fix(用户管理): 修复用户列表页面加载失败问题
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修复用户列表页面加载失败导致测试超时的问题,确保页面元素正确渲染
This commit is contained in:
iven
2026-04-19 08:46:28 +08:00
parent 0ee9d22634
commit 841766b168
174 changed files with 26366 additions and 675 deletions

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#!/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", "AI Prompt Keywords"],
"output_cols": ["Style Category", "Type", "Keywords", "Primary Colors", "Effects & Animation", "Best For", "Light Mode ✓", "Dark Mode ✓", "Performance", "Accessibility", "Framework Compatibility", "Complexity", "AI Prompt Keywords", "CSS/Technical Keywords", "Implementation Checklist", "Design System Variables"]
},
"color": {
"file": "colors.csv",
"search_cols": ["Product Type", "Notes"],
"output_cols": ["Product Type", "Primary", "On Primary", "Secondary", "On Secondary", "Accent", "On Accent", "Background", "Foreground", "Card", "Card Foreground", "Muted", "Muted Foreground", "Border", "Destructive", "On Destructive", "Ring", "Notes"]
},
"chart": {
"file": "charts.csv",
"search_cols": ["Data Type", "Keywords", "Best Chart Type", "When to Use", "When NOT to Use", "Accessibility Notes"],
"output_cols": ["Data Type", "Keywords", "Best Chart Type", "Secondary Options", "When to Use", "When NOT to Use", "Data Volume Threshold", "Color Guidance", "Accessibility Grade", "Accessibility Notes", "A11y Fallback", "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"]
},
"icons": {
"file": "icons.csv",
"search_cols": ["Category", "Icon Name", "Keywords", "Best For"],
"output_cols": ["Category", "Icon Name", "Keywords", "Library", "Import Code", "Usage", "Best For", "Style"]
},
"react": {
"file": "react-performance.csv",
"search_cols": ["Category", "Issue", "Keywords", "Description"],
"output_cols": ["Category", "Issue", "Platform", "Description", "Do", "Don't", "Code Example Good", "Code Example Bad", "Severity"]
},
"web": {
"file": "app-interface.csv",
"search_cols": ["Category", "Issue", "Keywords", "Description"],
"output_cols": ["Category", "Issue", "Platform", "Description", "Do", "Don't", "Code Example Good", "Code Example Bad", "Severity"]
},
"google-fonts": {
"file": "google-fonts.csv",
"search_cols": ["Family", "Category", "Stroke", "Classifications", "Keywords", "Subsets", "Designers"],
"output_cols": ["Family", "Category", "Stroke", "Classifications", "Styles", "Variable Axes", "Subsets", "Designers", "Popularity Rank", "Google Fonts URL"]
}
}
STACK_CONFIG = {
"react": {"file": "stacks/react.csv"},
"nextjs": {"file": "stacks/nextjs.csv"},
"vue": {"file": "stacks/vue.csv"},
"svelte": {"file": "stacks/svelte.csv"},
"astro": {"file": "stacks/astro.csv"},
"swiftui": {"file": "stacks/swiftui.csv"},
"react-native": {"file": "stacks/react-native.csv"},
"flutter": {"file": "stacks/flutter.csv"},
"nuxtjs": {"file": "stacks/nuxtjs.csv"},
"nuxt-ui": {"file": "stacks/nuxt-ui.csv"},
"html-tailwind": {"file": "stacks/html-tailwind.csv"},
"shadcn": {"file": "stacks/shadcn.csv"},
"jetpack-compose": {"file": "stacks/jetpack-compose.csv"},
"threejs": {"file": "stacks/threejs.csv"},
"angular": {"file": "stacks/angular.csv"},
"laravel": {"file": "stacks/laravel.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", "token", "semantic", "accent", "destructive", "muted", "foreground"],
"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", "fitness", "restaurant", "hotel", "travel", "music", "education", "learning", "legal", "insurance", "medical", "beauty", "pharmacy", "dental", "pet", "dating", "wedding", "recipe", "delivery", "ride", "booking", "calendar", "timer", "tracker", "diary", "note", "chat", "messenger", "crm", "invoice", "parking", "transit", "vpn", "alarm", "weather", "sleep", "meditation", "fasting", "habit", "grocery", "meme", "wardrobe", "plant care", "reading", "flashcard", "puzzle", "trivia", "arcade", "photography", "streaming", "podcast", "newsletter", "marketplace", "freelancer", "coworking", "airline", "museum", "theater", "church", "non-profit", "charity", "kindergarten", "daycare", "senior care", "veterinary", "florist", "bakery", "brewery", "construction", "automotive", "real estate", "logistics", "agriculture", "coding bootcamp"],
"style": ["style", "design", "ui", "minimalism", "glassmorphism", "neumorphism", "brutalism", "dark mode", "flat", "aurora", "prompt", "css", "implementation", "variable", "checklist", "tailwind"],
"ux": ["ux", "usability", "accessibility", "wcag", "touch", "scroll", "animation", "keyboard", "navigation", "mobile"],
"typography": ["font pairing", "typography pairing", "heading font", "body font"],
"google-fonts": ["google font", "font family", "font weight", "font style", "variable font", "noto", "font for", "find font", "font subset", "font language", "monospace font", "serif font", "sans serif font", "display font", "handwriting font", "font", "typography", "serif", "sans"],
"icons": ["icon", "icons", "lucide", "heroicons", "symbol", "glyph", "pictogram", "svg icon"],
"react": ["react", "next.js", "nextjs", "suspense", "memo", "usecallback", "useeffect", "rerender", "bundle", "waterfall", "barrel", "dynamic import", "rsc", "server component"],
"web": ["aria", "focus", "outline", "semantic", "virtualize", "autocomplete", "form", "input type", "preconnect"]
}
scores = {domain: sum(1 for kw in keywords if re.search(r'\b' + re.escape(kw) + r'\b', 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
}

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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
UI/UX Pro Max Search - BM25 search engine for UI/UX style guides
Usage: python search.py "<query>" [--domain <domain>] [--stack <stack>] [--max-results 3]
python search.py "<query>" --design-system [-p "Project Name"]
python search.py "<query>" --design-system --persist [-p "Project Name"] [--page "dashboard"]
Domains: style, prompt, color, chart, landing, product, ux, typography, google-fonts
Stacks: react, nextjs, vue, svelte, astro, swiftui, react-native, flutter, nuxtjs, nuxt-ui, html-tailwind, shadcn, jetpack-compose, threejs
Persistence (Master + Overrides pattern):
--persist Save design system to design-system/MASTER.md
--page Also create a page-specific override file in design-system/pages/
"""
import argparse
import sys
import io
from core import CSV_CONFIG, AVAILABLE_STACKS, MAX_RESULTS, search, search_stack
from design_system import generate_design_system, persist_design_system
# Force UTF-8 for stdout/stderr to handle emojis on Windows (cp1252 default)
if sys.stdout.encoding and sys.stdout.encoding.lower() != 'utf-8':
sys.stdout = io.TextIOWrapper(sys.stdout.buffer, encoding='utf-8')
if sys.stderr.encoding and sys.stderr.encoding.lower() != 'utf-8':
sys.stderr = io.TextIOWrapper(sys.stderr.buffer, encoding='utf-8')
def format_output(result):
"""Format results for Claude consumption (token-optimized)"""
if "error" in result:
return f"Error: {result['error']}"
output = []
if result.get("stack"):
output.append(f"## UI Pro Max Stack Guidelines")
output.append(f"**Stack:** {result['stack']} | **Query:** {result['query']}")
else:
output.append(f"## UI Pro Max Search Results")
output.append(f"**Domain:** {result['domain']} | **Query:** {result['query']}")
output.append(f"**Source:** {result['file']} | **Found:** {result['count']} results\n")
for i, row in enumerate(result['results'], 1):
output.append(f"### Result {i}")
for key, value in row.items():
value_str = str(value)
if len(value_str) > 300:
value_str = value_str[:300] + "..."
output.append(f"- **{key}:** {value_str}")
output.append("")
return "\n".join(output)
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="UI Pro Max Search")
parser.add_argument("query", help="Search query")
parser.add_argument("--domain", "-d", choices=list(CSV_CONFIG.keys()), help="Search domain")
parser.add_argument("--stack", "-s", choices=AVAILABLE_STACKS, help=f"Stack-specific search. Available: {', '.join(AVAILABLE_STACKS)}")
parser.add_argument("--max-results", "-n", type=int, default=MAX_RESULTS, help="Max results (default: 3)")
parser.add_argument("--json", action="store_true", help="Output as JSON")
# Design system generation
parser.add_argument("--design-system", "-ds", action="store_true", help="Generate complete design system recommendation")
parser.add_argument("--project-name", "-p", type=str, default=None, help="Project name for design system output")
parser.add_argument("--format", "-f", choices=["ascii", "markdown"], default="ascii", help="Output format for design system")
# Persistence (Master + Overrides pattern)
parser.add_argument("--persist", action="store_true", help="Save design system to design-system/MASTER.md (creates hierarchical structure)")
parser.add_argument("--page", type=str, default=None, help="Create page-specific override file in design-system/pages/")
parser.add_argument("--output-dir", "-o", type=str, default=None, help="Output directory for persisted files (default: current directory)")
args = parser.parse_args()
# Design system takes priority
if args.design_system:
result = generate_design_system(
args.query,
args.project_name,
args.format,
persist=args.persist,
page=args.page,
output_dir=args.output_dir
)
print(result)
# Print persistence confirmation
if args.persist:
project_slug = args.project_name.lower().replace(' ', '-') if args.project_name else "default"
print("\n" + "=" * 60)
print(f"✅ Design system persisted to design-system/{project_slug}/")
print(f" 📄 design-system/{project_slug}/MASTER.md (Global Source of Truth)")
if args.page:
page_filename = args.page.lower().replace(' ', '-')
print(f" 📄 design-system/{project_slug}/pages/{page_filename}.md (Page Overrides)")
print("")
print(f"📖 Usage: When building a page, check design-system/{project_slug}/pages/[page].md first.")
print(f" If exists, its rules override MASTER.md. Otherwise, use MASTER.md.")
print("=" * 60)
# Stack search
elif args.stack:
result = search_stack(args.query, args.stack, args.max_results)
if args.json:
import json
print(json.dumps(result, indent=2, ensure_ascii=False))
else:
print(format_output(result))
# Domain search
else:
result = search(args.query, args.domain, args.max_results)
if args.json:
import json
print(json.dumps(result, indent=2, ensure_ascii=False))
else:
print(format_output(result))