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"""Pydantic schemas for key-point matching prediction endpoints"""
from pydantic import BaseModel, Field, ConfigDict
from typing import List, Optional, Dict
class PredictionRequest(BaseModel):
"""Request model for single key-point/argument prediction"""
model_config = ConfigDict(
json_schema_extra={
"example": {
"argument": "Climate change is accelerating due to industrial emissions.",
"key_point": "Human industry contributes significantly to global warming."
}
}
)
argument: str = Field(
..., min_length=5, max_length=1000,
description="The argument text to evaluate"
)
key_point: str = Field(
..., min_length=5, max_length=500,
description="The key point used for comparison"
)
class PredictionResponse(BaseModel):
"""Response model for single prediction"""
model_config = ConfigDict(
json_schema_extra={
"example": {
"prediction": 1,
"confidence": 0.874,
"label": "MATCH",
"probabilities": {
"match": 0.874,
"no_match": 0.126
}
}
}
)
prediction: int = Field(..., description="1 = match, 0 = no match")
confidence: float = Field(..., ge=0.0, le=1.0,
description="Confidence score of the prediction")
label: str = Field(..., description="MATCH or NO_MATCH")
probabilities: Dict[str, float] = Field(
..., description="Dictionary of class probabilities"
)
class BatchPredictionRequest(BaseModel):
"""Request model for batch predictions"""
model_config = ConfigDict(
json_schema_extra={
"example": {
"pairs": [
{
"argument": "Schools should implement AI tools to support learning.",
"key_point": "AI can improve student engagement."
},
{
"argument": "Governments must reduce plastic usage.",
"key_point": "Plastic waste harms the environment."
}
]
}
}
)
pairs: List[PredictionRequest] = Field(
..., max_length=100,
description="List of argument-keypoint pairs (max 100)"
)
class BatchPredictionResponse(BaseModel):
"""Response model for batch key-point predictions"""
predictions: List[PredictionResponse]
total_processed: int = Field(..., description="Number of processed items")
class HealthResponse(BaseModel):
"""Health check model for the API"""
model_config = ConfigDict(
json_schema_extra={
"example": {
"status": "ok",
"model_loaded": True,
"device": "cuda"
}
}
)
status: str = Field(..., description="API health status")
model_loaded: bool = Field(..., description="Whether the model is loaded")
device: str = Field(..., description="Device used for inference (cpu/cuda)")
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