83 lines
2.9 KiB
Python
83 lines
2.9 KiB
Python
from typing import List, Dict
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import pandas as pd
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from datetime import datetime
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from pathlib import Path
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from jinja2 import Environment, FileSystemLoader
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from weasyprint import HTML
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import logging
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logger = logging.getLogger(__name__)
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class PullSheetService:
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def __init__(self):
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self.template_dir = Path("app/data/assets/templates")
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self.env = Environment(loader=FileSystemLoader(str(self.template_dir)))
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self.template = self.env.get_template("pull_sheet.html")
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self.output_dir = Path("app/data/cache/tcgplayer/pull_sheets")
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self.output_dir.mkdir(parents=True, exist_ok=True)
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def generate_pull_sheet_pdf(self, csv_path: str) -> str:
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"""Generate a PDF pull sheet from a CSV file.
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Args:
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csv_path: Path to the CSV file containing pull sheet data
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Returns:
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Path to the generated PDF file
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"""
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try:
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# Read and process CSV data
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items = self._read_and_process_csv(csv_path)
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# Prepare template data
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template_data = {
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'items': items,
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'generation_date': datetime.now().strftime("%Y-%m-%d %H:%M:%S")
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}
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# Render HTML
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html_content = self.template.render(**template_data)
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# Generate PDF
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pdf_path = self.output_dir / f"pull_sheet_{datetime.now().strftime('%Y%m%d_%H%M%S')}.pdf"
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HTML(string=html_content).write_pdf(str(pdf_path))
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return str(pdf_path)
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except Exception as e:
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logger.error(f"Error generating pull sheet PDF: {str(e)}")
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raise
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def _read_and_process_csv(self, csv_path: str) -> List[Dict]:
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"""Read and process CSV data using pandas.
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Args:
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csv_path: Path to the CSV file
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Returns:
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List of processed items
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"""
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# Read CSV into pandas DataFrame
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df = pd.read_csv(csv_path)
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# Filter out the "Orders Contained in Pull Sheet" row
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df = df[df['Product Line'] != 'Orders Contained in Pull Sheet:']
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# Convert Set Release Date to datetime
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df['Set Release Date'] = pd.to_datetime(df['Set Release Date'], format='%m/%d/%Y %H:%M:%S')
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# Sort by Set Release Date (descending) and then Product Name (ascending)
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df = df.sort_values(['Set Release Date', 'Product Name'], ascending=[False, True])
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# Convert to list of dictionaries
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items = []
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for _, row in df.iterrows():
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items.append({
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'product_name': row['Product Name'],
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'condition': row['Condition'],
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'quantity': str(int(row['Quantity'])), # Convert to string for template
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'set': row['Set'],
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'rarity': row['Rarity']
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})
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return items |