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| """ | |
| Pydantic schemas for prediction endpoints. | |
| """ | |
| from typing import Dict, List, Literal, Optional | |
| from pydantic import BaseModel, Field | |
| # Model display info schema (for frontend) | |
| class ModelDisplayInfo(BaseModel): | |
| """User-friendly display information for a model.""" | |
| display_name: str = Field(..., description="User-friendly model name (e.g., 'Texture Analysis')") | |
| short_name: str = Field(..., description="Short identifier (e.g., 'CNN')") | |
| method_name: str = Field(..., description="Explainability method name (e.g., 'Grad-CAM')") | |
| method_description: str = Field(..., description="Brief description of the method") | |
| educational_text: str = Field(..., description="Educational text about what this model analyzes") | |
| what_it_looks_for: List[str] = Field(..., description="List of things this model looks for") | |
| # LLM single-model explanation schema (for on-demand requests) | |
| class SingleModelInsight(BaseModel): | |
| """LLM-generated insight for a single model (on-demand).""" | |
| key_finding: str = Field(..., description="Main finding from the model") | |
| what_model_saw: str = Field(..., description="What the model detected in the image") | |
| important_regions: List[str] = Field(..., description="Key regions identified") | |
| confidence_qualifier: str = Field(..., description="Confidence assessment with hedging") | |
| class PredictionResult(BaseModel): | |
| """Single prediction result from a model.""" | |
| pred: Literal["real", "fake"] = Field( | |
| ..., | |
| description="Human-readable prediction label" | |
| ) | |
| pred_int: Literal[0, 1] = Field( | |
| ..., | |
| description="Integer prediction: 0=real, 1=fake" | |
| ) | |
| prob_fake: float = Field( | |
| ..., | |
| ge=0.0, | |
| le=1.0, | |
| description="Probability that the image is fake (0.0-1.0)" | |
| ) | |
| heatmap_base64: Optional[str] = Field( | |
| None, | |
| description="Base64-encoded PNG heatmap showing model attention/saliency (when explain=true)" | |
| ) | |
| explainability_type: Optional[Literal["grad_cam", "attention_rollout"]] = Field( | |
| None, | |
| description="Type of explainability method used" | |
| ) | |
| focus_summary: Optional[str] = Field( | |
| None, | |
| description="Brief description of where the model focused (e.g., 'concentrated on face region')" | |
| ) | |
| contribution_percentage: Optional[float] = Field( | |
| None, | |
| ge=0.0, | |
| le=100.0, | |
| description="How much this model contributed to the fusion decision (0-100%)" | |
| ) | |
| class FusionMeta(BaseModel): | |
| """Metadata from fusion model about how decision was made.""" | |
| submodel_weights: Dict[str, float] = Field( | |
| default_factory=dict, | |
| description="Learned coefficients for each submodel" | |
| ) | |
| weighted_contributions: Dict[str, float] = Field( | |
| default_factory=dict, | |
| description="Actual contribution to this prediction (weight * prob_fake)" | |
| ) | |
| contribution_percentages: Dict[str, float] = Field( | |
| default_factory=dict, | |
| description="Normalized percentages for display" | |
| ) | |
| class TimingInfo(BaseModel): | |
| """Timing breakdown for the prediction request.""" | |
| total: int = Field(..., description="Total time in milliseconds") | |
| download: Optional[int] = Field(None, description="Image download time in ms") | |
| preprocess: Optional[int] = Field(None, description="Preprocessing time in ms") | |
| inference: Optional[int] = Field(None, description="Model inference time in ms") | |
| fusion: Optional[int] = Field(None, description="Fusion computation time in ms") | |
| class PredictResponse(BaseModel): | |
| """Response schema for prediction endpoint.""" | |
| final: PredictionResult = Field( | |
| ..., | |
| description="Final prediction result" | |
| ) | |
| fusion_used: bool = Field( | |
| ..., | |
| description="Whether fusion was used for this prediction" | |
| ) | |
| submodels: Optional[Dict[str, PredictionResult]] = Field( | |
| None, | |
| description="Individual submodel predictions (when fusion_used=true and return_submodels=true)" | |
| ) | |
| fusion_meta: Optional[FusionMeta] = Field( | |
| None, | |
| description="Fusion metadata including model weights and contributions" | |
| ) | |
| model_display_info: Optional[Dict[str, ModelDisplayInfo]] = Field( | |
| None, | |
| description="Display information for each model (for frontend rendering)" | |
| ) | |
| timing_ms: TimingInfo = Field( | |
| ..., | |
| description="Timing breakdown in milliseconds" | |
| ) | |
| class Config: | |
| json_schema_extra = { | |
| "example": { | |
| "final": {"pred": "fake", "pred_int": 1, "prob_fake": 0.6667}, | |
| "fusion_used": True, | |
| "submodels": { | |
| "cnn-transfer": {"pred": "fake", "pred_int": 1, "prob_fake": 0.82, "contribution_percentage": 45.2}, | |
| "vit-base": {"pred": "fake", "pred_int": 1, "prob_fake": 0.75, "contribution_percentage": 32.1}, | |
| "gradfield-cnn": {"pred": "fake", "pred_int": 1, "prob_fake": 0.91, "contribution_percentage": 22.7} | |
| }, | |
| "timing_ms": {"total": 250, "inference": 200, "fusion": 5} | |
| } | |
| } | |
| # Request/Response for single-model explanation endpoint | |
| class ExplainModelRequest(BaseModel): | |
| """Request schema for single-model explanation.""" | |
| model_name: str = Field(..., description="Name of the model to explain") | |
| prob_fake: float = Field(..., ge=0.0, le=1.0, description="Model's fake probability") | |
| heatmap_base64: Optional[str] = Field(None, description="Base64-encoded heatmap") | |
| focus_summary: Optional[str] = Field(None, description="Where the model focused") | |
| contribution_percentage: Optional[float] = Field(None, description="Model's contribution to fusion") | |
| class ExplainModelResponse(BaseModel): | |
| """Response schema for single-model explanation.""" | |
| model_name: str = Field(..., description="Internal model name") | |
| insight: SingleModelInsight = Field(..., description="LLM-generated insight") | |
| class ErrorResponse(BaseModel): | |
| """Error response schema.""" | |
| error: str = Field(..., description="Error type") | |
| message: str = Field(..., description="Error message") | |
| details: Optional[Dict] = Field(None, description="Additional error details") | |