237 lines
8.8 KiB
Python
237 lines
8.8 KiB
Python
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#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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"""
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UI/UX Pro Max Core - BM25 search engine for UI/UX style guides
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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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"style": {
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"file": "styles.csv",
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"search_cols": ["Style Category", "Keywords", "Best For", "Type"],
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"output_cols": ["Style Category", "Type", "Keywords", "Primary Colors", "Effects & Animation", "Best For", "Performance", "Accessibility", "Framework Compatibility", "Complexity"]
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},
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"prompt": {
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"file": "prompts.csv",
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"search_cols": ["Style Category", "AI Prompt Keywords (Copy-Paste Ready)", "CSS/Technical Keywords"],
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"output_cols": ["Style Category", "AI Prompt Keywords (Copy-Paste Ready)", "CSS/Technical Keywords", "Implementation Checklist"]
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},
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"color": {
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"file": "colors.csv",
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"search_cols": ["Product Type", "Keywords", "Notes"],
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"output_cols": ["Product Type", "Keywords", "Primary (Hex)", "Secondary (Hex)", "CTA (Hex)", "Background (Hex)", "Text (Hex)", "Border (Hex)", "Notes"]
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},
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"chart": {
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"file": "charts.csv",
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"search_cols": ["Data Type", "Keywords", "Best Chart Type", "Accessibility Notes"],
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"output_cols": ["Data Type", "Keywords", "Best Chart Type", "Secondary Options", "Color Guidance", "Accessibility Notes", "Library Recommendation", "Interactive Level"]
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},
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"landing": {
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"file": "landing.csv",
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"search_cols": ["Pattern Name", "Keywords", "Conversion Optimization", "Section Order"],
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"output_cols": ["Pattern Name", "Keywords", "Section Order", "Primary CTA Placement", "Color Strategy", "Conversion Optimization"]
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},
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"product": {
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"file": "products.csv",
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"search_cols": ["Product Type", "Keywords", "Primary Style Recommendation", "Key Considerations"],
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"output_cols": ["Product Type", "Keywords", "Primary Style Recommendation", "Secondary Styles", "Landing Page Pattern", "Dashboard Style (if applicable)", "Color Palette Focus"]
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},
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"ux": {
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"file": "ux-guidelines.csv",
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"search_cols": ["Category", "Issue", "Description", "Platform"],
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"output_cols": ["Category", "Issue", "Platform", "Description", "Do", "Don't", "Code Example Good", "Code Example Bad", "Severity"]
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},
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"typography": {
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"file": "typography.csv",
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"search_cols": ["Font Pairing Name", "Category", "Mood/Style Keywords", "Best For", "Heading Font", "Body Font"],
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"output_cols": ["Font Pairing Name", "Category", "Heading Font", "Body Font", "Mood/Style Keywords", "Best For", "Google Fonts URL", "CSS Import", "Tailwind Config", "Notes"]
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}
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}
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STACK_CONFIG = {
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"html-tailwind": {"file": "stacks/html-tailwind.csv"},
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"react": {"file": "stacks/react.csv"},
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"nextjs": {"file": "stacks/nextjs.csv"},
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"vue": {"file": "stacks/vue.csv"},
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"svelte": {"file": "stacks/svelte.csv"},
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"swiftui": {"file": "stacks/swiftui.csv"},
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"react-native": {"file": "stacks/react-native.csv"},
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"flutter": {"file": "stacks/flutter.csv"}
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}
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# Common columns for all stacks
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_STACK_COLS = {
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"search_cols": ["Category", "Guideline", "Description", "Do", "Don't"],
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"output_cols": ["Category", "Guideline", "Description", "Do", "Don't", "Code Good", "Code Bad", "Severity", "Docs URL"]
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}
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AVAILABLE_STACKS = list(STACK_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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"color": ["color", "palette", "hex", "#", "rgb"],
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"chart": ["chart", "graph", "visualization", "trend", "bar", "pie", "scatter", "heatmap", "funnel"],
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"landing": ["landing", "page", "cta", "conversion", "hero", "testimonial", "pricing", "section"],
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"product": ["saas", "ecommerce", "e-commerce", "fintech", "healthcare", "gaming", "portfolio", "crypto", "dashboard"],
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"prompt": ["prompt", "css", "implementation", "variable", "checklist", "tailwind"],
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"style": ["style", "design", "ui", "minimalism", "glassmorphism", "neumorphism", "brutalism", "dark mode", "flat", "aurora"],
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"ux": ["ux", "usability", "accessibility", "wcag", "touch", "scroll", "animation", "keyboard", "navigation", "mobile"],
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"typography": ["font", "typography", "heading", "serif", "sans"]
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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 "style"
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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["style"])
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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_stack(query, stack, max_results=MAX_RESULTS):
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"""Search stack-specific guidelines"""
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if stack not in STACK_CONFIG:
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return {"error": f"Unknown stack: {stack}. Available: {', '.join(AVAILABLE_STACKS)}"}
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filepath = DATA_DIR / STACK_CONFIG[stack]["file"]
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if not filepath.exists():
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return {"error": f"Stack file not found: {filepath}", "stack": stack}
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results = _search_csv(filepath, _STACK_COLS["search_cols"], _STACK_COLS["output_cols"], query, max_results)
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return {
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"domain": "stack",
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"stack": stack,
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"query": query,
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"file": STACK_CONFIG[stack]["file"],
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"count": len(results),
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"results": results
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}
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