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Pydantic schemas for all API request/response bodies.
"""
from __future__ import annotations
from typing import Any, Optional
from pydantic import BaseModel, Field, field_validator
# ----------------------------------------------------------
# Prediction
# ----------------------------------------------------------
class PredictionRequest(BaseModel):
"""Features required for a single trip duration prediction."""
passenger_count: int = Field(..., ge=1, le=6, description="Number of passengers")
trip_distance: float = Field(..., gt=0, le=50, description="Trip distance in miles")
pickup_hour: int = Field(..., ge=0, le=23)
pickup_dow: int = Field(..., ge=0, le=6, description="Day of week (0=Monday)")
pickup_month: int = Field(..., ge=1, le=12)
pickup_is_weekend: int = Field(..., ge=0, le=1)
rate_code_id: int = Field(1, ge=1, le=5)
payment_type: int = Field(1, ge=1, le=4)
pu_location_zone: int = Field(1, ge=1, le=50)
do_location_zone: int = Field(1, ge=1, le=50)
vendor_id: int = Field(1, ge=1, le=2)
class PredictionResponse(BaseModel):
request_id: str
predicted_duration_min: float
model_version: str
timestamp: str
# ----------------------------------------------------------
# Ground Truth / Delayed Feedback
# ----------------------------------------------------------
class FeedbackRequest(BaseModel):
request_id: str = Field(..., description="Must match a prior prediction request_id")
actual_duration_min: float = Field(..., gt=0, description="Ground truth trip duration")
class FeedbackResponse(BaseModel):
request_id: str
matched: bool
message: str
# ----------------------------------------------------------
# Monitoring / Drift
# ----------------------------------------------------------
class DriftCheckResponse(BaseModel):
drift_detected: bool
root_cause: list[str]
performance_drop: Optional[str]
action: str
feature_results: dict[str, Any]
drifted_features: list[str]
rca_details: Optional[list[dict]]
timestamp: str
class PerformanceMetricsResponse(BaseModel):
rmse: Optional[float]
mae: Optional[float]
r2: Optional[float]
n_samples: Optional[int]
n_pending: int
baseline_rmse: Optional[float]
timestamp: str
# ----------------------------------------------------------
# Retraining
# ----------------------------------------------------------
class RetrainingResponse(BaseModel):
triggered: bool
promoted: Optional[bool]
improvement_pct: Optional[float]
root_causes: list[str]
action: str
message: str
timestamp: str
# ----------------------------------------------------------
# Health
# ----------------------------------------------------------
class HealthResponse(BaseModel):
status: str
model_loaded: bool
model_version: str
uptime_seconds: float
pending_feedback_count: int
matched_feedback_count: int
version: str
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