feat: unified analysis uç noktası ile desteği için YouTube ve file uploads eklendi

app/routes/analyze.py

@@ -19,13 +19,25 @@ import time
 import uuid
 from collections import defaultdict
 from concurrent.futures import ThreadPoolExecutor
-from typing import Optional, List, Dict, Any, Literal
+from typing import Optional, List, Dict
 from fastapi import (
     APIRouter, File, UploadFile, Form,
     Request, Depends, HTTPException, status,
 )
-from pydantic import BaseModel, Field
 
+from .analyze_schemas import (
+    AnalysisFeatures,
+    AnalysisResult,
+    AnalyzeResponse,
+    AudioInfo,
+    ConfidenceBandModel,
+    FeatureContributionModel,
+    MetaClassifierExplanation,
+    MetaFeatureImportance,
+    ModelVoteModel,
+    VocalAnalysis,
+    XAIExplanation,
+)
 from ..services.youtube_analysis import YouTubeAnalysisService
 from ..services.preview_model import PreviewModel
 from ..services.feature_extractor import (
@@ -93,137 +105,6 @@ meta_classifier_service = MetaClassifierService()
 _analysis_pool = ThreadPoolExecutor(max_workers=3)
 
 
-# ============================================
-# Response Models - Platform Uyumlu
-# ============================================
-
-class VocalAnalysis(BaseModel):
-    """Vocal analysis results for the response."""
-
-    hasVocals: bool = False
-    vocalConfidence: float = 0.0
-    vocalAiScore: float = 0.0
-    pitchStabilityScore: float = 0.0
-    vibratoRegularityScore: float = 0.0
-    formantConsistencyScore: float = 0.0
-    breathPatternScore: float = 0.0
-    vocalTextureScore: float = 0.0
-    pitchMeanHz: float = 0.0
-    pitchStdCents: float = 0.0
-    vibratoRateHz: float = 0.0
-    vibratoExtentCents: float = 0.0
-    indicators: List[str] = Field(default_factory=list)
-
-
-class AnalysisFeatures(BaseModel):
-    """Audio feature analysis results."""
-
-    spectralRegularity: float = 0.5
-    temporalPatterns: float = 0.5
-    harmonicStructure: float = 0.5
-    artificialIndicators: List[str] = Field(default_factory=list)
-
-
-class AudioInfo(BaseModel):
-    """Audio file information."""
-
-    duration: float = 0.0
-    sampleRate: int = 44100
-    bitrate: int = 128
-    format: str = "unknown"
-    channels: int = 2
-
-
-class ConfidenceBandModel(BaseModel):
-    tier: str
-    labelTr: str
-    labelEn: str
-    lowerBound: float
-    upperBound: float
-
-
-class ModelVoteModel(BaseModel):
-    name: str
-    probability: float
-    vote: str
-
-
-class FeatureContributionModel(BaseModel):
-    name: str
-    label: str
-    labelEn: str
-    category: str
-    value: float
-    zScore: float
-    shapValue: float
-    direction: str
-    description: str
-
-
-class XAIExplanation(BaseModel):
-    """Rich explainable analysis payload."""
-
-    probability: float
-    threshold: float
-    baseProbability: float
-    confidenceBand: ConfidenceBandModel
-    modelVotes: List[ModelVoteModel] = Field(default_factory=list)
-    bestModel: str = "XGBoost"
-    topContributions: List[FeatureContributionModel] = Field(default_factory=list)
-    allFeatures: Dict[str, FeatureContributionModel] = Field(default_factory=dict)
-    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."""
-
-    isAIGenerated: bool
-    confidence: float = Field(..., ge=0.03, le=0.97)
-    processingTime: float
-    modelVersion: str
-    decisionSource: str
-    analysisMode: Literal["production", "preview"]
-    source: Dict[str, Any]
-    features: AnalysisFeatures
-    audioInfo: AudioInfo
-    vocalAnalysis: Optional[VocalAnalysis] = None
-    xai: Optional[XAIExplanation] = None
-    towerScores: Dict[str, float] = Field(default_factory=dict)
-    metaClassifier: Optional[MetaClassifierExplanation] = None
-
-
-class AnalyzeResponse(BaseModel):
-    """Response model for analysis endpoint."""
-
-    result: Optional[AnalysisResult] = None
-    warnings: List[str] = Field(default_factory=list)
-    errors: List[str] = Field(default_factory=list)
-
-
 @router.post(
     "/api/analyze",
     response_model=AnalyzeResponse,