"""Pydantic schemas for stance detection endpoints"""

from pydantic import BaseModel, Field, ConfigDict
from typing import List


class StanceRequest(BaseModel):
    """Request model for stance prediction"""
    model_config = ConfigDict(
        json_schema_extra={
            "example": {
                "topic": "Assisted suicide should be a criminal offence",
                "argument": "People have the right to choose how they end their lives"
            }
        }
    )
    
    topic: str = Field(..., min_length=5, max_length=500, 
                       description="The debate topic or statement")
    argument: str = Field(..., min_length=5, max_length=1000,
                         description="The argument text to classify")


class StanceResponse(BaseModel):
    """Response model for stance prediction"""
    model_config = ConfigDict(
        json_schema_extra={
            "example": {
                "topic": "Assisted suicide should be a criminal offence",
                "argument": "People have the right to choose how they end their lives",
                "predicted_stance": "CON",
                "confidence": 0.9234,
                "probability_con": 0.9234,
                "probability_pro": 0.0766,
                "timestamp": "2024-11-15T10:30:00"
            }
        }
    )
    
    topic: str
    argument: str
    predicted_stance: str = Field(..., description="PRO or CON")
    confidence: float = Field(..., ge=0.0, le=1.0)
    probability_con: float
    probability_pro: float
    timestamp: str


class BatchStanceRequest(BaseModel):
    """Request model for batch predictions"""
    items: List[StanceRequest] = Field(..., max_length=50,
                                      description="List of topic-argument pairs (max 50)")


class BatchStanceResponse(BaseModel):
    """Response model for batch predictions"""
    results: List[StanceResponse]
    total_processed: int