feat: analysis jobs timeline scanning inference xai ve corresponding tests eklendi

app/routes/analyze_schemas.py

@@ -126,6 +126,61 @@ class MetaClassifierExplanation(BaseModel):
     topFeatures: List[MetaFeatureImportance] = Field(default_factory=list)
 
 
+class SegmentReason(BaseModel):
+    """One feature that pushed a window's score, from SHAP."""
+
+    name: str
+    label: str
+    labelEn: str
+    direction: Literal["towards_ai", "towards_human"]
+    shapValue: float
+
+
+class TimelineSegment(BaseModel):
+    """One window of the whole-track scan. Only `ok` windows carry a score."""
+
+    index: int
+    start: float
+    end: float
+    state: Literal["ok", "silent", "skipped", "failed"] = "ok"
+    probability: Optional[float] = None
+    isAi: Optional[bool] = None
+    # 0 at the decision threshold, 1 at the far end of the window's side.
+    margin: Optional[float] = None
+    hasVocals: Optional[bool] = None
+    reasons: List[SegmentReason] = Field(default_factory=list)
+
+
+class TimelineSummary(BaseModel):
+    scoredCount: int = 0
+    flaggedCount: int = 0
+    scoredSec: float = 0.0
+    # Share of the scored time whose windows lean AI.
+    aiShare: float = 0.0
+    meanProbability: float = 0.0
+    maxProbability: float = 0.0
+    peakIndex: Optional[int] = None
+
+
+class Timeline(BaseModel):
+    """The track read window by window with the primary classifier.
+
+    Window scores come from a model trained on 60 s clips that were wholly AI
+    or wholly human, so they show where the music reads AI-like rather than
+    proving where an AI was used.
+    """
+
+    # Seconds the scan covered (at most the first six minutes) and the full track length.
+    durationSec: float
+    totalSec: float
+    truncated: bool = False
+    threshold: float
+    # Peak level per slice of the covered part, 0 to 1, for drawing the waveform.
+    peaks: List[float] = Field(default_factory=list)
+    segments: List[TimelineSegment] = Field(default_factory=list)
+    summary: TimelineSummary = Field(default_factory=TimelineSummary)
+
+
 class AnalysisResult(BaseModel):
     """Platform/hooks/analysisTypes.ts uyumlu."""
 
@@ -142,6 +197,8 @@ class AnalysisResult(BaseModel):
     xai: Optional[XAIExplanation] = None
     towerScores: Dict[str, float] = Field(default_factory=dict)
     metaClassifier: Optional[MetaClassifierExplanation] = None
+    # Whole-track scan; absent when the classifier or the decode was unavailable.
+    timeline: Optional[Timeline] = None
     # Per-layer status: which model actually ran and how (e.g. CLAP embedding
     # vs. its spectral heuristic fallback), so the UI can label each score.
     layers: Dict[str, Dict[str, Any]] = Field(default_factory=dict)