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feat: add theme quality, pool size, and popularity badges with filtering (#56)
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mwisnowski
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21 changed files with 1165 additions and 64 deletions
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@ -8,6 +8,7 @@ Responsibilities:
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- Provide summary & detail projections (with synergy segmentation).
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- NEW (Phase F perf): precompute summary dicts & lowercase haystacks, and
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add fast filtering / result caching to accelerate list & API endpoints.
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- R20: Calculate theme quality scores and pool sizes for UI display.
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"""
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from __future__ import annotations
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@ -69,6 +70,10 @@ class SlugThemeIndex(BaseModel):
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haystack_by_slug: Dict[str, str]
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primary_color_by_slug: Dict[str, Optional[str]]
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secondary_color_by_slug: Dict[str, Optional[str]]
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quality_tier_by_slug: Dict[str, Optional[str]] # R20: Excellent/Good/Fair/Poor
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quality_score_by_slug: Dict[str, Optional[float]] # R20: 0.0-1.0 normalized quality
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pool_size_by_slug: Dict[str, int] # R20: Number of cards with this theme tag
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pool_tier_by_slug: Dict[str, str] # R20: Vast/Large/Moderate/Small/Tiny
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mtime: float
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yaml_mtime_max: float
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etag: str
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@ -162,6 +167,80 @@ def _compute_etag(size: int, mtime: float, yaml_mtime: float) -> str:
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return f"{int(size)}-{int(mtime)}-{int(yaml_mtime)}"
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def _calculate_theme_pool_sizes() -> Dict[str, int]:
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"""Calculate card pool sizes for each theme from parquet data (R20).
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Returns:
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Dict mapping theme name to count of cards with that theme tag.
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"""
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pool_sizes: Dict[str, int] = {}
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try:
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# Import pandas and path_util inside function to avoid circular imports
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import pandas as pd
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import numpy as np
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from path_util import get_processed_cards_path
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parquet_path = get_processed_cards_path()
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if not Path(parquet_path).exists():
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return pool_sizes
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# Read parquet file
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df = pd.read_parquet(parquet_path)
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if 'themeTags' not in df.columns:
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return pool_sizes
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# Count cards for each theme
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for tags in df['themeTags']:
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# Check for null/NA (works for scalars only, arrays need different check)
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if tags is None or (isinstance(tags, str) and tags == ''):
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continue
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# Handle different formats: numpy array, list, string
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if isinstance(tags, np.ndarray):
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theme_list = [str(t).strip() for t in tags if t]
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elif isinstance(tags, list):
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theme_list = [str(t).strip() for t in tags if t]
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elif isinstance(tags, str):
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theme_list = [t.strip() for t in tags.split(',') if t.strip()]
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else:
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continue
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for theme in theme_list:
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if theme:
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# Normalize theme name: Strip leading/trailing spaces
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normalized = theme.strip()
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pool_sizes[normalized] = pool_sizes.get(normalized, 0) + 1
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except Exception:
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# Silently fail if parquet unavailable (e.g., during initial setup)
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pass
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return pool_sizes
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def _categorize_pool_size(count: int) -> str:
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"""Categorize pool size into tier (R20).
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Args:
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count: Number of cards with this theme tag
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Returns:
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Tier: Vast/Large/Moderate/Small/Tiny
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"""
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if count >= 500:
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return 'Vast'
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elif count >= 200:
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return 'Large'
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elif count >= 50:
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return 'Moderate'
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elif count >= 15:
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return 'Small'
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else:
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return 'Tiny'
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def load_index() -> SlugThemeIndex:
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if not _needs_reload():
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return _CACHE["index"]
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@ -174,14 +253,92 @@ def load_index() -> SlugThemeIndex:
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haystack_by_slug: Dict[str, str] = {}
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primary_color_by_slug: Dict[str, Optional[str]] = {}
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secondary_color_by_slug: Dict[str, Optional[str]] = {}
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quality_tier_by_slug: Dict[str, Optional[str]] = {} # R20
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quality_score_by_slug: Dict[str, Optional[float]] = {} # R20
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pool_size_by_slug: Dict[str, int] = {} # R20
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pool_tier_by_slug: Dict[str, str] = {} # R20
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# R20: Calculate pool sizes from parquet data
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theme_pool_sizes = _calculate_theme_pool_sizes()
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# R20: Calculate global card frequency directly to avoid circular dependency
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global_card_freq: Dict[str, int] = {}
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for t in catalog.themes:
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if t.example_cards:
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for card in t.example_cards:
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global_card_freq[card] = global_card_freq.get(card, 0) + 1
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total_themes = len(catalog.themes)
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for t in catalog.themes:
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slug = slugify(t.theme)
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slug_to_entry[slug] = t
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summary = project_summary(t)
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summary_by_slug[slug] = summary
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# Will populate quality after calculation below
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summary_by_slug[slug] = {} # Populated after quality calculation
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haystack_by_slug[slug] = "|".join([t.theme] + t.synergies).lower()
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primary_color_by_slug[slug] = t.primary_color
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secondary_color_by_slug[slug] = t.secondary_color
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# R20: Calculate quality metrics inline (avoids ThemeEditorialService circular dependency)
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# Enhanced scoring: Card count (0-30) + Uniqueness (0-40) + Description (0-20) + Curation (0-10) = 100 max
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total_points = 0.0
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max_points = 100.0
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# 1. Example card count (0-30 points, 8+ cards = max)
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card_count = len(t.example_cards) if t.example_cards else 0
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card_points = min(30.0, (card_count / 8) * 30.0)
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total_points += card_points
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# 2. Uniqueness ratio (0-40 points, cards in <25% of themes)
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if t.example_cards and total_themes > 0:
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unique_count = sum(
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1 for card in t.example_cards
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if (global_card_freq.get(card, 0) / total_themes) < 0.25
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)
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uniqueness_ratio = unique_count / len(t.example_cards)
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uniqueness_points = uniqueness_ratio * 40.0
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total_points += uniqueness_points
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# 3. Description quality (0-20 points: manual=10, rule=5, generic=0)
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if t.description_source:
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desc_bonus = {'manual': 10, 'rule': 5, 'generic': 0}.get(t.description_source, 0)
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total_points += desc_bonus
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# 4. Manual curation bonus (0-10 points if has curated_synergies)
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if hasattr(t, 'curated_synergies') and t.curated_synergies:
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total_points += 10.0
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# Normalize to 0.0-1.0 and determine tier
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normalized_score = total_points / max_points
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if normalized_score >= 0.75:
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tier = 'Excellent'
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elif normalized_score >= 0.60:
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tier = 'Good'
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elif normalized_score >= 0.40:
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tier = 'Fair'
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else:
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tier = 'Poor'
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quality_tier_by_slug[slug] = tier
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quality_score_by_slug[slug] = normalized_score
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# R20: Calculate pool size from parquet data (normalize theme name for lookup)
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normalized_theme_name = t.theme.strip()
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pool_size = theme_pool_sizes.get(normalized_theme_name, 0)
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pool_tier = _categorize_pool_size(pool_size)
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pool_size_by_slug[slug] = pool_size
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pool_tier_by_slug[slug] = pool_tier
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# R20: Now create summary with quality and pool size data
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summary = project_summary(
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t,
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quality_tier=tier,
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quality_score=normalized_score,
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pool_size=pool_size,
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pool_tier=pool_tier
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)
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summary_by_slug[slug] = summary
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yaml_map, yaml_mtime_max = _load_yaml_map()
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idx = SlugThemeIndex(
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catalog=catalog,
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@ -191,6 +348,10 @@ def load_index() -> SlugThemeIndex:
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haystack_by_slug=haystack_by_slug,
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primary_color_by_slug=primary_color_by_slug,
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secondary_color_by_slug=secondary_color_by_slug,
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quality_tier_by_slug=quality_tier_by_slug, # R20
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quality_score_by_slug=quality_score_by_slug, # R20
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pool_size_by_slug=pool_size_by_slug, # R20
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pool_tier_by_slug=pool_tier_by_slug, # R20
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mtime=CATALOG_JSON.stat().st_mtime,
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yaml_mtime_max=yaml_mtime_max,
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etag=_compute_etag(CATALOG_JSON.stat().st_size, CATALOG_JSON.stat().st_mtime, yaml_mtime_max),
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@ -284,7 +445,13 @@ def has_fallback_description(entry: ThemeEntry) -> bool:
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return False
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def project_summary(entry: ThemeEntry) -> Dict[str, Any]:
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def project_summary(
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entry: ThemeEntry,
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quality_tier: Optional[str] = None,
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quality_score: Optional[float] = None,
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pool_size: Optional[int] = None,
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pool_tier: Optional[str] = None
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) -> Dict[str, Any]:
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# Short description (snippet) for list hover / condensed display
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desc = entry.description or ""
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short_desc = desc.strip()
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@ -298,6 +465,10 @@ def project_summary(entry: ThemeEntry) -> Dict[str, Any]:
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"popularity_bucket": entry.popularity_bucket,
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"deck_archetype": entry.deck_archetype,
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"editorial_quality": entry.editorial_quality,
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"quality_tier": quality_tier, # R20: Quality tier (Excellent/Good/Fair/Poor)
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"quality_score": quality_score, # R20: Normalized quality score (0.0-1.0)
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"pool_size": pool_size, # R20: Number of cards with this theme tag
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"pool_tier": pool_tier, # R20: Pool size tier (Vast/Large/Moderate/Small/Tiny)
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"description": entry.description,
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"short_description": short_desc,
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"synergies": entry.synergies,
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@ -317,7 +488,7 @@ def _split_synergies(slug: str, entry: ThemeEntry, yaml_map: Dict[str, Dict[str,
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}
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def project_detail(slug: str, entry: ThemeEntry, yaml_map: Dict[str, Dict[str, Any]], uncapped: bool = False) -> Dict[str, Any]:
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def project_detail(slug: str, entry: ThemeEntry, yaml_map: Dict[str, Dict[str, Any]], idx: 'SlugThemeIndex', uncapped: bool = False) -> Dict[str, Any]:
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seg = _split_synergies(slug, entry, yaml_map)
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uncapped_synergies: Optional[List[str]] = None
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if uncapped:
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@ -330,7 +501,18 @@ def project_detail(slug: str, entry: ThemeEntry, yaml_map: Dict[str, Dict[str, A
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full.append(s)
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seen.add(s)
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uncapped_synergies = full
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d = project_summary(entry)
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# R20: Look up quality and pool metrics from index (not stored on ThemeEntry)
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quality_tier = idx.quality_tier_by_slug.get(slug)
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quality_score = idx.quality_score_by_slug.get(slug)
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pool_size = idx.pool_size_by_slug.get(slug, 0)
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pool_tier = idx.pool_tier_by_slug.get(slug, "Tiny")
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d = project_summary(
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entry,
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quality_tier=quality_tier,
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quality_score=quality_score,
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pool_size=pool_size,
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pool_tier=pool_tier
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)
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d.update({
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"curated_synergies": seg["curated"],
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"enforced_synergies": seg["enforced"],
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@ -449,6 +631,7 @@ def summaries_for_slugs(idx: SlugThemeIndex, slugs: Iterable[str]) -> List[Dict[
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for s in slugs:
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summ = idx.summary_by_slug.get(s)
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if summ:
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# R20: Summary already contains quality_tier and quality_score from load_index
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out.append(summ.copy()) # shallow copy so route can pop diag-only fields
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return out
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