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| from pydantic import BaseModel, Field | |
| from typing import List, Optional, Dict, Any | |
| from datetime import datetime | |
| class PredictionRequest(BaseModel): | |
| image_base64: Optional[str] = Field(None, description="Base64-encoded image") | |
| class SinglePrediction(BaseModel): | |
| filename: str = Field(..., description="Input filename") | |
| prediction: str = Field(..., description="real or ai_generated") | |
| confidence: float = Field(..., ge=0.0, le=1.0) | |
| real_probability: float = Field(0.0, ge=0.0, le=1.0) | |
| ai_probability: float = Field(0.0, ge=0.0, le=1.0) | |
| processing_time_ms: float = 0.0 | |
| anomaly_heatmap: Optional[str] = None | |
| error: Optional[str] = None | |
| frequency_band_contributions: Optional[Dict[str, float]] = None | |
| class PredictionResponse(BaseModel): | |
| prediction: str = Field(..., description="real or ai_generated") | |
| confidence: float = Field(..., ge=0.0, le=1.0) | |
| real_probability: float = Field(..., ge=0.0, le=1.0) | |
| ai_probability: float = Field(..., ge=0.0, le=1.0) | |
| processing_time_ms: float | |
| anomaly_heatmap: Optional[str] = None | |
| frequency_band_contributions: Optional[Dict[str, float]] = None | |
| tier: Optional[Dict[str, Any]] = None | |
| class BatchPredictionResponse(BaseModel): | |
| results: List[SinglePrediction] | |
| summary: Dict[str, Any] | |
| tier: Optional[Dict[str, Any]] = None | |
| class HealthResponse(BaseModel): | |
| status: str | |
| model_loaded: bool | |
| version: str | |
| models_loaded: List[str] = Field(default_factory=list, description="Currently loaded model variants") | |
| timestamp: str | |
| class UsageStats(BaseModel): | |
| tier: str | |
| requests_this_minute: int | |
| rate_limit: int | |
| class ErrorResponse(BaseModel): | |
| detail: str | |
| status_code: int | |