feat: unified analyze uç noktası ve isolated test istemci yapılandırma eklendi

app/routes/analyze.py

@@ -41,6 +41,7 @@ from ..services.clap_detector import CLAPDetectorService
 from ..services.fst_client import FSTClientService
 from ..services.wav2vec2_detector import Wav2Vec2DetectorService
 from ..services.inference_xai import get_xai_service
+from ..services.meta_classifier import MetaClassifierService
 from ..services.logging_config import get_logger
 
 router = APIRouter()
@@ -86,6 +87,7 @@ clap_service = CLAPDetectorService()
 fst_service = FSTClientService()
 wav2vec2_service = Wav2Vec2DetectorService()
 xai_service = get_xai_service()
+meta_classifier_service = MetaClassifierService()
 
 # Thread pool for CPU-bound audio analysis (feature extraction + vocal)
 _analysis_pool = ThreadPoolExecutor(max_workers=3)
@@ -172,6 +174,30 @@ class XAIExplanation(BaseModel):
     featureCount: int = 0
 
 
+class MetaFeatureImportance(BaseModel):
+    """One feature's contribution to the meta-classifier's prediction."""
+
+    feature: str
+    importance: float
+    value: float
+
+
+class MetaClassifierExplanation(BaseModel):
+    """
+    Trained stacking ensemble result — a second, independent opinion
+    (LightGBM over all tower outputs) shown alongside the primary XAI
+    verdict for transparency, not used to override it. Present only when
+    the trained model artifacts are available; absent (not error) otherwise.
+    """
+
+    isAIGenerated: bool
+    confidence: float
+    modelVersion: str
+    towerScores: Dict[str, float] = Field(default_factory=dict)
+    indicators: List[str] = Field(default_factory=list)
+    topFeatures: List[MetaFeatureImportance] = Field(default_factory=list)
+
+
 class AnalysisResult(BaseModel):
     """Platform/hooks/analysisTypes.ts uyumlu."""
 
@@ -187,6 +213,7 @@ class AnalysisResult(BaseModel):
     vocalAnalysis: Optional[VocalAnalysis] = None
     xai: Optional[XAIExplanation] = None
     towerScores: Dict[str, float] = Field(default_factory=dict)
+    metaClassifier: Optional[MetaClassifierExplanation] = None
 
 
 class AnalyzeResponse(BaseModel):
@@ -487,6 +514,34 @@ async def _analyze_file(
         logger.warning(f"[{request_id}] XAI failed: {e}")
         warnings.append("xai_unavailable")
 
+    # ── Step 5c: Meta-classifier (trained stacking ensemble) ────
+    # A second, independent opinion shown for transparency — never
+    # overrides the primary XAI/fusion verdict above.
+    meta_payload: Optional[MetaClassifierExplanation] = None
+    try:
+        meta_result = meta_classifier_service.predict(
+            features, vocals=vocals,
+            wav2vec2=wav2vec2_result, clap=clap_result, fst=fst_result,
+        )
+        if meta_result.model_version == "auris-v2-trained":
+            meta_payload = MetaClassifierExplanation(
+                isAIGenerated=meta_result.is_ai_generated,
+                confidence=meta_result.confidence,
+                modelVersion=meta_result.model_version,
+                towerScores={k: round(float(v), 3) for k, v in meta_result.tower_scores.items()},
+                indicators=meta_result.indicators,
+                topFeatures=[
+                    MetaFeatureImportance(**f) for f in meta_result.top_features
+                ],
+            )
+            logger.info(
+                f"[{request_id}] Meta-classifier: ai={meta_result.is_ai_generated}, "
+                f"confidence={meta_result.confidence}"
+            )
+    except Exception as e:
+        logger.warning(f"[{request_id}] Meta-classifier failed: {e}")
+        warnings.append("meta_classifier_unavailable")
+
     # Build tower scores dict for UI (multi-signal breakdown)
     tower_scores: Dict[str, float] = {
         "local_features": round(
@@ -572,6 +627,7 @@ async def _analyze_file(
         vocalAnalysis=vocal_response,
         xai=xai_payload,
         towerScores=tower_scores,
+        metaClassifier=meta_payload,
     )
 
     return AnalyzeResponse(result=result, warnings=warnings, errors=[])