models/analysis.py

"""Pydantic models for analysis endpoints"""

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


class AnalysisRequest(BaseModel):
    """Request model for analysis with arguments"""
    model_config = ConfigDict(
        json_schema_extra={
            "example": {
                "arguments": [
                    "Social media companies must NOT be allowed to track people across websites.",
                    "I don't think universal basic income is a good idea — it'll disincentivize work.",
                    "We must invest in renewable energy to combat climate change."
                ]
            }
        }
    )
    
    arguments: List[str] = Field(
        ..., min_length=1, max_length=100,
        description="List of argument texts to analyze (max 100)"
    )


class AnalysisResult(BaseModel):
    """Model for a single analysis result"""
    model_config = ConfigDict(
        json_schema_extra={
            "example": {
                "id": "123e4567-e89b-12d3-a456-426614174000",
                "user_id": "123e4567-e89b-12d3-a456-426614174000",
                "argument": "Social media companies must NOT be allowed to track people across websites.",
                "topic": "social media tracking and cross-website user privacy",
                "predicted_stance": "CON",
                "confidence": 0.9234,
                "probability_con": 0.9234,
                "probability_pro": 0.0766,
                "created_at": "2024-01-01T12:00:00Z",
                "updated_at": "2024-01-01T12:00:00Z"
            }
        }
    )
    
    id: str = Field(..., description="Analysis result UUID")
    user_id: str = Field(..., description="User UUID")
    argument: str = Field(..., description="The argument text")
    topic: str = Field(..., description="Extracted topic")
    predicted_stance: str = Field(..., description="PRO or CON")
    confidence: float = Field(..., ge=0.0, le=1.0, description="Confidence score")
    probability_con: float = Field(..., ge=0.0, le=1.0, description="Probability of CON")
    probability_pro: float = Field(..., ge=0.0, le=1.0, description="Probability of PRO")
    created_at: str = Field(..., description="Creation timestamp")
    updated_at: str = Field(..., description="Last update timestamp")


class AnalysisResponse(BaseModel):
    """Response model for analysis endpoint"""
    model_config = ConfigDict(
        json_schema_extra={
            "example": {
                "results": [
                    {
                        "id": "123e4567-e89b-12d3-a456-426614174000",
                        "user_id": "123e4567-e89b-12d3-a456-426614174000",
                        "argument": "Social media companies must NOT be allowed to track people across websites.",
                        "topic": "social media tracking and cross-website user privacy",
                        "predicted_stance": "CON",
                        "confidence": 0.9234,
                        "probability_con": 0.9234,
                        "probability_pro": 0.0766,
                        "created_at": "2024-01-01T12:00:00Z",
                        "updated_at": "2024-01-01T12:00:00Z"
                    }
                ],
                "total_processed": 1,
                "timestamp": "2024-01-01T12:00:00Z"
            }
        }
    )
    
    results: List[AnalysisResult] = Field(..., description="List of analysis results")
    total_processed: int = Field(..., description="Number of arguments processed")
    timestamp: str = Field(..., description="Analysis timestamp")


class GetAnalysisRequest(BaseModel):
    """Request model for getting user's analysis results"""
    limit: Optional[int] = Field(100, ge=1, le=1000, description="Maximum number of results")
    offset: Optional[int] = Field(0, ge=0, description="Number of results to skip")


class GetAnalysisResponse(BaseModel):
    """Response model for getting analysis results"""
    results: List[AnalysisResult] = Field(..., description="List of analysis results")
    total: int = Field(..., description="Total number of results")
    limit: int = Field(..., description="Limit used")
    offset: int = Field(..., description="Offset used")