#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ UI/UX Pro Max Core - BM25 search engine for UI/UX style guides """ import csv import re from pathlib import Path from math import log from collections import defaultdict # ============ CONFIGURATION ============ DATA_DIR = Path(__file__).parent.parent / "data" MAX_RESULTS = 3 CSV_CONFIG = { "style": { "file": "styles.csv", "search_cols": ["Style Category", "Keywords", "Best For", "Type"], "output_cols": ["Style Category", "Type", "Keywords", "Primary Colors", "Effects & Animation", "Best For", "Performance", "Accessibility", "Framework Compatibility", "Complexity"] }, "prompt": { "file": "prompts.csv", "search_cols": ["Style Category", "AI Prompt Keywords (Copy-Paste Ready)", "CSS/Technical Keywords"], "output_cols": ["Style Category", "AI Prompt Keywords (Copy-Paste Ready)", "CSS/Technical Keywords", "Implementation Checklist"] }, "color": { "file": "colors.csv", "search_cols": ["Product Type", "Keywords", "Notes"], "output_cols": ["Product Type", "Keywords", "Primary (Hex)", "Secondary (Hex)", "CTA (Hex)", "Background (Hex)", "Text (Hex)", "Border (Hex)", "Notes"] }, "chart": { "file": "charts.csv", "search_cols": ["Data Type", "Keywords", "Best Chart Type", "Accessibility Notes"], "output_cols": ["Data Type", "Keywords", "Best Chart Type", "Secondary Options", "Color Guidance", "Accessibility Notes", "Library Recommendation", "Interactive Level"] }, "landing": { "file": "landing.csv", "search_cols": ["Pattern Name", "Keywords", "Conversion Optimization", "Section Order"], "output_cols": ["Pattern Name", "Keywords", "Section Order", "Primary CTA Placement", "Color Strategy", "Conversion Optimization"] }, "product": { "file": "products.csv", "search_cols": ["Product Type", "Keywords", "Primary Style Recommendation", "Key Considerations"], "output_cols": ["Product Type", "Keywords", "Primary Style Recommendation", "Secondary Styles", "Landing Page Pattern", "Dashboard Style (if applicable)", "Color Palette Focus"] }, "ux": { "file": "ux-guidelines.csv", "search_cols": ["Category", "Issue", "Description", "Platform"], "output_cols": ["Category", "Issue", "Platform", "Description", "Do", "Don't", "Code Example Good", "Code Example Bad", "Severity"] }, "typography": { "file": "typography.csv", "search_cols": ["Font Pairing Name", "Category", "Mood/Style Keywords", "Best For", "Heading Font", "Body Font"], "output_cols": ["Font Pairing Name", "Category", "Heading Font", "Body Font", "Mood/Style Keywords", "Best For", "Google Fonts URL", "CSS Import", "Tailwind Config", "Notes"] } } STACK_CONFIG = { "html-tailwind": {"file": "stacks/html-tailwind.csv"}, "react": {"file": "stacks/react.csv"}, "nextjs": {"file": "stacks/nextjs.csv"}, "vue": {"file": "stacks/vue.csv"}, "svelte": {"file": "stacks/svelte.csv"}, "swiftui": {"file": "stacks/swiftui.csv"}, "react-native": {"file": "stacks/react-native.csv"}, "flutter": {"file": "stacks/flutter.csv"} } # Common columns for all stacks _STACK_COLS = { "search_cols": ["Category", "Guideline", "Description", "Do", "Don't"], "output_cols": ["Category", "Guideline", "Description", "Do", "Don't", "Code Good", "Code Bad", "Severity", "Docs URL"] } AVAILABLE_STACKS = list(STACK_CONFIG.keys()) # ============ BM25 IMPLEMENTATION ============ class BM25: """BM25 ranking algorithm for text search""" def __init__(self, k1=1.5, b=0.75): self.k1 = k1 self.b = b self.corpus = [] self.doc_lengths = [] self.avgdl = 0 self.idf = {} self.doc_freqs = defaultdict(int) self.N = 0 def tokenize(self, text): """Lowercase, split, remove punctuation, filter short words""" text = re.sub(r'[^\w\s]', ' ', str(text).lower()) return [w for w in text.split() if len(w) > 2] def fit(self, documents): """Build BM25 index from documents""" self.corpus = [self.tokenize(doc) for doc in documents] self.N = len(self.corpus) if self.N == 0: return self.doc_lengths = [len(doc) for doc in self.corpus] self.avgdl = sum(self.doc_lengths) / self.N for doc in self.corpus: seen = set() for word in doc: if word not in seen: self.doc_freqs[word] += 1 seen.add(word) for word, freq in self.doc_freqs.items(): self.idf[word] = log((self.N - freq + 0.5) / (freq + 0.5) + 1) def score(self, query): """Score all documents against query""" query_tokens = self.tokenize(query) scores = [] for idx, doc in enumerate(self.corpus): score = 0 doc_len = self.doc_lengths[idx] term_freqs = defaultdict(int) for word in doc: term_freqs[word] += 1 for token in query_tokens: if token in self.idf: tf = term_freqs[token] idf = self.idf[token] numerator = tf * (self.k1 + 1) denominator = tf + self.k1 * (1 - self.b + self.b * doc_len / self.avgdl) score += idf * numerator / denominator scores.append((idx, score)) return sorted(scores, key=lambda x: x[1], reverse=True) # ============ SEARCH FUNCTIONS ============ def _load_csv(filepath): """Load CSV and return list of dicts""" with open(filepath, 'r', encoding='utf-8') as f: return list(csv.DictReader(f)) def _search_csv(filepath, search_cols, output_cols, query, max_results): """Core search function using BM25""" if not filepath.exists(): return [] data = _load_csv(filepath) # Build documents from search columns documents = [" ".join(str(row.get(col, "")) for col in search_cols) for row in data] # BM25 search bm25 = BM25() bm25.fit(documents) ranked = bm25.score(query) # Get top results with score > 0 results = [] for idx, score in ranked[:max_results]: if score > 0: row = data[idx] results.append({col: row.get(col, "") for col in output_cols if col in row}) return results def detect_domain(query): """Auto-detect the most relevant domain from query""" query_lower = query.lower() domain_keywords = { "color": ["color", "palette", "hex", "#", "rgb"], "chart": ["chart", "graph", "visualization", "trend", "bar", "pie", "scatter", "heatmap", "funnel"], "landing": ["landing", "page", "cta", "conversion", "hero", "testimonial", "pricing", "section"], "product": ["saas", "ecommerce", "e-commerce", "fintech", "healthcare", "gaming", "portfolio", "crypto", "dashboard"], "prompt": ["prompt", "css", "implementation", "variable", "checklist", "tailwind"], "style": ["style", "design", "ui", "minimalism", "glassmorphism", "neumorphism", "brutalism", "dark mode", "flat", "aurora"], "ux": ["ux", "usability", "accessibility", "wcag", "touch", "scroll", "animation", "keyboard", "navigation", "mobile"], "typography": ["font", "typography", "heading", "serif", "sans"] } scores = {domain: sum(1 for kw in keywords if kw in query_lower) for domain, keywords in domain_keywords.items()} best = max(scores, key=scores.get) return best if scores[best] > 0 else "style" def search(query, domain=None, max_results=MAX_RESULTS): """Main search function with auto-domain detection""" if domain is None: domain = detect_domain(query) config = CSV_CONFIG.get(domain, CSV_CONFIG["style"]) filepath = DATA_DIR / config["file"] if not filepath.exists(): return {"error": f"File not found: {filepath}", "domain": domain} results = _search_csv(filepath, config["search_cols"], config["output_cols"], query, max_results) return { "domain": domain, "query": query, "file": config["file"], "count": len(results), "results": results } def search_stack(query, stack, max_results=MAX_RESULTS): """Search stack-specific guidelines""" if stack not in STACK_CONFIG: return {"error": f"Unknown stack: {stack}. Available: {', '.join(AVAILABLE_STACKS)}"} filepath = DATA_DIR / STACK_CONFIG[stack]["file"] if not filepath.exists(): return {"error": f"Stack file not found: {filepath}", "stack": stack} results = _search_csv(filepath, _STACK_COLS["search_cols"], _STACK_COLS["output_cols"], query, max_results) return { "domain": "stack", "stack": stack, "query": query, "file": STACK_CONFIG[stack]["file"], "count": len(results), "results": results }