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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": "Students should wear school uniforms",
"key_point": "School uniforms are good for students"
}
}
)
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": 0,
"confidence": 0.9992,
"label": "non_apparie",
"probabilities": {
"non_apparie": 0.9992,
"apparie": 0.0008
}
}
}
)
prediction: int = Field(..., description="1 = apparie, 0 = non_apparie")
confidence: float = Field(..., ge=0.0, le=1.0,
description="Confidence score of the prediction")
label: str = Field(..., description="apparie or non_apparie")
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": "Students should wear school uniforms",
"key_point": "School uniforms are good for students"
},
{
"argument": "We need more renewable energy",
"key_point": "Solar and wind power are important"
},
{
"argument": "Exercise improves health",
"key_point": "Physical activity is good for the body"
},
{
"argument": "Education should be free for everyone",
"key_point": "Cars should be electric"
},
{
"argument": "Reading books helps learning",
"key_point": "We should build more parks"
}
]
}
}
)
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"""
model_config = ConfigDict(
json_schema_extra={
"example": {
"predictions": [
{
"prediction": 0,
"confidence": 0.9992,
"label": "non_apparie",
"probabilities": {
"non_apparie": 0.9992,
"apparie": 0.0008
}
},
{
"prediction": 1,
"confidence": 0.5279,
"label": "apparie",
"probabilities": {
"non_apparie": 0.4721,
"apparie": 0.5279
}
},
{
"prediction": 1,
"confidence": 0.8836,
"label": "apparie",
"probabilities": {
"non_apparie": 0.1164,
"apparie": 0.8836
}
},
{
"prediction": 0,
"confidence": 0.5157,
"label": "non_apparie",
"probabilities": {
"non_apparie": 0.5157,
"apparie": 0.4843
}
},
{
"prediction": 0,
"confidence": 0.9958,
"label": "non_apparie",
"probabilities": {
"non_apparie": 0.9958,
"apparie": 0.0042
}
}
],
"total_processed": 5,
"summary": {
"total_apparie": 2,
"total_non_apparie": 3,
"average_confidence": 0.7844,
"successful_predictions": 5,
"failed_predictions": 0
}
}
}
)
predictions: List[PredictionResponse]
total_processed: int = Field(..., description="Number of processed items")
summary: Dict[str, float] = Field(
default_factory=dict,
description="Summary statistics of the batch prediction"
)
class HealthResponse(BaseModel):
"""Health check model for the API"""
model_config = ConfigDict(
json_schema_extra={
"example": {
"status": "healthy",
"model_loaded": True,
"device": "cpu",
"model_name": "NLP-Debater-Project/distilBert-keypoint-matching",
"timestamp": "2024-01-01T12:00:00Z"
}
}
)
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)")
model_name: Optional[str] = Field(None, description="Name of the loaded model")
timestamp: str = Field(..., description="Timestamp of the health check")
class ModelInfoResponse(BaseModel):
"""Detailed model information response"""
model_config = ConfigDict(
json_schema_extra={
"example": {
"model_name": "NLP-Debater-Project/distilBert-keypoint-matching",
"device": "cpu",
"max_length": 256,
"num_labels": 2,
"loaded": True,
"performance": {
"accuracy": 0.9285,
"f1_score": 0.8836,
"f1_apparie": 0.8113,
"f1_non_apparie": 0.9559
},
"description": "DistilBERT model for key point - argument semantic matching"
}
}
)
model_name: str
device: str
max_length: int
num_labels: int
loaded: bool
performance: Dict[str, float] = Field(
..., description="Model performance metrics"
)
description: str = Field(..., description="Model description") |