"""
AI-powered classification and confidence scoring.
"""
import openai
import json
import logging
from typing import Dict, Any

from app.config import config
from app.ai.prompts import AUDITOR_PROMPT
from app.schema.base import ProvisionalExpense

# Configure the OpenAI client
openai.api_key = config.OPENAI_API_KEY

logger = logging.getLogger(__name__)

def classify_and_audit(expense: ProvisionalExpense) -> ProvisionalExpense:
    """
    Uses an AI model to audit an extracted expense, providing a confidence
    score and notes. This is a placeholder for a more complex classification
    and validation logic.

    Args:
        expense: A ProvisionalExpense object with extracted data.

    Returns:
        The same ProvisionalExpense object, updated with the audit findings.
    """
    logger.info(f"Starting AI audit for expense: {expense.extracted_data.description}")

    # For now, this is a placeholder. A real implementation would
    # call an AI model like in the extractor.
    # For demonstration, we'll just assign a high confidence score.
    
    expense.confidence_score = 0.95
    expense.validation_notes.append("AI audit placeholder: auto-approved.")
    expense.processing_method = "ai_inference" # Assume AI was used

    logger.info("AI audit placeholder complete.")
    
    return expense