_process_asl_frames_preserves_detector_diagnostics(monkeypatch):
     assert result["asl"]["confidence_threshold"] == 0.70
     assert result["asl"]["top_predictions"] == [{"label": "talk", "confidence": 0.96}]
     assert result["intent_input"]["detected_glosses"] == ["talk"]
+    assert result["intent_input"]["sign_detection"]["top_prediction"] == "talk"
 
 
 def test_summarize_asl_result_is_stable_for_missing_fields():