Rewrite filename check + add price/currency check to image QC
Filename check: - Rewritten to flexibly parse multiple H&M naming conventions (Display, DOOH, OOH, SOME STATIC, Social, POS, DS) - Extracts country code, language code, dimensions, campaign number - Scores based on how much metadata was extracted (not rigid pattern) - Tested against real filenames: BG_bg, ES_es, NO-no formats Price/currency check (new): - Detects prices in images via LLM vision API - Validates currency against global pricing reference (deterministic) - Falls back to LLM validation for unknown countries - Optional campaign pricing sheet validation when has_pricing=True - Added to profile with weight 30 Profile weights rebalanced: filename 30, quality 40, price 30 Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
This commit is contained in:
nickviljoen
parent
dc73268309
commit
fc15a2dda3
5 changed files with 454 additions and 87 deletions
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@ -7,5 +7,6 @@ from .base_check import BaseCheck
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from .sample_filename_check import FilenameCheck
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from .sample_quality_check import QualityCheck
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from .dimension_check import DimensionCheck
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from .price_currency_check import PriceCurrencyCheck
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__all__ = ['BaseCheck', 'FilenameCheck', 'QualityCheck', 'DimensionCheck']
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__all__ = ['BaseCheck', 'FilenameCheck', 'QualityCheck', 'DimensionCheck', 'PriceCurrencyCheck']
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271
modules/hm_qc/checks/price_currency_check.py
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271
modules/hm_qc/checks/price_currency_check.py
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@ -0,0 +1,271 @@
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"""
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Price & Currency Check.
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Validates that prices/currency in the image match the expected country
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using the global pricing reference and optional campaign pricing sheet.
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"""
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import os
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import json
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import logging
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from typing import Dict, Any
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from PIL import Image
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from .base_check import BaseCheck
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from core.services.llm_config import LLMConfig
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logger = logging.getLogger(__name__)
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class PriceCurrencyCheck(BaseCheck):
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"""
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Check that price/currency in the image matches the country code from filename.
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Uses:
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1. Global pricing reference (storage/reference/global_pricing.json) for
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deterministic currency symbol/format validation
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2. Campaign pricing sheet (if has_pricing=True) for actual price validation
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3. LLM fallback for unknown countries
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"""
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def __init__(self, name: str = "price_currency", weight: float = 25.0, config: Dict[str, Any] = None):
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super().__init__(name, weight, config)
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self.llm_provider = self.config.get('llm_provider', 'openai')
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self.llm_model = self.config.get('llm_model', 'gpt-4o')
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def run(self, file_path: str, context: Dict[str, Any]) -> Dict[str, Any]:
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try:
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# Get language/country from filename check
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filename_data = context.get('filename_data', {})
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language = filename_data.get('language', '')
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country_code = filename_data.get('country_code', '')
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if not language and not country_code:
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return self._create_result(
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status='skipped',
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score=100.0,
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message='No country/language code found in filename — skipping price check',
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details={'reason': 'no_country_code'}
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)
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# Skip for GEN/CEN files
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if language and language.upper() in ('GEN', 'CEN'):
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return self._create_result(
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status='skipped',
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score=100.0,
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message=f'Price check skipped for {language.upper()} files',
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details={'reason': 'gen_cen_file'}
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)
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# Check file is an image
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ext = os.path.splitext(file_path)[1].lower()
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if ext not in ('.jpg', '.jpeg', '.png'):
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return self._create_result(
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status='skipped',
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score=100.0,
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message='Price check only applies to image files',
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details={'file_type': ext}
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)
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self.logger.info(f"Running price/currency check for {language} ({country_code})")
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# Step 1: Detect prices in the image using LLM
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price_info = self._detect_prices(file_path, context)
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if not price_info or price_info.get('currency_found') == 'NOT_FOUND':
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return self._create_result(
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status='skipped',
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score=100.0,
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message='No price/currency detected in image — skipping validation',
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details={'price_detection': price_info or {}}
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)
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# Step 2: Validate against global pricing reference
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currency = price_info.get('currency_found', '')
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validation = self._validate_currency(currency, language, country_code)
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# Step 3: Campaign-specific price validation
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campaign_result = self._validate_campaign_price(price_info, language, context)
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# Build result
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score = 100.0
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issues = []
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details = {
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'language': language,
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'country_code': country_code,
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'detected_currency': currency,
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'detected_price': price_info.get('price_value'),
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'confidence': price_info.get('confidence', 0),
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**validation
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}
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if campaign_result:
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details['campaign_price'] = campaign_result
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# Score deductions
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if price_info.get('confidence', 0) < 0.7:
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score -= 20
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issues.append(f"Low confidence in price detection ({price_info.get('confidence', 0):.0%})")
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if not validation.get('matches_region', True):
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score -= 40
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issues.append(
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f"Currency mismatch: found {currency}, "
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f"expected {validation.get('expected_currency', '?')} for {language}"
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)
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if campaign_result and campaign_result.get('price_matches_campaign') is False:
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score -= 20
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issues.append(
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f"Price does not match campaign sheet: "
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f"expected {campaign_result.get('expected_price', '?')}"
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)
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details['issues'] = issues
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if score >= 90:
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status = 'passed'
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message = f'Price/currency validation passed — {currency} correct for {language}'
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elif score >= 70:
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status = 'warning'
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message = f'Price/currency has minor issues: {", ".join(issues)}'
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else:
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status = 'failed'
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message = f'Price/currency validation failed: {", ".join(issues)}'
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return self._create_result(
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status=status,
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score=score,
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message=message,
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details=details,
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recommendations=[
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f'Expected currency for {language}: {validation.get("expected_currency", "unknown")} ({validation.get("expected_symbol", "")})'
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] if issues else None
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)
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except Exception as e:
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self.logger.error(f"Price/currency check error: {e}", exc_info=True)
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return self._create_result(
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status='error',
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score=0.0,
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message=f'Error running price check: {str(e)}',
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details={'error': str(e)}
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)
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def _detect_prices(self, file_path: str, context: Dict[str, Any]) -> dict:
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"""Use LLM to detect prices and currency in the image."""
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prompt = """Analyze this image for price and currency information.
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Extract any prices shown and identify the currency used.
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Be flexible in recognizing different formats (e.g., "$100", "LE 699", "€20", "29,99 лв.").
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Return JSON with:
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- currency_found: 3-letter currency code (e.g., "BGN", "EUR", "USD") or "NOT_FOUND"
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- currency_symbol: the actual symbol shown (e.g., "лв.", "€", "$") or null
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- price_value: detected numerical value as string or null
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- symbol_position: "before" or "after" the price, or null
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- format_valid: boolean - is the price properly formatted
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- confidence: confidence score 0-1
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"""
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try:
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response = LLMConfig.call_vision_api(
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prompt=prompt,
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image_asset=file_path,
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provider=self.llm_provider,
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model=self.llm_model,
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usage_context={
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'module': 'hm_qc',
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'check_name': 'price_currency',
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'user': context.get('user'),
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'session_id': context.get('session_id')
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}
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)
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text = response.get('text', '')
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# Extract JSON from response
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start = text.find('{')
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end = text.rfind('}') + 1
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if start != -1 and end > start:
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return json.loads(text[start:end])
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except Exception as e:
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self.logger.warning(f"Price detection failed: {e}")
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return None
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def _validate_currency(self, currency: str, language: str, country_code: str) -> dict:
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"""Validate detected currency against global pricing reference."""
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result = {'matches_region': True, 'validation_method': 'none'}
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# Try global pricing reference
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pricing_path = os.path.join('storage', 'reference', 'global_pricing.json')
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if os.path.exists(pricing_path):
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try:
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with open(pricing_path, 'r', encoding='utf-8') as f:
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global_pricing = json.load(f)
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# Try language code (e.g., "bg-BG"), then country code (e.g., "BG")
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entry = global_pricing.get(language) or global_pricing.get(country_code)
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if entry:
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expected_currency = entry.get('currency_code', '')
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expected_symbol = entry.get('symbol', '')
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matches = (
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currency.upper() == expected_currency.upper() or
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currency == expected_symbol
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)
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return {
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'matches_region': matches,
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'expected_currency': expected_currency,
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'expected_symbol': expected_symbol,
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'expected_position': entry.get('position', ''),
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'expected_country': entry.get('country', ''),
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'validation_method': 'global_pricing_reference',
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'reason': (
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f"{'Match' if matches else 'Mismatch'}: "
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f"{language} expects {expected_currency} ({expected_symbol}), found {currency}"
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)
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}
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except (json.JSONDecodeError, OSError):
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pass
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# No global pricing available — don't penalize
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result['validation_method'] = 'skipped_no_reference'
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result['reason'] = 'No global pricing reference available'
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return result
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def _validate_campaign_price(self, price_info: dict, language: str, context: Dict[str, Any]) -> dict:
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"""Validate actual price against campaign pricing sheet if available."""
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campaign_ctx = context.get('campaign_presentation', {})
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if not campaign_ctx.get('has_pricing') or not campaign_ctx.get('parsed_content'):
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return None
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try:
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currency = price_info.get('currency_found', '')
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price_value = price_info.get('price_value', '')
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prompt = f"""Compare the price detected in this asset against the campaign pricing sheet.
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Detected price: {price_value} {currency}
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Region/Language: {language}
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Campaign pricing reference:
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{campaign_ctx['parsed_content'][:6000]}
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Return JSON with:
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- price_matches_campaign: true/false
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- expected_price: the expected price from the campaign sheet for this region (or null)
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- reason: brief explanation
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"""
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client = LLMConfig.get_client('openai', 'gpt-4o')
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response = client.chat.completions.create(
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model='gpt-4o',
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messages=[{"role": "user", "content": prompt}],
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max_tokens=1024
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)
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text = response.choices[0].message.content or ''
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start = text.find('{')
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end = text.rfind('}') + 1
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if start != -1 and end > start:
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return json.loads(text[start:end])
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except Exception as e:
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self.logger.warning(f"Campaign price validation failed: {e}")
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return None
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@ -1,106 +1,97 @@
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"""
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Sample Filename Check.
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H&M Filename Check.
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Validates H&M filename conventions (simple check, no AI needed).
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Demonstrates scoring with deductions for issues.
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Extracts key metadata from H&M filenames using flexible pattern matching.
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Supports multiple naming conventions used across H&M campaigns.
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"""
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import os
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import re
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from typing import Dict, Any
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from typing import Dict, Any, Optional
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from .base_check import BaseCheck
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class FilenameCheck(BaseCheck):
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"""
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Check filename against H&M conventions.
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Flexible filename parser for H&M marketing assets.
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Expected format: {dimensions}_{format}_{year}_{reference}_{language}.{ext}
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Example: 1080x1920_jpg_2024_spring_en-GB.pdf
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Extracts country code, language code, dimensions, campaign number,
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and format type from various H&M naming conventions. Scores based
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on how much metadata could be extracted.
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Known H&M filename formats:
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- SOME STATIC: Market_Language_CampaignNum_CampaignName_Format_CreativeType_Ratio_ImageNum
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e.g., AT_de_4116A_Halloween_Stories_fb_9x16_1.jpg
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- Display Banners: ID_ID_CampaignNum_Display_Static_MediaOwner_Size_Version_Market_Lang_Priority
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e.g., 6186653_1910358_1022B_Display_Static_Netinfo_300x600_V1_BG_bg_PRIO4.jpg
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- DOOH/OOH: CampaignNum_DOOH_Static_Option_MediaOwner_FormatName_Size_Language-Market
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e.g., 4045_DOOH_Static_PRIO1_EyeMediaGiant_Noreport_1080x1920_NO-no.jpg
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- POS GEN: Size_Format_CampaignNum_POPNumber_GEN
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e.g., 21x29.7cm_A4_4068A_10065-01_GEN.jpg
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- POS Country: Size_Format_CampaignNum_POPNumber_Language-Market
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e.g., 50x70cm_Poster_4068A_10107-01_en-GB.jpg
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- Video: Market_Lang_CampaignNum_CampaignName_Platform_Ratio_Duration_Version_ID
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e.g., ES_es_1013A_Spring_W_Bumper_YT_16x9_6_A_6889135.mp4
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"""
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def __init__(self, name: str = "filename_check", weight: float = 10.0, config: Dict[str, Any] = None):
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super().__init__(name, weight, config)
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# Filename pattern (simplified for demo)
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self.pattern = re.compile(
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r'^(\d+x\d+)_([a-zA-Z0-9]+)_(\d{4})_([a-zA-Z0-9_-]+)_([a-zA-Z]{2}-[A-Z]{2}|GEN|CEN)\.(pdf|jpg|jpeg|png|psd)$',
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re.IGNORECASE
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)
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def run(self, file_path: str, context: Dict[str, Any]) -> Dict[str, Any]:
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"""
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Run filename validation check.
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Args:
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file_path: Path to file
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context: Shared context
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Returns:
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Check result with score
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"""
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try:
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filename = os.path.basename(file_path)
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base_name, ext = os.path.splitext(filename)
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ext_clean = ext.lstrip('.').lower()
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self.logger.info(f"Checking filename: {filename}")
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# Parse filename
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match = self.pattern.match(filename)
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# Extract metadata using flexible patterns
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parsed = self._parse_filename(base_name, ext_clean)
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if not match:
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return self._create_result(
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status='failed',
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score=30.0,
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message='Filename does not match H&M conventions',
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details={
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'filename': filename,
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'expected_format': '{dimensions}_{format}_{year}_{reference}_{language}.{ext}',
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'example': '1080x1920_jpg_2024_spring_en-GB.pdf'
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},
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recommendations=[
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'Rename file to follow H&M naming convention',
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'Ensure all required fields are present',
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'Use correct language code format (e.g., en-GB, GEN, CEN)'
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]
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)
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# Store in context for other checks (quality, pricing)
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context['filename_data'] = parsed
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# Extract components
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dimensions, format_type, year, reference, language, ext = match.groups()
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# Calculate score with deductions
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score = 100.0
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# Score based on what was extracted
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score = 50.0 # Base score for having a parseable filename
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issues = []
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found = []
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# Check year (should be current or recent)
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current_year = 2025
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year_int = int(year)
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if year_int < current_year - 2:
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score -= 10
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issues.append(f"Year {year} is more than 2 years old")
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if parsed.get('country_code'):
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score += 15
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found.append(f"Country: {parsed['country_code']}")
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else:
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issues.append("Could not extract country code")
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# Check format consistency
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if format_type.lower() != ext.lower() and ext.lower() != 'pdf':
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score -= 15
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issues.append(f"Format '{format_type}' doesn't match extension '{ext}'")
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if parsed.get('language_code'):
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score += 10
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found.append(f"Language: {parsed['language_code']}")
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else:
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issues.append("Could not extract language code")
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if parsed.get('dimensions'):
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score += 10
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found.append(f"Dimensions: {parsed['dimensions']}")
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else:
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issues.append("Could not extract dimensions/size")
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if parsed.get('campaign_number'):
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score += 10
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found.append(f"Campaign: {parsed['campaign_number']}")
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else:
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issues.append("Could not extract campaign number")
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if parsed.get('format_type'):
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score += 5
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found.append(f"Format: {parsed['format_type']}")
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# Determine status
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if score >= 90:
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status = 'passed'
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message = 'Filename follows H&M conventions'
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message = 'Filename parsed successfully — all key metadata extracted'
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elif score >= 70:
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status = 'warning'
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message = f'Filename valid but has minor issues: {", ".join(issues)}'
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message = f'Filename partially parsed — missing: {", ".join(issues)}'
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else:
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status = 'failed'
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message = f'Filename has significant issues: {", ".join(issues)}'
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# Store parsed data in context for other checks
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context['filename_data'] = {
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'dimensions': dimensions,
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'format': format_type,
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'year': year,
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'reference': reference,
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'language': language,
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'extension': ext
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}
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message = f'Filename could not be parsed — missing: {", ".join(issues)}'
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return self._create_result(
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status=status,
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|
|
@ -108,18 +99,16 @@ class FilenameCheck(BaseCheck):
|
|||
message=message,
|
||||
details={
|
||||
'filename': filename,
|
||||
'parsed': {
|
||||
'dimensions': dimensions,
|
||||
'format': format_type,
|
||||
'year': year,
|
||||
'reference': reference,
|
||||
'language': language,
|
||||
'extension': ext
|
||||
},
|
||||
'issues': issues if issues else []
|
||||
'detected_format': parsed.get('detected_format', 'unknown'),
|
||||
'extracted': {k: v for k, v in parsed.items() if v and k != 'detected_format'},
|
||||
'found': found,
|
||||
'issues': issues
|
||||
},
|
||||
recommendations=['Update year to current year'] if issues else None,
|
||||
analysis=f"Filename components parsed successfully. {len(issues)} issue(s) found." if issues else "Filename is valid and follows conventions."
|
||||
recommendations=[
|
||||
'Ensure filename contains country code (e.g., BG, GB, ES)',
|
||||
'Ensure filename contains language code (e.g., bg, en, es)',
|
||||
'Ensure filename contains dimensions (e.g., 300x600, 1080x1920)'
|
||||
] if issues else None
|
||||
)
|
||||
|
||||
except Exception as e:
|
||||
|
|
@ -130,3 +119,101 @@ class FilenameCheck(BaseCheck):
|
|||
message=f'Error checking filename: {str(e)}',
|
||||
details={'error': str(e)}
|
||||
)
|
||||
|
||||
def _parse_filename(self, base_name: str, ext: str) -> Dict[str, Any]:
|
||||
"""
|
||||
Extract metadata from filename using multiple pattern strategies.
|
||||
|
||||
Returns dict with: country_code, language_code, language (combined),
|
||||
dimensions, campaign_number, format_type, detected_format, extension
|
||||
"""
|
||||
result = {
|
||||
'country_code': None,
|
||||
'language_code': None,
|
||||
'language': None, # Combined "lang-COUNTRY" for other checks
|
||||
'dimensions': None,
|
||||
'campaign_number': None,
|
||||
'format_type': None,
|
||||
'detected_format': None,
|
||||
'extension': ext,
|
||||
'ratio': None,
|
||||
}
|
||||
|
||||
parts = base_name.split('_')
|
||||
|
||||
# --- Extract dimensions (NxN pattern) ---
|
||||
dim_match = re.search(r'(\d+x\d+)', base_name, re.IGNORECASE)
|
||||
if dim_match:
|
||||
result['dimensions'] = dim_match.group(1)
|
||||
# Derive ratio from dimensions
|
||||
try:
|
||||
w, h = dim_match.group(1).split('x')
|
||||
result['ratio'] = f"{w}x{h}"
|
||||
except ValueError:
|
||||
pass
|
||||
|
||||
# --- Extract campaign number (digits + optional letter, e.g., 1022B, 4116A) ---
|
||||
campaign_match = re.search(r'(?:^|_)(\d{3,5}[A-Z]?)(?:_|$)', base_name)
|
||||
if campaign_match:
|
||||
result['campaign_number'] = campaign_match.group(1)
|
||||
|
||||
# --- Detect format type ---
|
||||
name_upper = base_name.upper()
|
||||
if '_DOOH_' in name_upper:
|
||||
result['format_type'] = 'DOOH'
|
||||
result['detected_format'] = 'DOOH'
|
||||
elif '_OOH_' in name_upper:
|
||||
result['format_type'] = 'OOH'
|
||||
result['detected_format'] = 'OOH'
|
||||
elif '_DISPLAY_' in name_upper:
|
||||
result['format_type'] = 'Display'
|
||||
result['detected_format'] = 'DISPLAY_BANNER'
|
||||
elif any(x in name_upper for x in ['_FB_', '_TK_', '_PN_', '_YT_']):
|
||||
result['format_type'] = 'Social'
|
||||
result['detected_format'] = 'SOCIAL_MEDIA'
|
||||
elif '_POS_' in name_upper or re.match(r'^\d+x\d+cm', base_name, re.IGNORECASE):
|
||||
result['format_type'] = 'POS'
|
||||
result['detected_format'] = 'POS'
|
||||
|
||||
# --- Extract country/language codes ---
|
||||
|
||||
# Strategy 1: SOME STATIC — Market_Language at start (e.g., AT_de_...)
|
||||
start_match = re.match(r'^([A-Z]{2})_([a-z]{2})_', base_name)
|
||||
if start_match:
|
||||
result['country_code'] = start_match.group(1)
|
||||
result['language_code'] = start_match.group(2)
|
||||
result['detected_format'] = result['detected_format'] or 'SOME_STATIC'
|
||||
|
||||
# Strategy 2: Display banner — _MARKET_lang_ near end (e.g., _BG_bg_)
|
||||
if not result['country_code']:
|
||||
market_lang = re.search(r'_([A-Z]{2})_([a-z]{2})(?:_|$)', base_name)
|
||||
if market_lang:
|
||||
result['country_code'] = market_lang.group(1)
|
||||
result['language_code'] = market_lang.group(2)
|
||||
|
||||
# Strategy 3: Hyphenated — lang-MARKET or MARKET-lang at end (e.g., _en-GB, _NO-no)
|
||||
if not result['country_code']:
|
||||
hyph_match = re.search(r'_([a-z]{2})-([A-Z]{2})(?:\.|_|$)', base_name)
|
||||
if hyph_match:
|
||||
result['language_code'] = hyph_match.group(1)
|
||||
result['country_code'] = hyph_match.group(2)
|
||||
else:
|
||||
hyph_match2 = re.search(r'_([A-Z]{2})-([a-z]{2})(?:\.|_|$)', base_name)
|
||||
if hyph_match2:
|
||||
result['country_code'] = hyph_match2.group(1)
|
||||
result['language_code'] = hyph_match2.group(2)
|
||||
|
||||
# Strategy 4: GEN/CEN marker
|
||||
if not result['country_code']:
|
||||
gen_match = re.search(r'_(GEN|CEN)(?:\.|_|$)', base_name, re.IGNORECASE)
|
||||
if gen_match:
|
||||
result['language'] = gen_match.group(1).upper()
|
||||
result['country_code'] = gen_match.group(1).upper()
|
||||
result['language_code'] = gen_match.group(1).upper()
|
||||
result['detected_format'] = result['detected_format'] or 'POS_GEN'
|
||||
|
||||
# Build combined language code (e.g., "bg-BG")
|
||||
if result['country_code'] and result['language_code'] and not result['language']:
|
||||
result['language'] = f"{result['language_code']}-{result['country_code']}"
|
||||
|
||||
return result
|
||||
|
|
|
|||
|
|
@ -11,7 +11,7 @@ import logging
|
|||
from datetime import datetime
|
||||
from typing import Dict, List, Any
|
||||
from .scoring import ScoringEngine
|
||||
from .checks import FilenameCheck, QualityCheck, DimensionCheck
|
||||
from .checks import FilenameCheck, QualityCheck, DimensionCheck, PriceCurrencyCheck
|
||||
from core.utils.progress_tracker import UnifiedProgressTracker
|
||||
from core.models.qc_report import QCReport
|
||||
from core.models.database import db
|
||||
|
|
@ -171,7 +171,8 @@ class QCExecutor:
|
|||
'filename_parse': FilenameCheck,
|
||||
'quality_check': QualityCheck,
|
||||
'image_quality': QualityCheck,
|
||||
'dimension_check': DimensionCheck
|
||||
'dimension_check': DimensionCheck,
|
||||
'price_currency': PriceCurrencyCheck
|
||||
}
|
||||
|
||||
for check_config in profile_checks:
|
||||
|
|
|
|||
|
|
@ -11,17 +11,24 @@ profiles:
|
|||
description: "Quality checks for H&M image assets (JPG, PNG, PSD)"
|
||||
checks:
|
||||
- name: "filename_parse"
|
||||
weight: 50
|
||||
weight: 30
|
||||
enabled: true
|
||||
llm_provider: null
|
||||
description: "Validate H&M filename conventions"
|
||||
description: "Extract and validate country code, language, dimensions from filename"
|
||||
|
||||
- name: "image_quality"
|
||||
weight: 50
|
||||
weight: 40
|
||||
enabled: true
|
||||
llm_provider: "openai"
|
||||
llm_model: "gpt-4o"
|
||||
description: "AI-powered image quality and legibility assessment"
|
||||
description: "AI-powered image quality, legibility, and campaign guideline assessment"
|
||||
|
||||
- name: "price_currency"
|
||||
weight: 30
|
||||
enabled: true
|
||||
llm_provider: "openai"
|
||||
llm_model: "gpt-4o"
|
||||
description: "Validate price/currency matches country using global pricing reference"
|
||||
|
||||
# Note: Weights should sum to 100 for each profile
|
||||
# Higher weight = more important to overall score
|
||||
|
|
|
|||
Loading…
Add table
Reference in a new issue