| """ |
| Pydantic Schemas — Request / Response validation |
| """ |
|
|
| from typing import Dict, Optional |
| from pydantic import BaseModel, Field |
|
|
|
|
| class PredictResponse(BaseModel): |
| predicted_emotion: str = Field(..., description="Emotion with highest confidence") |
| confidence: float = Field(..., ge=0.0, le=1.0, description="Confidence score 0-1") |
| all_scores: Dict[str, float] = Field(..., description="Score for every emotion class") |
| inference_time_ms: float = Field(..., description="Model inference time in milliseconds") |
|
|
| model_config = { |
| "json_schema_extra": { |
| "example": { |
| "predicted_emotion": "happy", |
| "confidence": 0.923, |
| "all_scores": { |
| "angry": 0.012, |
| "disgust": 0.003, |
| "fear": 0.008, |
| "happy": 0.923, |
| "neutral": 0.041, |
| "sad": 0.007, |
| "surprise": 0.006, |
| }, |
| "inference_time_ms": 48.3, |
| } |
| } |
| } |
|
|
|
|
| class ErrorResponse(BaseModel): |
| error: str = Field(..., description="Error message") |
| status_code: int = Field(..., description="HTTP status code") |
|
|
| model_config = { |
| "json_schema_extra": { |
| "example": { |
| "error": "File too large. Max allowed: 5 MB", |
| "status_code": 413, |
| } |
| } |
| } |
|
|
|
|
| class HealthResponse(BaseModel): |
| status: str = Field(..., description="API status") |
| model_path: str = Field(..., description="Path to loaded ONNX model") |
| model_loaded: bool = Field(..., description="Whether model file exists") |
| workers: int = Field(..., description="Number of process pool workers") |
|
|
| model_config = { |
| "json_schema_extra": { |
| "example": { |
| "status": "ok", |
| "model_path": "models/onnx/model_quantized.onnx", |
| "model_loaded": True, |
| "workers": 2, |
| } |
| } |
| } |
|
|
