Kunthawat Greethong
8a0edd225d
- Extract 9 nested skills from website-creator/ to root skills/ - Remove duplicate seo-analyzers, seo-geo, seo-multi-channel from website-creator - Add new ui-ux-pro-max skill with full UI/UX data - Update install-skills.sh to sync properly - Remove .DS_Store artifacts Moved skills: - api-and-interface-design - banner-design - brand - design-system - design - frontend-ui-engineering - slides - spec-driven-development - ui-styling
454 lines
14 KiB
Python
Executable File
454 lines
14 KiB
Python
Executable File
#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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"""
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Slide Search Core - BM25 search engine for slide design databases
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"""
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import csv
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import re
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from pathlib import Path
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from math import log
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from collections import defaultdict
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# ============ CONFIGURATION ============
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DATA_DIR = Path(__file__).parent.parent / "data"
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MAX_RESULTS = 3
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CSV_CONFIG = {
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"strategy": {
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"file": "slide-strategies.csv",
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"search_cols": ["strategy_name", "keywords", "goal", "audience", "narrative_arc"],
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"output_cols": ["strategy_name", "keywords", "slide_count", "structure", "goal", "audience", "tone", "narrative_arc", "sources"]
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},
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"layout": {
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"file": "slide-layouts.csv",
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"search_cols": ["layout_name", "keywords", "use_case", "recommended_for"],
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"output_cols": ["layout_name", "keywords", "use_case", "content_zones", "visual_weight", "cta_placement", "recommended_for", "avoid_for", "css_structure"]
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},
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"copy": {
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"file": "slide-copy.csv",
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"search_cols": ["formula_name", "keywords", "use_case", "emotion_trigger", "slide_type"],
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"output_cols": ["formula_name", "keywords", "components", "use_case", "example_template", "emotion_trigger", "slide_type", "source"]
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},
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"chart": {
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"file": "slide-charts.csv",
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"search_cols": ["chart_type", "keywords", "best_for", "when_to_use", "slide_context"],
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"output_cols": ["chart_type", "keywords", "best_for", "data_type", "when_to_use", "when_to_avoid", "max_categories", "slide_context", "css_implementation", "accessibility_notes"]
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}
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}
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AVAILABLE_DOMAINS = list(CSV_CONFIG.keys())
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# ============ BM25 IMPLEMENTATION ============
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class BM25:
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"""BM25 ranking algorithm for text search"""
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def __init__(self, k1=1.5, b=0.75):
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self.k1 = k1
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self.b = b
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self.corpus = []
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self.doc_lengths = []
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self.avgdl = 0
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self.idf = {}
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self.doc_freqs = defaultdict(int)
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self.N = 0
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def tokenize(self, text):
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"""Lowercase, split, remove punctuation, filter short words"""
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text = re.sub(r'[^\w\s]', ' ', str(text).lower())
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return [w for w in text.split() if len(w) > 2]
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def fit(self, documents):
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"""Build BM25 index from documents"""
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self.corpus = [self.tokenize(doc) for doc in documents]
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self.N = len(self.corpus)
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if self.N == 0:
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return
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self.doc_lengths = [len(doc) for doc in self.corpus]
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self.avgdl = sum(self.doc_lengths) / self.N
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for doc in self.corpus:
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seen = set()
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for word in doc:
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if word not in seen:
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self.doc_freqs[word] += 1
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seen.add(word)
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for word, freq in self.doc_freqs.items():
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self.idf[word] = log((self.N - freq + 0.5) / (freq + 0.5) + 1)
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def score(self, query):
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"""Score all documents against query"""
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query_tokens = self.tokenize(query)
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scores = []
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for idx, doc in enumerate(self.corpus):
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score = 0
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doc_len = self.doc_lengths[idx]
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term_freqs = defaultdict(int)
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for word in doc:
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term_freqs[word] += 1
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for token in query_tokens:
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if token in self.idf:
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tf = term_freqs[token]
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idf = self.idf[token]
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numerator = tf * (self.k1 + 1)
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denominator = tf + self.k1 * (1 - self.b + self.b * doc_len / self.avgdl)
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score += idf * numerator / denominator
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scores.append((idx, score))
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return sorted(scores, key=lambda x: x[1], reverse=True)
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# ============ SEARCH FUNCTIONS ============
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def _load_csv(filepath):
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"""Load CSV and return list of dicts"""
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with open(filepath, 'r', encoding='utf-8') as f:
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return list(csv.DictReader(f))
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def _search_csv(filepath, search_cols, output_cols, query, max_results):
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"""Core search function using BM25"""
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if not filepath.exists():
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return []
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data = _load_csv(filepath)
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# Build documents from search columns
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documents = [" ".join(str(row.get(col, "")) for col in search_cols) for row in data]
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# BM25 search
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bm25 = BM25()
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bm25.fit(documents)
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ranked = bm25.score(query)
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# Get top results with score > 0
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results = []
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for idx, score in ranked[:max_results]:
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if score > 0:
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row = data[idx]
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results.append({col: row.get(col, "") for col in output_cols if col in row})
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return results
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def detect_domain(query):
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"""Auto-detect the most relevant domain from query"""
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query_lower = query.lower()
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domain_keywords = {
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"strategy": ["pitch", "deck", "investor", "yc", "seed", "series", "demo", "sales", "webinar",
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"conference", "board", "qbr", "all-hands", "duarte", "kawasaki", "structure"],
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"layout": ["slide", "layout", "grid", "column", "title", "hero", "section", "cta",
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"screenshot", "quote", "timeline", "comparison", "pricing", "team"],
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"copy": ["headline", "copy", "formula", "aida", "pas", "hook", "cta", "benefit",
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"objection", "proof", "testimonial", "urgency", "scarcity"],
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"chart": ["chart", "graph", "bar", "line", "pie", "funnel", "metrics", "data",
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"visualization", "kpi", "trend", "comparison", "heatmap", "gauge"]
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}
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scores = {domain: sum(1 for kw in keywords if kw in query_lower) for domain, keywords in domain_keywords.items()}
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best = max(scores, key=scores.get)
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return best if scores[best] > 0 else "strategy"
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def search(query, domain=None, max_results=MAX_RESULTS):
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"""Main search function with auto-domain detection"""
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if domain is None:
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domain = detect_domain(query)
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config = CSV_CONFIG.get(domain, CSV_CONFIG["strategy"])
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filepath = DATA_DIR / config["file"]
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if not filepath.exists():
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return {"error": f"File not found: {filepath}", "domain": domain}
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results = _search_csv(filepath, config["search_cols"], config["output_cols"], query, max_results)
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return {
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"domain": domain,
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"query": query,
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"file": config["file"],
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"count": len(results),
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"results": results
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}
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def search_all(query, max_results=2):
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"""Search across all domains for comprehensive results"""
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all_results = {}
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for domain in AVAILABLE_DOMAINS:
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result = search(query, domain, max_results)
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if result.get("count", 0) > 0:
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all_results[domain] = result
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return all_results
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# ============ CONTEXTUAL SEARCH (Premium Slide System) ============
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# New CSV configurations for decision system
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DECISION_CSV_CONFIG = {
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"layout-logic": {
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"file": "slide-layout-logic.csv",
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"key_col": "goal"
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},
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"typography": {
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"file": "slide-typography.csv",
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"key_col": "content_type"
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},
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"color-logic": {
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"file": "slide-color-logic.csv",
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"key_col": "emotion"
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},
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"backgrounds": {
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"file": "slide-backgrounds.csv",
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"key_col": "slide_type"
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}
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}
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def _load_decision_csv(csv_type):
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"""Load a decision CSV and return as dict keyed by primary column."""
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config = DECISION_CSV_CONFIG.get(csv_type)
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if not config:
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return {}
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filepath = DATA_DIR / config["file"]
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if not filepath.exists():
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return {}
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data = _load_csv(filepath)
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return {row[config["key_col"]]: row for row in data if config["key_col"] in row}
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def get_layout_for_goal(goal, previous_emotion=None):
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"""
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Get layout recommendation based on slide goal.
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Uses slide-layout-logic.csv for decision.
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"""
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layouts = _load_decision_csv("layout-logic")
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row = layouts.get(goal, layouts.get("features", {}))
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result = dict(row) if row else {}
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# Apply pattern-breaking logic
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if result.get("break_pattern") == "true" and previous_emotion:
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result["_pattern_break"] = True
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result["_contrast_with"] = previous_emotion
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return result
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def get_typography_for_slide(slide_type, has_metrics=False, has_quote=False):
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"""
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Get typography recommendation based on slide content.
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Uses slide-typography.csv for decision.
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"""
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typography = _load_decision_csv("typography")
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if has_metrics:
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return typography.get("metric-callout", {})
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if has_quote:
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return typography.get("quote-block", {})
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# Map slide types to typography
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type_map = {
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"hero": "hero-statement",
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"hook": "hero-statement",
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"title": "title-only",
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"problem": "subtitle-heavy",
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"agitation": "metric-callout",
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"solution": "subtitle-heavy",
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"features": "feature-grid",
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"proof": "metric-callout",
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"traction": "data-insight",
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"social": "quote-block",
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"testimonial": "testimonial",
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"pricing": "pricing",
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"team": "team",
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"cta": "cta-action",
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"comparison": "comparison",
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"timeline": "timeline",
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}
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content_type = type_map.get(slide_type, "feature-grid")
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return typography.get(content_type, {})
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def get_color_for_emotion(emotion):
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"""
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Get color treatment based on emotional beat.
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Uses slide-color-logic.csv for decision.
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"""
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colors = _load_decision_csv("color-logic")
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return colors.get(emotion, colors.get("clarity", {}))
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def get_background_config(slide_type):
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"""
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Get background image configuration.
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Uses slide-backgrounds.csv for decision.
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"""
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backgrounds = _load_decision_csv("backgrounds")
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return backgrounds.get(slide_type, {})
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def should_use_full_bleed(slide_index, total_slides, emotion):
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"""
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Determine if slide should use full-bleed background.
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Premium decks use 2-3 full-bleed slides strategically.
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Rules:
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1. Never consecutive full-bleed
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2. One in first third, one in middle, one at end
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3. Reserved for high-emotion beats (hope, urgency, fear)
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"""
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high_emotion_beats = ["hope", "urgency", "fear", "curiosity"]
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if emotion not in high_emotion_beats:
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return False
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if total_slides < 3:
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return False
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third = total_slides // 3
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strategic_positions = [1, third, third * 2, total_slides - 1]
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return slide_index in strategic_positions
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def calculate_pattern_break(slide_index, total_slides, previous_emotion=None):
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"""
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Determine if this slide should break the visual pattern.
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Used for emotional contrast (Duarte Sparkline technique).
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"""
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# Pattern breaks at strategic positions
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if total_slides < 5:
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return False
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# Break at 1/3 and 2/3 points
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third = total_slides // 3
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if slide_index in [third, third * 2]:
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return True
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# Break when switching between frustration and hope
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contrasting_emotions = {
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"frustration": ["hope", "relief"],
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"hope": ["frustration", "fear"],
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"fear": ["hope", "relief"],
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}
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if previous_emotion in contrasting_emotions:
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return True
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return False
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def search_with_context(query, slide_position=1, total_slides=9, previous_emotion=None):
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"""
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Enhanced search that considers deck context.
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Args:
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query: Search query
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slide_position: Current slide index (1-based)
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total_slides: Total slides in deck
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previous_emotion: Emotion of previous slide (for contrast)
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Returns:
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Search results enriched with contextual recommendations
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"""
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# Get base results from existing BM25 search
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base_results = search_all(query, max_results=2)
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# Detect likely slide goal from query
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goal = detect_domain(query.lower())
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if "problem" in query.lower():
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goal = "problem"
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elif "solution" in query.lower():
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goal = "solution"
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elif "cta" in query.lower() or "call to action" in query.lower():
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goal = "cta"
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elif "hook" in query.lower() or "title" in query.lower():
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goal = "hook"
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elif "traction" in query.lower() or "metric" in query.lower():
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goal = "traction"
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# Enrich with contextual recommendations
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context = {
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"slide_position": slide_position,
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"total_slides": total_slides,
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"previous_emotion": previous_emotion,
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"inferred_goal": goal,
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}
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# Get layout recommendation
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layout = get_layout_for_goal(goal, previous_emotion)
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if layout:
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context["recommended_layout"] = layout.get("layout_pattern")
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context["layout_direction"] = layout.get("direction")
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context["visual_weight"] = layout.get("visual_weight")
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context["use_background_image"] = layout.get("use_bg_image") == "true"
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# Get typography recommendation
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typography = get_typography_for_slide(goal)
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if typography:
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context["typography"] = {
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"primary_size": typography.get("primary_size"),
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"secondary_size": typography.get("secondary_size"),
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"weight_contrast": typography.get("weight_contrast"),
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}
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# Get color treatment
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emotion = layout.get("emotion", "clarity") if layout else "clarity"
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color = get_color_for_emotion(emotion)
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if color:
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context["color_treatment"] = {
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"background": color.get("background"),
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"text_color": color.get("text_color"),
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"accent_usage": color.get("accent_usage"),
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"card_style": color.get("card_style"),
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}
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# Calculate pattern breaking
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context["should_break_pattern"] = calculate_pattern_break(
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slide_position, total_slides, previous_emotion
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)
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context["should_use_full_bleed"] = should_use_full_bleed(
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slide_position, total_slides, emotion
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)
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# Get background config if needed
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if context.get("use_background_image"):
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bg_config = get_background_config(goal)
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if bg_config:
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context["background"] = {
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"image_category": bg_config.get("image_category"),
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"overlay_style": bg_config.get("overlay_style"),
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"search_keywords": bg_config.get("search_keywords"),
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}
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# Suggested animation classes
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animation_map = {
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"hook": "animate-fade-up",
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"problem": "animate-fade-up",
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"agitation": "animate-count animate-stagger",
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"solution": "animate-scale",
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"features": "animate-stagger",
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"traction": "animate-chart animate-count",
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"proof": "animate-stagger-scale",
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"social": "animate-fade-up",
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"cta": "animate-pulse",
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}
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context["animation_class"] = animation_map.get(goal, "animate-fade-up")
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return {
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"query": query,
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"context": context,
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"base_results": base_results,
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}
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