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https://github.com/mwisnowski/mtg_python_deckbuilder.git
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cfcc01db85
- Include/exclude cards feature complete with 300+ card knowledge base and intelligent fuzzy matching - Enhanced visual validation with warning icons and performance benchmarks (100% pass rate) - Mobile responsive design with bottom-floating controls, two-column layout, and horizontal scroll prevention - Dark theme confirmation modal for fuzzy matches with card preview and alternatives - Dual architecture support for web UI staging system and CLI direct build paths - All M3 checklist items completed: fuzzy match modal, enhanced algorithm, summary panel, mobile responsive, Playwright tests
42 lines
1.5 KiB
Python
42 lines
1.5 KiB
Python
#!/usr/bin/env python3
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"""Debug what Lightning cards are in the dataset"""
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import pandas as pd
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# Load the cards CSV
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cards_df = pd.read_csv('csv_files/cards.csv')
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print(f"Total cards loaded: {len(cards_df)}")
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# Find cards that contain "light" (case insensitive)
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light_cards = cards_df[cards_df['name'].str.contains('light', case=False, na=False)]['name'].unique()
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print(f"\nCards containing 'light': {len(light_cards)}")
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for card in sorted(light_cards)[:20]: # Show first 20
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print(f" - {card}")
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# Find cards that start with "light"
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light_start = cards_df[cards_df['name'].str.lower().str.startswith('light', na=False)]['name'].unique()
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print(f"\nCards starting with 'Light': {len(light_start)}")
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for card in sorted(light_start):
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print(f" - {card}")
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# Find specific Lightning cards
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lightning_cards = cards_df[cards_df['name'].str.contains('lightning', case=False, na=False)]['name'].unique()
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print(f"\nCards containing 'Lightning': {len(lightning_cards)}")
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for card in sorted(lightning_cards):
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print(f" - {card}")
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print(f"\nTesting direct matches for 'lightn':")
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test_input = "lightn"
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candidates = []
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for name in cards_df['name'].dropna().unique():
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# Test similarity to lightn
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from difflib import SequenceMatcher
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similarity = SequenceMatcher(None, test_input.lower(), name.lower()).ratio()
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if similarity > 0.6:
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candidates.append((similarity, name))
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# Sort by similarity
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candidates.sort(key=lambda x: x[0], reverse=True)
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print("Top 10 matches for 'lightn':")
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for score, name in candidates[:10]:
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print(f" {score:.3f} - {name}")
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