models.py

from pydantic import BaseModel, Field
from typing import Optional, List, Dict, Any


class FeedbackAnalysisConfig(BaseModel):
    """Configuration for feedback analysis."""
    model: str = Field(..., description="LLM model to use for analysis")
    temperature: float = Field(0.0, description="Temperature setting for the LLM")
    max_tokens: int = Field(1000, description="Maximum tokens for LLM response")
    batch_size: int = Field(10, description="Number of feedback items to process in each batch")
    feedback_column: str = Field(..., description="Column name containing the feedback text")
    output_column: str = Field(..., description="Column name for the analysis results")
    prompt_template: str = Field(..., description="Template for the analysis prompt")


class FeedbackAnalysisResult(BaseModel):
    """Base model for feedback analysis results."""
    raw_response: str = Field(..., description="Raw response from the LLM")
    parsed_data: Optional[Dict[str, Any]] = Field(None, description="Parsed structured data")
    error: Optional[str] = Field(None, description="Error message if parsing failed")


class BatchProcessingStats(BaseModel):
    """Statistics for batch processing."""
    total_items: int = Field(..., description="Total number of items to process")
    processed_items: int = Field(0, description="Number of items processed")
    successful_items: int = Field(0, description="Number of items successfully processed")
    failed_items: int = Field(0, description="Number of items that failed processing")
    start_time: float = Field(..., description="Processing start time (timestamp)")
    end_time: Optional[float] = Field(None, description="Processing end time (timestamp)")
    
    def calculate_progress(self) -> float:
        """Calculate the current progress as a percentage."""
        if self.total_items == 0:
            return 1.0
        return self.processed_items / self.total_items