The user is asking in Chinese about "测试code能力，结合evalscope的能力" which translates to "Testing code capabilities, combined with evalscope's capabilities." They want to test code capabilities and combine them with EvalScope's capabilities. EvalScope is a model evaluation framework developed by ModelScope (Alibaba). It's used for evaluating LLMs on various benchmarks. Let me think about what would be helpful here: 1. They might want to know how to use EvalScope to evaluate code capabilities of models 2. They might want a demo/example showing code evaluation 3. They might want an interactive demo Since the system prompt says the response will be rendered in an iframe/web browser, and I should include full HTML if I use external libraries, it seems like they might want a visual/interactive demonstration. Let me create a comprehensive HTML page that demonstrates: 1. How EvalScope evaluates code capabilities 2. Code benchmarks like HumanEval, MBPP, LiveCodeBench 3. Example code showing how to use EvalScope for code evaluation 4. Maybe an interactive demo showing code testing EvalScope supports code evaluation benchmarks: - HumanEval - MBPP - LiveCodeBench - Codeforces - etc. The typical EvalScope usage for code evaluation: ```python from evalscope import TaskConfig, run_task task_cfg = TaskConfig( model='qwen2.5-coder', datasets=['humaneval', 'mbpp', 'live_code_bench'], dataset_args={ 'live_code_bench': { 'subset': 'release_latest' } }, eval_type='service', # or local model generation_config={ 'max_tokens': 4096, 'temperature': 0.0 }, limit=10 # limit samples for testing ) run_task(task_cfg=task_cfg) ``` Or via command line: ```bash evalscope eval \ --model qwen2.5-coder \ --datasets humaneval mbpp live_code_bench \ --limit 10 ``` I should create a nice HTML page that: 1. Explains EvalScope's code evaluation capabilities 2. Shows example code (with syntax highlighting) 3. Maybe an interactive code playground demo 4. Shows benchmark results structure Let me make a beautiful, comprehensive single-page HTML with: - Header explaining the topic - Code examples with syntax highlighting (using highlight.js or Prism) - Interactive demo showing how pass@k works - Benchmark comparison visualization (using Chart.js or ECharts) - Step-by-step guide Since they mentioned "测试code能力" (test code capability), I could also include an interactive code editor demo where users can see how code evaluation works - like a mini HumanEval simulation. Let me design: 1. **概述** - Overview of EvalScope code evaluation 2. **支持的代码基准** - Supported code benchmarks (HumanEval, MBPP, LiveCodeBench, etc.) 3. **快速开始代码** - Quick start code examples 4. **交互式演示** - Interactive demo: a simple code evaluator that runs test cases against generated code (simulating pass@1) 5. **评估指标可视化** - Metrics visualization For the interactive demo, I can use JavaScript to simulate a code evaluation - show a problem, let user see generated code, run test cases, show pass/fail. Since we can't run Python in browser easily without Pyodide (which is heavy), I could: - Use Pyodide for real Python execution (might be slow but impressive) - Or simulate with JavaScript Pyodide would be cool but loading takes time. Let me use Pyodide via CDN - it allows running Python in the browser. That would make it a real demonstration of code evaluation: define a HumanEval-style problem, provide a solution, run test cases, compute pass rate. Actually, let me include Pyodide - it makes the demo genuinely functional. The page loads Pyodide from CDN, and the demo can actually execute Python code against test cases. Structure: 1. Hero section: "EvalScope 代码能力评测" 2. Benchmarks cards: HumanEval (164 problems, pass@1), MBPP (1000 problems), LiveCodeBench (contamination-free), etc. 3. Quick start: CLI + Python API code blocks with syntax highlighting 4. Interactive lab: - Show a HumanEval-style problem - Editable code textarea (or pre-filled model output) - "运行测试" button - executes code with test cases via Pyodide - Show pass/fail results per test case - Compute pass@1 - Maybe multiple problems to simulate a mini benchmark, show aggregate score 5. Results interpretation section Let me write the code. Key EvalScope details: - GitHub: modelscope/evalscope - Install: pip install evalscope - Code benchmarks supported: humaneval, mbpp, live_code_bench, codeforces, etc. Example config for code eval: ```python from evalscope import TaskConfig, run_task task_config = TaskConfig( model='Qwen2.5-Coder-32B-Instruct', api_url='http://127.0.0.1:8000/v1', # vLLM/OpenAI compatible service api_key='EMPTY', eval_type='service', datasets=['humaneval', 'live_code_bench'], dataset_args={ 'humaneval': {'few_shot_num': 0}, }, generation_config={ 'temperature': 0.2, 'max_tokens': 2048, }, limit=20, ) run_task(task_config=task_config) ``` CLI: ```bash evalscope eval \ --model Qwen2.5-Coder-32B-Instruct \ --api-url http://127.0.0.1:8000/v1 \ --api-key EMPTY \ --eval-type service \ --datasets humaneval mbpp live_code_bench \ --limit 20 ``` HumanEval problem example: ```python def has_close_elements(numbers, threshold): """ Check if in given list of numbers, are any two numbers closer to each other than given threshold. >>> has_close_elements([1.0, 2.0, 3.0], 0.5) False >>> has_close_elements([1.0, 2.8, 3.0, 4.0, 5.0, 2.0], 0.3) True """ ``` For the interactive demo, let me have 3-4 mini problems: 1. `add(a, b)` - simple 2. `is_palindrome(s)` 3. `fib(n)` 4. `max_diff(lst)` Each with test cases. The "model" generates code (pre-filled, user can edit), click run, Pyodide executes, show per-test results and overall pass rate = fraction of problems where all tests pass (like pass@1). Also add a chart (ECharts from CDN) showing example benchmark scores of popular models on HumanEval/MBPP/LiveCodeBench for illustration. I should label these as 示例数据 (illustrative) to be accurate, or use approximate public numbers. Better to mark as "示例/演示数据". Let me use ECharts from CDN: https://cdn.jsdelivr.net/npm/echarts@5/dist/echarts.min.js Pyodide: https://cdn.jsdelivr.net/pyodide/v0.24.1/full/pyodide.js (or newer v0.26.x). Let me use v0.26.4: https://cdn.jsdelivr.net/pyodide/v0.26.4/full/pyodide.js. The loadPyodide function with indexURL. Actually to be safe with versions, v0.24.1 is well-known stable. Let me use v0.26.4 as it's recent and stable. Either works. I'll use v0.26.4. For code highlighting, I can use highlight.js CDN or just style it manually. highlight.js: https://cdnjs.cloudflare.com/ajax/libs/highlight.js/11.9.0/highlight.min.js with a theme CSS. Let me now design the page aesthetically: - Dark theme, modern, gradient accents - Chinese language UI since user wrote in Chinese - Sections with scroll navigation Let me write the problems for the mini-benchmark: Problem 1: ```python def add(a, b): """返回两个数之和 >>> add(2, 3) 5 >>> add(-1, 1) 0 """ ``` Model solution: `return a + b` Problem 2: ```python def is_palindrome(s): """判断字符串是否为回文 >>> is_palindrome("level") True >>> is_palindrome("hello") False """ ``` Problem 3: ```python def count_vowels(s): ... ``` Problem 4: ```python def second_largest(nums): ... ``` Test harness in Pyodide: ```python import sys from io import StringIO # user code {user_code} # tests results = [] {test_calls} # try/except each, compare result ``` I'll structure the JS to run each test individually and capture results. For pass@1 simulation: each problem either passes all tests (1) or not (0). Score = passed/total. Also I could add a temperature/pass@k explanation with a small interactive: "模拟多次采样" — generate random pass probabilities and compute pass@k using the estimator formula: pass@k = 1 - C(n-c, k)/C(n, k). That could be a nice visualization. But keep it manageable. Let me include: - pass@k calculator: inputs n (total samples), c (correct samples), k → compute pass@k. With the formula explanation. That's directly relevant to code eval metrics. Structure plan: 1. Nav bar (fixed): logo "EvalScope × Code" links: 概览, 基准, 快速开始, 在线实验, 指标 2. Hero: title + subtitle + animated background (subtle grid/particles) 3. Stats strip: 15+ 代码基准, HumanEval/MBPP/LiveCodeBench etc. 4. Benchmarks section: cards for HumanEval, MBPP, LiveCodeBench, Codeforces/SWE-bench maybe 5. Quick start: two tabs (CLI / Python API) with code 6. Workflow diagram: 模型服务 → EvalScope → 沙箱执行 → 报告 (visual pipeline with CSS) 7. Interactive lab (Pyodide): - Problem selector tabs - Problem statement + starter "模型生成代码" (editable textarea) - Run button → loading → per-test results → verdict - Overall mini-benchmark score with progress ring 8. pass@k calculator + chart 9. Example results chart (ECharts bar chart, labeled 演示数据) 10. Footer with links Let me write the code carefully. Pyodide execution details: ```javascript async function runTests(problemIndex) { const py = pyodide; const code = editors[problemIndex].value; const problem = problems[problemIndex]; // Build test runner const harness = ` import sys, json, traceback from io import StringIO _results = [] def _run_test(fn_name, args, expected): try: fn = globals()[fn_name] got = fn(*args) ok = (got == expected) _results.append({"ok": ok, "got": repr(got), "expected": repr(expected), "error": None}) except Exception as e: _results.append({"ok": False, "got": None, "expected": repr(expected), "error": str(e)}) `; // run user code first try { await py.runPythonAsync(code); } catch (e) { show error } // then run each test for (const t of problem.tests) { py.globals.set('fn_name', t.fn) ... } } ``` Better approach: single Python script that includes user code + tests, returns JSON. But syntax errors in user code need separate handling. Two-phase: Phase 1: runPythonAsync(userCode) in a namespace — errors reported as syntax/runtime errors at definition time. Phase 2: for each test, call the function with arguments via pyodide globals. I can do: ```javascript py.globals.set("__test_args", py.toPy(args)) // messy ``` Simpler: build Python code per test: ```python import json def __run(): try: got = fn(arg1, arg2) return json.dumps({"ok": bool(got == expected), "got": repr(got), "err": ""}) except Exception as e: return json.dumps({"ok": False, "got": "", "err": str(e)}) __run() ``` I'll generate test call strings like `add(2, 3)` and expected repr. But passing Python literals from JS: I'll store test cases as Python source strings, e.g. `{"call": "add(2, 3)", "expected": "5"}`, then compare `repr(eval(call)) == expected`? Using repr comparison is simple and robust: ```python try: __got = repr({call}) __ok = (__got == {expected!r}) except Exception as __e: ... ``` So test: call: "add(2, 3)", expected: "5". Compare repr strings. Good. Also wrap in timeout? Pyodide doesn't easily support timeouts; a while True loop would hang. To mitigate, I can mention "请勿写死循环". For robustness I could run code in a web worker with timeout and terminate. That's complex. I'll accept the risk but note it. Actually, I could implement a simple guard: use sys.settrace-based step limit: ```python import sys def __limit(frame, event, arg): __limit.count += 1 if __limit.count > 2_000_000: raise TimeoutError("执行步数超限（疑似死循环）") return __limit __limit.count = 0 sys.settrace(__limit) ``` settrace adds overhead but fine for tiny tests. Include it per test with lower limit like 1,000,000 line events. Hmm, line events for simple loops are fine. Let me set 500k. Actually settrace 'line' events - a loop of 10^7 iterations would trigger 10^7 events quickly-ish. Limit 200_000 lines → raises. Good enough. Also need to disable trace after test: sys.settrace(None). Let me write the harness generator: ```javascript function buildHarness(call, expected) { return ` import sys as __sys def __trace(frame, event, arg): __trace.n += 1 if __trace.n > 200000: raise TimeoutError("执行超出步数限制") return __trace __trace.n = 0 __sys.settrace(__trace) try: __got = repr(${call}) __sys.settrace(None) __res = {"ok": __got == ${JSON.stringify(expected)}, "got": __got, "err": ""} except Exception as __e: __sys.settrace(None) __res = {"ok": False, "got": "", "err": type(__e).__name__ + ": " + str(__e)} __res `; } ``` Note: TimeoutError raised inside trace will propagate when calling eval expression — good, caught by except. But careful: user code might define names colliding with `__res` etc. Use unique prefixes. Fine. Also user code may print — capture stdout per run: pyodide setStdout batched. I can capture and display. Use: ```javascript pyodide.setStdout({ batched: (s) => output += s + "\n" }); ``` Problems list (4 problems, HumanEval style): 1. add — trivial warmup 2. is_palindrome 3. fizzbuzz_sum? Let me pick interesting ones: - `count_distinct_chars(s)` count distinct characters case-insensitive - `median(lst)` - `vowels_count(s)` from HumanEval - `triangle_area(a,b,c)` Heron with validation (HumanEval #??) Let me use actual HumanEval-style problems: P1: `add(a,b)` custom simple P2: HumanEval-like `vowels_count(s)`: count vowels, 'y' counts as vowel only at end. Tests: vowels_count("abcde") == 2, vowels_count("ACEDY") == 3, vowels_count("rhythm") == 1? 'y' at position... "rhythm" ends with 'm', y not at end → 0. Hmm careful. Use HumanEval tests: vowels_count("abcde")=>2, vowels_count("Alone")=>3, vowels_count("key")=>2, vowels_count("bye")=>1? "bye": vowels e=1, y at end +1 =2? Actually HumanEval: vowels_count("bye") => 1? Let me recall: function counts a,e,i,o,u plus y only if at end. "bye": b,y,e → e is vowel (1), y at end (+1) = 2. Hmm but HumanEval test says vowels_count("bye") == 1? Let me think again... HumanEval #?? "vowels_count": ``` def vowels_count(s): vowels = "aeiouAEIOU" n_vowels = sum(c in vowels for c in s) if s[-1] == 'y' or s[-1] == 'Y': n_vowels += 1 return n_vowels ``` Wait that counts y at end in addition. Test: vowels_count("bye") == 1? If canonical counts "e"=1 and y at end adds 1 → 2. Hmm, actually I recall canonical solution doesn't double count... Let me not risk it; I'll define my own spec clearly in the docstring and tests consistent with the provided solution. Since I provide both docstring, tests, and solution, consistency is what matters. I'll craft custom problems to avoid HumanEval factual errors: P1 `add(a, b)` → return a+b. Tests: add(2,3)→5, add(-1,1)→0, add(0,0)→0. Model code: `return a + b` ✓ P2 `is_palindrome(s)`: return True if s reads same forwards/backwards. Tests: "level"→True, "hello"→False, ""→True, "abba"→True. Model: `return s == s[::-1]` ✓ P3 `second_largest(nums)`: return second largest distinct value; assume at least 2 distinct. Tests: [1,2,3]→2; [5,5,4]→4; [10,9,8,7]→9. Model solution: ```python def second_largest(nums): uniq = sorted(set(nums), reverse=True) return uniq[1] ``` ✓ P4 `count_words(s)`: count words separated by whitespace. Tests: "hello world"→2, " a b c "→3, ""→0. Model: `return len(s.split())` ✓ P5 (challenge) `roman_to_int(s)`: convert roman numeral. Tests: "III"→3, "IV"→4, "IX"→9, "LVIII"→58, "MCMXCIV"→1994. Model solution standard ✓ To demonstrate failure, I can make one pre-filled solution intentionally buggy (like a model would produce), e.g., P3's pre-filled code uses sorted(nums)[-2] without dedup → fails [5,5,4] test (gives 5, expected 4). This nicely demonstrates how EvalScope catches errors! Great pedagogical point: 模型生成代码常见错误被测试用例捕获。 So P3 pre-filled: ```python def second_largest(nums): nums = sorted(nums, reverse=True) return nums[1] # ⚠️ 未去重，存在 bug ``` Tests: [1,2,3]→2 ✓, [5,5,4]→4 ✗ (got 5), [10,9]→9 ✓. Verdict: FAIL → shows how pass@1 works. User can fix it. pass@k calculator: - Inputs: n=10 samples, c=4 correct, k slider 1..10 - Formula: pass@k = 1 - C(n-c, k) / C(n, k) - Show result + mini bar chart of pass@k for k=1..10 (canvas or ECharts line) ECharts: use for both the pass@k curve and a benchmark comparison bar chart (演示数据). Benchmark comparison chart data (labeled 示例数据, roughly public numbers for HumanEval pass@1): - Qwen2.5-Coder-32B-Instruct: HumanEval ~92, MBPP ~90? Actually Qwen2.5-Coder-32B claims HumanEval 92.1, MBPP 90.2 (approx). GPT-4o HumanEval ~92. DeepSeek-Coder-V2-Lite: ~81. I'll mark as 演示数据（请以官方报告为准）to be safe. Maybe just label the whole chart "演示数据". Actually to avoid stating possibly-wrong numbers, I'll title it "示例：多模型代码基准对比（演示数据）". Values: plausible but flagged. Now the pipeline/workflow section: CSS-based horizontal steps: 1. 启动模型服务 (vLLM / API) 2. 配置任务 (TaskConfig) 3. 生成与采样 (temperature, n samples) 4. 沙箱执行测试 (Docker/本地) 5. 计算 pass@k 6. 生成报告 (表格/可视化) Quick start code blocks — make them accurate per EvalScope docs: Install: ```bash pip install 'evalscope[all]' # 或 pip install evalscope ``` CLI: ```bash evalscope eval \ --model Qwen/Qwen2.5-Coder-32B-Instruct \ --api-url http://127.0.0.1:8000/v1/chat/completions \ --api-key EMPTY \ --eval-type service \ --datasets humaneval live_code_bench \ --dataset-args '{"live_code_bench": {"subset": "release_v6"}}' \ --generation-config '{"temperature":0.2,"max_tokens":2048}' \ --limit 20 ``` Hmm, exact CLI arg names: evalscope uses `--model`, `--api-url`, `--datasets`, `--limit`, `--generation-config`? I believe TaskConfig fields: model, model_id, api_url, api_key, eval_type, datasets, dataset_args, generation_config, eval_batch_size, limit. CLI mirrors with dashes. This is approximately right; I'll present Python API as primary (more certain) and a CLI example. Python API: ```python from evalscope import TaskConfig, run_task task_cfg = TaskConfig( model='Qwen2.5-Coder-32B-Instruct', # 服务化模型名称 api_url='http://127.0.0.1:8000/v1', # OpenAI 兼容服务 (vLLM/SGLang) api_key='EMPTY', eval_type='service', # 评测服务而非本地加载 datasets=['humaneval', 'mbpp', 'live_code_bench'], dataset_args={ 'humaneval': {'few_shot_num': 0}, }, generation_config={ 'temperature': 0.2, # 代码任务建议低温度 'max_tokens': 4096, }, eval_batch_size=8, limit=20, # 调试时先小样本 ) run_task(task_cfg=task_cfg) ``` Outputs: ./outputs/... reports with pass@1. Also mention custom code dataset support & LiveCodeBench subset versions. Now design/aesthetics. Dark theme: - Background: #0a0e14 / #0d1117 - Accent: gradient from #22d3ee (cyan) to #a78bfa (purple), or green terminal vibes. Code theme: use a "terminal green" #4ade80 + cyan. Let me do cyan/purple gradient, looks modern. - Font: system + 'JetBrains Mono' for code (Google Fonts CDN). - Cards with glass effect, subtle borders rgba(255,255,255,0.08). - Section reveal animations via IntersectionObserver. - Fixed nav with blur. Editor: textarea styled like code editor with line numbers? Simpler: just monospace textarea with dark bg. Add "重置为模型输出" button. Status of Pyodide: show loading indicator "正在加载 Python 运行时 (Pyodide)..." then ready badge. Overall scoreboard: track each problem status (未运行/通过/失败), show score like "3 / 5 通过 (60%)" with a progress ring (SVG circle stroke-dasharray). Run all button: runs all 5 sequentially. Per-test results: list with ✓/✗ icons, call expression, expected vs got, error if any. Also capture stdout (print output) to a small console area. Now the pass@k calculator UI: - Sliders: n (2..50, default 10), c (0..n, default 4), k (1..n, default 1) - Big number result: pass@k percentage - Line chart pass@k vs k using ECharts, updates on input. Combination function in JS: ```javascript function comb(n, k){ if(k<0||k>n) return 0; let r=1; for(let i=0;i, avoid " ``` Then `loadPyodide({ indexURL: "https://cdn.jsdelivr.net/pyodide/v0.26.4/full/" })`. Highlight.js for static code blocks: ```html ``` ECharts: ```html ``` Fonts: Google Fonts JetBrains Mono + maybe "Noto Sans SC" for Chinese. Use system fonts for Chinese to save load; JetBrains Mono for code. Let me now write out the full HTML. It'll be long. I'll be careful about correctness of JS. Detailed component: problems data ```javascript const problems = [ { id: 0, title: "P0 · add（热身）", fn: "add", doc: `def add(a, b): """ 返回两个数字 a 与 b 的和。 >>> add(2, 3) 5 """`, starter: `def add(a, b): # 模型生成的代码 return a + b `, tests: [ { call: "add(2, 3)", expected: "5" }, { call: "add(-1, 1)", expected: "0" }, { call: "add(0, 0)", expected: "0" }, ], }, ... ]; ``` Problem P3 (second_largest) buggy starter: ```python def second_largest(nums): # 模型生成的代码 nums.sort(reverse=True) return nums[1] ``` Test [5,5,4] expects 4 → returns 5 → fail. Note nums.sort mutates; fine. P5 roman_to_int starter: ```python def roman_to_int(s): values = {'I': 1, 'V': 5, 'X': 10, 'L': 50, 'C': 100, 'D': 500, 'M': 1000} total = 0 for i, ch in enumerate(s): if i + 1 < len(s) and values[ch] < values[s[i + 1]]: total -= values[ch] else: total += values[ch] return total ``` Tests: "III"→3, "IV"→4, "IX"→9, "LVIII"→58, "MCMXCIV"→1994. ✓ P4 count_words starter: `return len(s.split())` tests: "hello world"→2, " a b c "→3, ""→0. ✓ P2 is_palindrome starter: `return s == s[::-1]` tests: "level"→True, "hello"→False, ""→True, "noon"→True. repr(True) = "True". Expected strings "True"/"False". ✓ So statuses: P0 ✓, P1 ✓, P2 ✗ (bug), P3 ✓, P4 ✓ initially if run all → score 4/5 = 80%. Good demo narrative: fix P2 to reach 100%. Wait indexing: I'll label P1..P5 for users. Running tests: ```javascript async function runProblem(idx) { const py = await pyodideReady; const p = problems[idx]; const code = document.getElementById(`editor-${idx}`).value; results area clear, show running // Phase 1: exec user code let stdoutBuf = ""; py.setStdout({ batched: s => stdoutBuf += s + "\n" }); py.setStderr({ batched: s => stdoutBuf += s + "\n" }); try { await py.runPythonAsync(code); } catch (err) { // syntax error → all tests fail with error message renderVerdict(idx, 'error', cleanErr(err.message)); return; } const results = []; for (const t of p.tests) { const harness = buildHarness(t.call, t.expected); let res; try { res = py.runPython(harness); // returns dict → proxy? } catch (err) { res = { ok:false, got:"", err: cleanErr(err.message) }; } ... } } ``` `py.runPython` returning a Python dict gives a PyProxy; convert with `.toJs({dict_converter: Object.fromEntries})`. Simpler: return JSON string: end harness with `__import__('json').dumps(__res)`? Then runPython returns JS string. Let's do that: harness returns JSON string via json.dumps. Harness (returning JSON str): ```javascript function buildHarness(call, expected) { return ` import sys as __sys, json as __json def __trace(frame, event, arg): __trace.n += 1 if __trace.n > 300000: raise TimeoutError('执行超出步数限制（疑似死循环）') return __trace __trace.n = 0 __sys.settrace(__trace) try: __got = repr(${call}) __sys.settrace(None) __out = {'ok': __got == ${JSON.stringify(expected)}, 'got': __got, 'err': ''} except Exception as __e: __sys.settrace(None) __out = {'ok': False, 'got': '', 'err': type(__e).__name__ + ': ' + str(__e)} __json.dumps(__out) `; } ``` Careful: user code might itself use sys.settrace or import sys as __sys — collision unlikely. Also user code defining __trace would be overwritten by ours — fine since harness defines its own. Note: settrace applies to new frames; the eval of `${call}` happens in module frame which was entered before settrace? The module-level frame starts when runPython begins; settrace only affects subsequently-called functions. The `repr(add(2,3))` call happens in the module frame (not traced), but `add(...)` call itself is a new frame → traced. Good, loop inside add is traced. Edge: user code executed at import phase (Phase 1) without trace — if top-level infinite loop, hang. Acceptable; note warning. Render per-test rows and verdict. Update scoreboard state: state[idx] = 'pass' | 'fail' | 'error' | null. Scoreboard: 5 dots + "Pass@1 = 80%" ring. Clean error message: Pyodide errors are verbose; take last non-empty line: ```javascript function cleanErr(msg){ const lines = String(msg).trim().split('\n').filter(l=>l.trim()); return lines[lines.length-1]; } ``` Now pass@k calculator: ```html n slider (2-50, default 10), c slider (0-n, default 4), k slider (1-50 but clamp ≤ n, default 1) ``` On change: clamp c ≤ n, k ≤ n. Compute pass@k. Update ECharts line for k=1..n with markPoint at current k. ECharts init once, update options. Benchmark chart: bar chart, series per benchmark or per model. xAxis: models; series: HumanEval, MBPP, LiveCodeBench. Also a radar? Keep bar. Sample report: styled table. Hero terminal typing animation: lines: ``` $ evalscope eval --model Qwen2.5-Coder-32B-Instruct \ --datasets humaneval live_code_bench --limit 20 [2024-..] Loading dataset: humaneval (164 samples) Generating responses... 100%|██████████| 20/20 Evaluating humaneval: pass@1 = 92.07 Report saved to ./outputs/2024xxxx/report.md ``` Typewriter effect with cursor. Loop or run once. I'll type once then leave cursor blinking. Nav smooth scroll. Reveal on scroll via IntersectionObserver adding class. Responsive: grid → single column on mobile. Let me count sections and write final HTML now. Also remember: no mentioning iframe. One more consideration: highlight.js with `language-python` and `language-bash`. Also add copy buttons on code blocks? Nice-to-have; add simple copy button. Now write everything. I need to be careful with backticks inside template strings in my JS (problems starter code uses backticks as delimiters; code contains no backticks — ok). The doc strings contain `>>>` and newlines — fine in template literals. Editor rows: number of rows based on lines. Set rows attribute. Scoreboard ring: SVG circle circumference = 2πr, r=26 → ≈163.4. dasharray = pct*163.4. Let me write the run-all with sequential await and slight delay for UX. Also disable run buttons while running. Pyodide status badge: gray "加载中…" → green "Python 运行时就绪". Load pyodide on page load (async). If CDN fails, show error message on run attempt. pass@k numbers formatting: percentage with 1 decimal. Combination: use multiplicative loop, values small, fine. Chart theme: dark background transparent, colors cyan #22d3ee, purple #a78bfa, green #4ade80. Benchmark chart data (演示数据): models: ['Qwen2.5-Coder-32B', 'DeepSeek-V3', 'GPT-4o', 'Claude-3.5-Sonnet'] HumanEval: [92.1, 90.2, 92.4, 93.0]? Claude 3.5 Sonnet HumanEval 92.0? Approx fine since labeled demo. MBPP: [90.2, 86.5, 88.0, 88.5], LCB: [31.4, 40.5, 33.1, 35.0]. All labeled 演示数据. Let me write final code. I'll aim for polished visuals. Also include EvalScope facts: - 支持 15+ 代码生成基准（HumanEval, MBPP, LiveCodeBench, CodeForces, SWE-bench 等）— I should be careful: EvalScope supports humaneval, mbpp(?), live_code_bench, codeforces(?), swe_bench... EvalScope docs list: HumanEval, MBPP, LiveCodeBench, CodeContests? I know EvalScope added "live_code_bench" and "humaneval", also "code_switch"? To be safe: "HumanEval / MBPP / LiveCodeBench / SWE-bench 等主流代码基准". MBPP: evalscope has 'mbpp'? I believe evalscope supports humaneval and mbpp in code generation category, plus live_code_bench and recently codeforces. SWE-bench is in evalscope? There is "swe_bench" support in EvalScope (agentic eval). I'll phrase cautiously: "内置 HumanEval、LiveCodeBench 等代码生成基准，并可通过自定义数据集接入 MBPP、SWE-bench 等" — hmm, MBPP likely built-in. I'll say 支持 HumanEval、MBPP、LiveCodeBench 等. That's likely accurate. pass@k explanation formula: pass@k = 1 − C(n−c, k)/C(n, k). Unbiased estimator from HumanEval paper (Chen et al. 2021). OK writing now. Keep total length reasonable but rich — maybe ~900-1100 lines. Ensure valid HTML. For the nav on mobile: hide links, keep simple. Write CSS with custom properties. Let me draft the HTML skeleton: ```html EvalScope · 代码能力评测实战

EvalScope × Code Evaluation

用 EvalScope
系统化测试代码能力

...

typing animation

3 stat cards: 基准覆盖 / 指标 pass@k / 执行方式(沙箱) + pipeline steps

cards grid

tabs CLI/Python + code blocks + install

interactive pyodide lab

pass@k calculator + charts + sample report