{ "version": "https://jsonfeed.org/version/1", "title": "Python", "description": "\u8fd9\u91cc\u8ba8\u8bba\u5404\u79cd Python \u8bed\u8a00\u7f16\u7a0b\u8bdd\u9898\uff0c\u4e5f\u5305\u62ec Django\uff0cTornado \u7b49\u6846\u67b6\u7684\u8ba8\u8bba\u3002\u8fd9\u91cc\u662f\u4e00\u4e2a\u80fd\u591f\u5e2e\u52a9\u4f60\u89e3\u51b3\u5b9e\u9645\u95ee\u9898\u7684\u5730\u65b9\u3002", "home_page_url": "go/python", "feed_url": "feed/python.json", "icon": "https://cdn.v2ex.com/navatar/8613/985e/90_large.png?m=1648339948", "favicon": "https://cdn.v2ex.com/navatar/8613/985e/90_normal.png?m=1648339948", "items": [ { "author": { "url": "member/codists", "name": "codists", "avatar": "https://cdn.v2ex.com/avatar/8bba/467a/525158_large.png?m=1716029644" }, "url": "t/1165216", "title": "\u8fed\u4ee3\u5668\u7684\u5b9e\u9645\u5e94\u7528\u573a\u666f\u662f\u4ec0\u4e48\uff1f", "id": "t/1165216", "date_published": "2025-10-14T12:18:12+00:00", "content_html": "

\u6982\u8ff0

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\u6700\u8fd1\u5728\u68b3\u7406 iterator \uff0c\u4e0d\u5f97\u4e0d\u8bf4\uff0c \u5373\u4f7f\u81ea\u5df1\u5199\u4e86\u5f88\u591a\u5e74\u7684\u4ee3\u7801\uff0c\u6211\u4ecd\u7136\u6ca1\u6709\u5728\u5b9e\u9645\u5e94\u7528\u4e2d\u770b\u5230\u81ea\u5b9a\u4e49\u7684\u8fed\u4ee3\u5668\u3002\u5373\u4f7f\u8bfb\u4e86\u5f88\u591a\u4e66\uff0c\u4f46\u662f\u8fd9\u4e9b\u4e66\u4e2d\u7684\u793a\u4f8b\u5927\u591a\u662f\u6ee5\u7afd\u5145\u6570\uff0c\u4e0d\u5177\u5907\u5b9e\u9645\u5e94\u7528\u610f\u4e49\u3002\u6240\u4ee5\u987a\u7740\u7f51\u7ebf\u722c\u4e0a V \u7ad9\u8bf7\u6559\u5404\u4f4d\u3002

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\u53ef\u8fed\u4ee3\u5bf9\u8c61 & \u8fed\u4ee3\u5668\u5b9a\u4e49

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\u53ef\u8fed\u4ee3\u5bf9\u8c61

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\u5982\u679c\u4e00\u4e2a\u5bf9\u8c61\u5b9a\u4e49\u4e86 __iter__() \u65b9\u6cd5\u6216\u5b9a\u4e49\u4e86 __getitem__() \u65b9\u6cd5\uff0c\u90a3\u4e48\u8fd9\u6837\u7684\u5bf9\u8c61\u79f0\u4e3a\u53ef\u8fed\u4ee3\u5bf9\u8c61(iterable)\u3002

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\u8fed\u4ee3\u5668

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\u5982\u679c\u4e00\u4e2a\u5bf9\u8c61\u5b9a\u4e49\u4e86 __iter__() \u65b9\u6cd5\u548c __next__() \u65b9\u6cd5\uff0c\u90a3\u4e48\u8fd9\u6837\u7684\u5bf9\u8c61\u79f0\u4e3a\u8fed\u4ee3\u5668(iterator)\u3002

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\u6ce8\uff1a

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1.\u540e\u7eed\u7684\u8ba8\u8bba\u90fd\u662f\u57fa\u4e8e\u4ee5\u4e0a\u4e24\u4e2a\u5b9a\u4e49\u3002

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2.\u56e0\u8fed\u4ee3\u5668\u5e38\u548c\u53ef\u8fed\u4ee3\u5bf9\u8c61\u7ed3\u5408\u4f7f\u7528\uff0c\u6545\u5f15\u5982\u53ef\u8fed\u4ee3\u5bf9\u8c61\u8fd9\u4e00\u6982\u5ff5\uff0c\u4f46\u8fed\u4ee3\u5668\u7684\u6982\u5ff5\u5148\u4e8e\u751f\u6210\u5668(generator)\uff0c\u5728\u540e\u7eed\u7684\u8ba8\u8bba\u4e2d\u8bf7\u52ff\u6d89\u53ca\u751f\u6210\u5668\u3002

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\u4e00\u4e9b\u793a\u4f8b

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\u793a\u4f8b 1

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python 3 \u7684 range() \u662f\u4e00\u4e2a\u53ef\u8fed\u4ee3\u5bf9\u8c61\uff0c\u5176\u5b9e\u73b0\u4f7f\u7528\u4e86\u8fed\u4ee3\u5668\u3002\u4f7f\u7528\u8fed\u4ee3\u5668\u540e\u4e0d\u662f\u76f4\u63a5\u751f\u6210\u5217\u8868\uff0c\u8282\u7701\u4e86\u5185\u5b58\uff0c\u4f53\u73b0\u4e86\u8fed\u4ee3\u5668\u7684\u5e94\u7528\u610f\u4e49\u3002

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\u793a\u4f8b 2

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\u300a Learn Python Programming(4th)\u300b \u7b2c 246 \u9875\uff1a

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class OddEven:\n def __init__(self, data):\n self._data = data\n self.indexes = list(range(0, len(data), 2)) + list(range(1, len(data), 2))\n\n def __iter__(self):\n return self\n\n def __next__(self):\n if self.indexes:\n return self._data[self.indexes.pop(0)]\n raise StopIteration\n\n\n# Testing the OddEven class\noddeven = OddEven(\"0123456789\")\nprint(\"\".join(c for c in oddeven)) # 0246813579\n\noddeven = OddEven(\"ABCD\") # or manually...\nit = iter(oddeven) # this calls oddeven.__iter__ internally\nprint(next(it)) # A\nprint(next(it)) # C\nprint(next(it)) # B\nprint(next(it)) # D\n\n

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\u8be5\u793a\u4f8b\u867d\u7136\u521b\u5efa\u4e86\u4e00\u4e2a\u8fed\u4ee3\u5668\uff0c\u4f46\u5c31\u529f\u80fd\u800c\u8a00\u5176\u5b9e\u5c31\u662f\u201c\u5c06\u5947\u6570\u4f4d\u7f6e\u7684\u5b57\u7b26\u653e\u5728\u524d\u534a\u6bb5\uff0c\u5c06\u5076\u6570\u4f4d\u7f6e\u7684\u5b57\u7b26\u653e\u5728\u540e\u534a\u6bb5\u201d\uff0c\u5b8c\u5168\u6ca1\u6709\u5fc5\u8981\u4f7f\u7528\u8fed\u4ee3\u5668\u3002\u5173\u4e8e\u8fed\u4ee3\u5668\u7684\u5b9e\u529b\uff0c\u672c\u4eba\u770b\u5230\u7684\u5927\u591a\u662f\u8fd9\u6837\u7684\u2014\u2014\u6beb\u65e0\u5b9e\u9645\u5e94\u7528\u610f\u4e49\uff0c\u4ee4\u4eba\u6df1\u6076\u75db\u7edd\uff01

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\u95ee\u9898

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\u95ee\u9898 1

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PEP 234 \u4e2d\u5199\u5230 iterator \u7684 virtues \u6709:

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1. It provides an extensible iterator interface.
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2. It allows performance enhancements to list iteration.
\n
3. It allows big performance enhancements to dictionary iteration.
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4. It allows one to provide an interface for just iteration without pretending to provide random access to elements.
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5. It is backward-compatible with all existing user-defined classes and extension objects that emulate sequences and mappings, even mappings that only implement a subset of {__getitem__, keys, values, items}.
\n
6. It makes code iterating over non-sequence collections more concise and readable.
\n

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\u4e2d\u8bd1\u7248\uff1a

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\u5982\u679c\u5305\u542b\u8be5\u63d0\u6848\u7684\u6240\u6709\u90e8\u5206\uff0c\u5219\u4f1a\u4ee5\u4e00\u81f4\u4e14\u7075\u6d3b\u7684\u65b9\u5f0f\u89e3\u51b3\u8bb8\u591a\u95ee\u9898\u3002\u5176\u4e3b\u8981\u4f18\u70b9\u5305\u62ec\u4ee5\u4e0b\u56db\u70b9\u2014\u2014\u4e0d\uff0c\u4e94\u70b9\u2014\u2014\u4e0d\uff0c\u516d\u70b9

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\n
1. \u5b83\u63d0\u4f9b\u4e86\u4e00\u4e2a\u53ef\u6269\u5c55\u7684\u8fed\u4ee3\u5668\u63a5\u53e3\u3002
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2. \u5b83\u5141\u8bb8\u5bf9\u5217\u8868\u8fed\u4ee3\u8fdb\u884c\u6027\u80fd\u4f18\u5316\u3002
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3. \u5b83\u5141\u8bb8\u5bf9\u5b57\u5178\u8fed\u4ee3\u8fdb\u884c\u5927\u5e45\u5ea6\u6027\u80fd\u63d0\u5347\u3002
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4. \u5b83\u5141\u8bb8\u4e3a\u4ec5\u8fed\u4ee3\u63d0\u4f9b\u63a5\u53e3\uff0c\u800c\u65e0\u9700\u5047\u88c5\u63d0\u4f9b\u5bf9\u5143\u7d20\u7684\u968f\u673a\u8bbf\u95ee\u3002
\n
5. \u5b83\u4e0e\u6240\u6709\u73b0\u6709\u7684\u7528\u6237\u5b9a\u4e49\u7c7b\u548c\u6a21\u62df\u5e8f\u5217\u548c\u6620\u5c04\u7684\u6269\u5c55\u5bf9\u8c61\u5411\u540e\u517c\u5bb9\uff0c\u5373\u4f7f\u662f\u4ec5\u5b9e\u73b0\u4e86 {__getitem__, keys, values, items} \u5b50\u96c6\u7684\u6620\u5c04\u3002
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6. \u5b83\u4f7f\u904d\u5386\u975e\u5e8f\u5217\u96c6\u5408\u7684\u4ee3\u7801\u66f4\u52a0\u7b80\u6d01\u6613\u8bfb\u3002
\n

\n

\n

\u4e0a\u9762\u6240\u5217\u51fa\u7684\u4f18\u70b9\u8f83\u62bd\u8c61\uff0c\u5404\u4f4d\u80fd\u5426\u63d0\u4f9b\u4e00\u4e9b\u5177\u4f53\u7684\u4f8b\u5b50\uff1f

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\u95ee\u9898 2

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\u5404\u4f4d\u5728\u5b9e\u9645\u5e94\u7528\u4e2d\u662f\u5426\u81ea\u5df1\u5b9e\u73b0\u8fc7\u8fed\u4ee3\u5668\uff1f\u5982\u679c\u6709\u9ebb\u70e6\u63d0\u4f9b\u4e00\u4e9b\u4f8b\u5b50\u3002

\n

\u53c2\u8003\u8d44\u6599

\n

[1] Python Document Glossary \uff0citerator\uff1a https://docs.python.org/3/glossary.html#term-iterator

[2] PEP 234 \u2013 Iterators\uff1a https://peps.python.org/pep-0234

[3] PEP 234 \u2013 \u8fed\u4ee3\u5668: https://peps.pythonlang.cn/pep-0234/

\u5982\u679c\u627e\u4eba\u4e00\u5bf9\u4e00\u6765\u5b66\u4e60\u662f\u4e0d\u662f\u66f4\u597d

\u7248\u672c\u53f7\u5341\u5206\u7279\u522b\uff0c\u521a\u597d\u5c31\u662f \ud835\udf0b

\u5df2\u7ecf\u6709\u4eba\u6539\u4e86\u4ee3\u7801\uff0c\u4e3a\u8fd9\u4e2a\u7248\u672c\u6dfb\u52a0\u4e13\u95e8\u7684\u540d\u79f0\uff1a\ud835\udf0bthon

gh-119535: Support \ud835\udf0bthon in Python 3.14 venvs

\u91cd\u5927\u66f4\u65b0\uff1a

\n
1. \u81ea\u7531\u7ebf\u7a0b\uff08 PEP 779 \uff09\u7279\u6027\u5df2\u7a33\u5b9a\u652f\u6301\u3002\u4e4b\u524d\u5728 Python 3.13 \u4e2d\uff0c\u8fd9\u4e00\u7279\u6027\u9700\u8981\u663e\u5f0f\u6307\u5b9a\u7f16\u8bd1\u9009\u9879\u3002\u4e0d\u8fc7\uff0c\u5168\u5c40\u89e3\u91ca\u5668\u9501\uff08 GIL \uff09\u4f9d\u7136\u662f\u53ef\u9009\u7684\uff0c\u800c\u662f\u5426\u8981\u5f7b\u5e95\u79fb\u9664 GIL \u4ecd\u5728\u8ba8\u8bba\u4e2d\uff08 PEP 703 \uff09\uff1b
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2. \u591a\u89e3\u91ca\u5668\uff08 PEP 734 \uff09\u7279\u6027\u5df2\u652f\u6301\u3002\u73b0\u5728\uff0c\u53ef\u4ee5\u5728\u540c\u4e00\u4e2a\u8fdb\u7a0b\u4e2d\u8fd0\u884c\u591a\u4e2a\u89e3\u91ca\u5668\uff0c\u6bcf\u4e2a\u89e3\u91ca\u5668\u90fd\u6709\u72ec\u7acb\u7684 GIL \u3002\u4f5c\u4e3a Python \u5e76\u884c\u7f16\u7a0b\u4e2d\u51cf\u5c11\u5bf9 multiprocessing \u4f9d\u8d56\u7684\u53c8\u4e00\u6b65\uff0c\u8fd8\u6dfb\u52a0\u4e86 InterpreterPoolExecutor \u6765\u5e2e\u52a9\u7ba1\u7406\u591a\u4e2a\u89e3\u91ca\u5668\u7684\u5e76\u884c\uff1b
\n
3. \u5b98\u65b9\u53d1\u5e03\u7684 macOS \u548c Windows \u7248\u4e8c\u8fdb\u5236\u73b0\u5df2\u5305\u542b\u00a0\u5b9e\u9a8c\u6027\u00a0\u7684\u5373\u65f6\u7f16\u8bd1\uff08 JIT \uff09\u5668\uff08 PEP 744 \uff09\u3002\u8be5 JIT \u7f16\u8bd1\u5668\u5c1a\u5904\u4e8e\u65e9\u671f\u5f00\u53d1\u9636\u6bb5\uff0c\u6027\u80fd\u8868\u73b0\u5b58\u5728\u6ce2\u52a8\uff1a\u542f\u7528\u540e\u6839\u636e\u5de5\u4f5c\u8d1f\u8f7d\u4e0d\u540c\uff0c\u53ef\u80fd\u4ea7\u751f 10% \u7684\u6027\u80fd\u4e0b\u964d\u81f3 20% \u7684\u6027\u80fd\u63d0\u5347\uff1b
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4. \u6a21\u677f\u5b57\u7b26\u4e32\uff08 t-string \uff0cPEP 750 \uff09\u652f\u6301\u3002\u8fd9\u4e00\u529f\u80fd\u4e3b\u8981\u662f\u5bf9 f-string \u7684\u8865\u5145\uff0c\u5141\u8bb8\u4ece\u7c7b\u4f3c\u5b57\u7b26\u4e32\u5b57\u9762\u91cf\u7684\u5199\u6cd5\u76f4\u63a5\u521b\u5efa\u4e00\u4e2a\u5b57\u7b26\u4e32\u6a21\u677f\u5bf9\u8c61\uff1b
\n
5. \u589e\u91cf\u5f0f\u5783\u573e\u56de\u6536\u3002\u5faa\u73af\u5783\u573e\u56de\u6536\u5668\u73b0\u5728\u91c7\u7528\u589e\u91cf\u5f0f\u5904\u7406\u3002\u8fd9\u610f\u5473\u7740\u5bf9\u4e8e\u8f83\u5927\u7684\u5806\u5185\u5b58\uff0c\u6700\u5927\u6682\u505c\uff08 Stop The World \uff09\u65f6\u95f4\u5c06\u51cf\u5c11\u4e00\u4e2a\u6570\u91cf\u7ea7\u6216\u66f4\u591a\uff1b
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6. \u4ea4\u4e92\u5f0f Shell \u652f\u6301\u8bed\u6cd5\u9ad8\u4eae\u3002\u9664\u975e\u663e\u5f0f\u7981\u7528\uff0c\u5426\u5219\u4ece Python 3.14 \u8d77\uff0cPython \u4ea4\u4e92\u5f0f\u73af\u5883\uff08 RHEL \uff09\u5c06\u9ed8\u8ba4\u5728\u7ec8\u7aef\u4e2d\u6e32\u67d3\u4ee3\u7801\u9ad8\u4eae\u3002\u6b64\u5916\uff0cShell \u8fd8\u589e\u52a0\u4e86\u5bf9 import ___ \u7684\u81ea\u52a8\u8865\u5168\uff1b
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7. asyncio \u5185\u7701\u80fd\u529b\u3002\u53ef\u4ee5\u4f7f\u7528 python\u00a0-m\u00a0asyncio\u00a0<ps|pstree>\u00a0<PID> \u529f\u80fd\u6765\u68c0\u67e5\u6b63\u5728\u8fd0\u884c\u7684\u5f02\u6b65 Python \u7a0b\u5e8f\u7684 async task \u6811\u3002
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\u66f4\u591a\u53ef\u5728\u8fd9\u91cc\u770b\u5230\uff1a https://docs.python.org/zh-cn/3.14/whatsnew/3.14.html

https://github.com/allmonday/fastapi-router-viz

\u5bf9\u4e8e\u9075\u5faa er \u6a21\u578b\u6765\u6784\u5efa\u89c6\u56fe\u6570\u636e\u7684\u9879\u76ee\uff0cfastapi-router-viz \u53ef\u4ee5\u4e3a\u4e86\u89e3 api \u8fd4\u56de\u7c7b\u578b\u95f4\u7684\u5173\u7cfb\u63d0\u4f9b\u5feb\u901f\uff0c\u76f4\u63a5\uff0c\u53ef\u4ea4\u4e92\u7684\u56fe\u6548\u679c\u3002

\u53ef\u4ee5\u901a\u8fc7\u70b9\u51fb\u8282\u70b9\u9ad8\u4eae\u5168\u90e8\u4e0a\u4e0b\u6e38\u94fe\u8def\uff0c \u4e86\u89e3 pydantic class \u7684\u4e0a\u4e0b\u6e38\u4f9d\u8d56\u60c5\u51b5

alt \u70b9\u51fb\u67e5\u770b\u8282\u70b9\u7684\u6e90\u4ee3\u7801\uff0c\u6216\u8005\u76f4\u63a5\u5728 vscode \u4e2d\u6253\u5f00

\u6839\u636e class + field name \u6765\u7cbe\u51c6\u5b9a\u4f4d\u6570\u636e\u88ab\u54ea\u4e9b\u9875\u9762/ \u63a5\u53e3\u4f7f\u7528

\u8fd9\u95ee\u9898\u90fd\u51fa\u73b0\u534a\u5e74\u4e86\u3002\u5c1d\u8bd5\u7528 AI \u89e3\u51b3\uff0c\u7ed9\u6211\u8bd5\u4e86\u534a\u5929\u8fd8\u90fd\u662f too many request.

\u6211\u7684\u7f51\u7edc\u662f\u53ef\u4ee5\u7528 gemini cli \u4e4b\u7c7b\u7684\uff0c\u6309\u7406\u8bf4\u5e94\u8be5\u4e0d\u662f\u7f51\u7edc\u95ee\u9898\u554a\u3002

\u7b80\u5355\u6d4b\u8bd5\u4ee3\u7801\u5982\u4e0b\uff1a

import yfinance as yf\nimport pandas as pd\n\n# \u8bbe\u7f6e\u6c38\u8f89\u8d85\u5e02\u7684\u80a1\u7968\u4ee3\u7801\nticker = \"601933.SS\" # .SS \u8868\u793a\u4e0a\u6d77\u8bc1\u5238\u4ea4\u6613\u6240\n\n# \u521b\u5efa yfinance \u5bf9\u8c61\nstock = yf.Ticker(ticker)\n\n# \u83b7\u53d6\u5b9e\u65f6\u4ef7\u683c\uff08\u6700\u65b0\u6536\u76d8\u4ef7\u6216\u5f53\u524d\u4ef7\u683c\uff0c\u89c6\u5e02\u573a\u60c5\u51b5\u800c\u5b9a\uff09\ncurrent_price = stock.history(period=\"1d\")[\"Close\"].iloc[-1]\n\n# \u83b7\u53d6\u80a1\u7968\u7684\u8be6\u7ec6\u4fe1\u606f\uff08\u5305\u62ec\u516c\u53f8\u540d\u79f0\u7b49\uff09\ninfo = stock.info\n\n# \u8f93\u51fa\u7ed3\u679c\nprint(f\"\u6c38\u8f89\u8d85\u5e02 ({ticker}) \u7684\u5b9e\u65f6\u4ef7\u683c: {current_price:.2f} CNY\")\nprint(f\"\u516c\u53f8\u540d\u79f0: {info.get('shortName', '\u672a\u77e5')}\")\n

\u8fd0\u884c\u5c31\u76f4\u63a5\u62a5\u9519\uff1a

 line 11, in <module>\n current_price = stock.history(period=\"1d\")[\"Close\"].iloc[-1]\n ^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/anaconda3/lib/python3.12/site-packages/yfinance/utils.py\", line 103, in wrapper\n result = func(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/anaconda3/lib/python3.12/site-packages/yfinance/base.py\", line 91, in history\n return self._lazy_load_price_history().history(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/anaconda3/lib/python3.12/site-packages/yfinance/utils.py\", line 103, in wrapper\n result = func(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/anaconda3/lib/python3.12/site-packages/yfinance/scrapers/history.py\", line 178, in history\n data = get_fn(\n ^^^^^^^\n File \"/opt/anaconda3/lib/python3.12/site-packages/yfinance/utils.py\", line 103, in wrapper\n result = func(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/anaconda3/lib/python3.12/site-packages/yfinance/data.py\", line 364, in get\n return self._make_request(url, request_method = self._session.get, user_agent_headers=user_agent_headers, params=\nparams, timeout=timeout)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/anaconda3/lib/python3.12/site-packages/yfinance/utils.py\", line 103, in wrapper\n result = func(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/anaconda3/lib/python3.12/site-packages/yfinance/data.py\", line 424, in _make_request\n raise YFRateLimitError()\nyfinance.exceptions.YFRateLimitError: Too Many Requests. Rate limited. Try after a while.\n

\u8fd9\u8981\u600e\u4e48\u89e3\u51b3\u5462\uff0c\u96be\u9053 yahoo \u7684 API \u5c01\u8fd9\u4e48\u4e25\u91cd\u554a\u3002\u6709\u6ca1\u6709\u77e5\u9053\u600e\u4e48\u89e3\u51b3\u7684\u8001\u5144\u554a\uff0c\u611f\u8c22\uff01

\u53d1\u73b0 Celery 4.2.3 \u4e0d\u652f\u6301\u7c7b\u65b9\u6cd5

\u4f8b\u5b50\uff1a

class Demo:\n \u00a0@app.task\n \u00a0@classmethod\n \u00a0def test(cls):\n \u00a0 \u00a0 \u00a0pass\n \n\nschedule \u6ce8\u518c\u4efb\u52a1\uff1a\nDemo.test

\u76ee\u524d\u5df2\u6709 i18n \u5e93\u7684\u75db\u70b9

\n
1. Python \u81ea\u5e26\u7684 gettext \u4e0d\u652f\u6301\u52a8\u6001\u9009\u62e9\u8bed\u8a00 (\u6bd4\u5982 Bot \u9700\u8981\u6839\u636e\u7528\u6237\u9009\u62e9\u8bed\u8a00)
\n
2. \u5176\u4ed6\u5e93\u9700\u8981\u624b\u52a8\u7ef4\u62a4\u7ffb\u8bd1\u5b57\u5178, \u5f00\u53d1\u6548\u7387\u4f4e
\n
3. \u90fd\u4e0d\u652f\u6301 f-string \u8bed\u6cd5, \u5199\u8d77\u6765\u4e0d\u591f\u4f18\u96c5, \u4e0d\u591f\u65b9\u4fbf
\n

\u6240\u4ee5, Easy AI18n \u8bde\u751f\u4e86, \u89e3\u51b3\u4e86\u4ee5\u4e0a\u6240\u6709\u75db\u70b9

Easy AI18n \u662f\u4e00\u4e2a\u73b0\u4ee3\u5316\u7684 Python3 \u56fd\u9645\u5316\u5de5\u5177\u5e93\uff0c\u652f\u6301 AI \u7ffb\u8bd1\u3001\u591a\u7528\u6237\u573a\u666f\u4ee5\u53ca\u5b8c\u6574\u7684\u5b57\u7b26\u4e32\u683c\u5f0f\u5316\u8bed\u6cd5\uff0c\u8ba9\u9879\u76ee\u5168\u7403\u5316\u53d8\u5f97\u66f4\u52a0\u4f18\u96c5\u81ea\u7136\u3002

https://github.com/z-mio/easy-ai18n

\u7279\u70b9

\n
• \u81ea\u52a8\u63d0\u53d6\u5185\u5bb9\u751f\u6210\u7ffb\u8bd1\u5b57\u5178, \u63d0\u9ad8\u5f00\u53d1\u6548\u7387
\n
• \u652f\u6301\u52a8\u6001\u591a\u8bed\u8a00, \u9002\u914d\u591a\u7528\u6237\u573a\u666f
\n
• \u652f\u6301\u6240\u6709\u683c\u5f0f\u5316\u8bed\u6cd5, \u4ee3\u7801\u66f4\u4f18\u96c5
\n
• \u652f\u6301 AI \u7ffb\u8bd1, \u7ffb\u8bd1\u8d28\u91cf\u66f4\u597d
\n

\u5bf9\u6bd4

\u5176\u4ed6 i18n \u5de5\u5177	EasyAI18n
\u9700\u624b\u52a8\u7ef4\u62a4\u7ffb\u8bd1\u5b57\u5178, \u5f00\u53d1\u6548\u7387\u4f4e	\u81ea\u52a8\u63d0\u53d6\u7ffb\u8bd1\u5185\u5bb9\uff0c\u65e0\u9700\u624b\u52a8\u7ef4\u62a4\u6587\u4ef6
\u4ec5\u652f\u6301\u90e8\u5206\u683c\u5f0f\u5316\u8bed\u6cd5	\u5b8c\u5168\u652f\u6301\u6240\u6709\u683c\u5f0f\u5316\u8bed\u6cd5
\u4e0d\u652f\u6301\u5b9e\u65f6\u591a\u8bed\u8a00\u5207\u6362\uff0c\u4e0d\u9002\u7528\u4e8e\u591a\u7528\u6237\u573a\u666f	\u652f\u6301\u9ed8\u8ba4\u8bed\u8a00\u4e0e\u591a\u8bed\u8a00\u5207\u6362\uff0c\u9002\u914d\u591a\u7528\u6237\u73af\u5883

\ud83d\udc65 \u591a\u7528\u6237\u591a\u8bed\u8a00\u573a\u666f\uff08\u5982 Telegram Bot \uff09

\u901a\u8fc7\u81ea\u5b9a\u4e49\u8bed\u8a00\u9009\u62e9\u5668, \u5728\u591a\u7528\u6237\u73af\u5883\u4e2d\u5b9e\u73b0\u52a8\u6001\u8bed\u8a00\u9009\u62e9:

/i18n.py:

from pyrogram.types import Message\nfrom easy_ai18n import EasyAI18n, PostLanguageSelector\n\n\nclass MyPostLanguageSelector(PostLanguageSelector):\n def __getitem__(self, msg: Message):\n lang = msg.from_user.language_code\n return super().__getitem__(lang)\n\n\ni18n = EasyAI18n()\n\n_ = i18n.i18n(post_lang_selector=MyPostLanguageSelector)\n\nif __name__ == \"__main__\":\n i18n.build(target_lang=['en', 'ru'])\n

/bot.py:

@bot.on_message()\nasync def start(__, msg: Message):\n await msg.reply(_[msg](\"Hello, world!\"))\n

RT \uff0c\u8c22\u8c22\u5404\u4f4d\u6709\u7ecf\u9a8c\u7684\u5927\u4f6c

\u5982\u9898\uff0c\u4ee3\u7801\u6b63\u5e38\u80fd\u8dd1\u7684\u3002\u4f46\u662f debug \u6a21\u5f0f pycharm \u4f1a\u81ea\u52a8\u6253\u5370\u5bf9\u8c61\u3002\u7136\u540e\u5c31\u5806\u6808\u6ea2\u51fa\u4e86\uff0c\n\u8fd8\u770b\u4e0d\u5230\u5177\u4f53\u662f\u4ec0\u4e48\u8c03\u7528\u5bfc\u81f4\u7684\u6ea2\u51fa\uff0c\u53ea\u6709

\u8fdb\u7a0b\u5df2\u7ed3\u675f\uff0c\u9000\u51fa\u4ee3\u7801\u4e3a -1073741571 (0xC00000FD) \u7684\u9000\u51fa\u7801\n

\u559c\u6b22\u7684\u53ef\u4ee5 star \u4e00\u4e0b\u54c8\uff0c\u66f4\u5feb\u66f4\u5f3a

https://github.com/auv-sh/av

\u6709\u6ca1\u6709\u4e00\u8d77\u5b66\u6df1\u5ea6\u5b66\u4e60\u548c Transformer \u7684\uff0c\u6216\u8005\u9700\u8981\u5199\u8bba\u6587\u7684\uff0c\u6211\u4e70\u4e86\u5957\u6211\u89c9\u5f97\u8fd8\u4e0d\u9519\u7684\u8bfe\uff0c\u6211\u5df2\u7ecf\u57fa\u672c\u4e0a\u5b66\u5b8c\u4e86\uff0c\u4e5f\u624b\u64b8\u4e86\u51e0\u4e2a CNN \u3001LSTM \u3001Transformer \u7684\u6a21\u578b\u3002\u4f46\u662f\u8bfe 3K \u4e70\u7684\uff0c\u5c31\u60f3\u627e\u51e0\u4e2a\u60f3\u5b66\u7684\uff0c\u6211\u597d\u56de\u70b9\u672c\uff01

\u6211\u60f3\u4e86\u5f88\u4e45\u4e5f\u6ca1\u60f3\u660e\u767d\u4e3a\u4ec0\u4e48\u8981\u624b\u64b8\u4e00\u4e2a\u652f\u6301\u4f20\u5165 numpy \u8fdb\u884c\u8fd0\u7b97\u7684\u77e9\u9635\u7c7b\u800c\u4e0d\u662f\u76f4\u63a5\u7528 numpy \uff1f\n

>>> type({/})\n<class 'set'>\n>>> {/} == set()\nTrue\n

\u611f\u89c9\u597d\u5947\u602a\u7684\u8bed\u6cd5\uff0c\uff0c\u4e3a\u5565\u975e\u8981\u5f15\u5165\u4e00\u4e2a\u7a7a set \u8bed\u6cd5\uff1f\uff1f\u6709\u5565\u7279\u522b\u5927\u7684\u597d\u5904\u5417\uff1f\uff1f set() \u4e5f\u633a\u77ed\u7684\u554a\uff1f\uff1f

\u60f3\u8981\u722c\u7684\u7f51\u7ad9\uff1a https://crushon.ai/character/7e388767-63c7-45e3-b922-53458b4ae4f7/chat

\u4e00\u76f4\u8bf7\u6c42 403 \uff0c\u96be\u53d7\uff0c\u641e\u4e0d\u5b9a\uff0c\u7528\u65e0\u5934 playwrihgt \uff0c\u53c8\u4e0d\u8fc7 cloudflare \uff0c\u8bf7\u6c42\u53c8\u88ab\u62d2\u7edd\uff0c\u96be\u641e\uff01

\u60f3\u8bf7\u6559\u4e00\u4e0b v2 \u7684\u5404\u4f4d\u4f6c\uff0c\u600e\u4e48\u722c\u8fd9\u4e2a\u7f51\u7ad9\u804a\u5929\u5bf9\u8bdd\u3002

\n\u8ba9\u4ed6\u6539\u4e2a\u7b97\u6cd5\u4ee3\u7801\uff0c\u628a\u6211\u4ee3\u7801\u7ed9\u5220\u9519\u4e86\uff0c\u5bfc\u81f4\u6574\u4e2a\u5de5\u7a0b\u51fa\u73b0\u4e0d\u53ef\u63a7\u95ee\u9898

\u5982\u9898\uff0c\u9009\u6280\u672f\u6808 ing \uff0c\u4e0d\u77e5\u9053\u6709\u6ca1\u6709\u5751

\u65e5\u5e38\u9700\u8981\u628a\u8c37\u6b4c\u8ddf\u8e2a\u4ee3\u7801\u52a0\u5165\u5230 head \u5143\u7d20\u91cc\u9762\uff0c\u6240\u4ee5\u501f\u52a9 AI \u5199\u4e86\u4e00\u4e2a\u5c0f\u5de5\u5177\u3002

1 \u3001\u628a\u9700\u8981\u52a0\u5165\u7684\u5185\u5bb9\u4fdd\u5b58\u5230 GACode.txt \u6587\u4ef6\u4e2d

2 \u3001\u6267\u884c\u4ee3\u7801\uff0c\u8f93\u5165\u76ee\u5f55\u5373\u53ef\u3002\uff08\u53ea\u4f1a\u4fee\u6539 html \u6269\u5c55\u540d\u7684\u6587\u4ef6\uff09

\u4ee3\u7801

import os\nimport re\nimport sys\nfrom pathlib import Path\n\ndef get_resource_path(relative_path):\n \"\"\"\u83b7\u53d6\u8d44\u6e90\u6587\u4ef6\u7684\u7edd\u5bf9\u8def\u5f84\uff0c\u652f\u6301 PyInstaller \u6253\u5305\"\"\"\n try:\n # PyInstaller \u521b\u5efa\u4e34\u65f6\u6587\u4ef6\u5939\uff0c\u5e76\u5c06\u8def\u5f84\u5b58\u50a8\u5728_MEIPASS \u4e2d\n base_path = sys._MEIPASS\n except Exception:\n base_path = os.path.abspath(\".\")\n return os.path.join(base_path, relative_path)\n\ndef check_and_add_google_analytics():\n \"\"\"\n \u68c0\u67e5\u6240\u6709 HTML \u6587\u4ef6\u662f\u5426\u5305\u542b Google Analytics \u4ee3\u7801\uff0c\u5982\u679c\u4e0d\u5b58\u5728\u5219\u6dfb\u52a0\n \"\"\"\n # \u521d\u59cb\u5316\u7edf\u8ba1\u4fe1\u606f\n stats = {\n 'total': 0,\n 'modified': 0,\n 'skipped': 0,\n 'errors': 0\n }\n \n # \u4ece GACode.txt \u6587\u4ef6\u8bfb\u53d6 Google Analytics \u4ee3\u7801\n ga_code_file = get_resource_path('GACode.txt')\n \n try:\n with open(ga_code_file, 'r', encoding='utf-8') as f:\n ga_code = f.read().strip()\n except FileNotFoundError:\n print(f\"\u9519\u8bef\uff1a\u627e\u4e0d\u5230\u6587\u4ef6 {ga_code_file}\")\n stats['errors'] = 1\n return stats\n except Exception as e:\n print(f\"\u9519\u8bef\uff1a\u8bfb\u53d6 GA \u4ee3\u7801\u6587\u4ef6\u65f6\u51fa\u9519: {str(e)}\")\n stats['errors'] = 1\n return stats\n \n if not ga_code:\n print(\"\u9519\u8bef\uff1aGA \u4ee3\u7801\u6587\u4ef6\u4e3a\u7a7a\")\n stats['errors'] = 1\n return stats\n \n # \u83b7\u53d6\u5f53\u524d\u76ee\u5f55\u4e0b\u6240\u6709 HTML \u6587\u4ef6\n html_files = list(Path('.').glob('*.html'))\n stats['total'] = len(html_files)\n \n print(f\"\u627e\u5230 {len(html_files)} \u4e2a HTML \u6587\u4ef6\")\n \n for html_file in html_files:\n print(f\"\\n \u5904\u7406\u6587\u4ef6: {html_file}\")\n \n try:\n # \u8bfb\u53d6\u6587\u4ef6\u5185\u5bb9\n with open(html_file, 'r', encoding='utf-8') as f:\n cOntent= f.read()\n \n # \u68c0\u67e5\u662f\u5426\u5df2\u7ecf\u5305\u542b Google Analytics \u4ee3\u7801\n if 'googletagmanager.com/gtag/js' in content:\n print(f\" \u2713 {html_file} \u5df2\u5305\u542b Google Analytics \u4ee3\u7801\uff0c\u8df3\u8fc7\")\n stats['skipped'] += 1\n continue\n \n # \u67e5\u627e</head>\u6807\u7b7e\u7684\u4f4d\u7f6e\n head_end_pattern = r'</head>'\n match = re.search(head_end_pattern, content, re.IGNORECASE)\n \n if not match:\n print(f\" \u2717 {html_file} \u672a\u627e\u5230</head>\u6807\u7b7e\uff0c\u8df3\u8fc7\")\n stats['errors'] += 1\n continue\n \n # \u5728</head>\u6807\u7b7e\u524d\u63d2\u5165 Google Analytics \u4ee3\u7801\n head_end_pos = match.start()\n new_cOntent= (\n content[:head_end_pos] +\n ' ' + ga_code + '\\n' +\n content[head_end_pos:]\n )\n \n # \u5199\u5165\u4fee\u6539\u540e\u7684\u5185\u5bb9\n with open(html_file, 'w', encoding='utf-8') as f:\n f.write(new_content)\n \n print(f\" \u2713 {html_file} \u5df2\u6210\u529f\u6dfb\u52a0 Google Analytics \u4ee3\u7801\")\n stats['modified'] += 1\n \n except Exception as e:\n print(f\" \u2717 \u5904\u7406 {html_file} \u65f6\u51fa\u9519: {str(e)}\")\n stats['errors'] += 1\n \n print(\"\\n \u5904\u7406\u5b8c\u6210\uff01\")\n return stats\n\nif __name__ == \"__main__\":\n try:\n # \u83b7\u53d6\u7528\u6237\u8f93\u5165\u7684\u76ee\u5f55\u8def\u5f84\n current_dir = input(\"\u8bf7\u8f93\u5165\u8981\u5904\u7406\u7684\u76ee\u5f55\u8def\u5f84: \").strip()\n \n # \u5982\u679c\u7528\u6237\u6ca1\u6709\u8f93\u5165\uff0c\u5219\u4f7f\u7528\u5f53\u524d\u76ee\u5f55\n if not current_dir:\n current_dir = os.getcwd()\n \n print(f\"\u5f53\u524d\u5de5\u4f5c\u76ee\u5f55: {current_dir}\")\n \n # \u5207\u6362\u5230\u6307\u5b9a\u76ee\u5f55\n try:\n os.chdir(current_dir)\n except FileNotFoundError:\n print(f\"\u9519\u8bef\uff1a\u76ee\u5f55 '{current_dir}' \u4e0d\u5b58\u5728\")\n input(\"\\n \u6309\u56de\u8f66\u952e\u9000\u51fa...\")\n exit(1)\n except PermissionError:\n print(f\"\u9519\u8bef\uff1a\u6ca1\u6709\u6743\u9650\u8bbf\u95ee\u76ee\u5f55 '{current_dir}'\")\n input(\"\\n \u6309\u56de\u8f66\u952e\u9000\u51fa...\")\n exit(1)\n \n # \u68c0\u67e5\u662f\u5426\u5728\u5305\u542b HTML \u6587\u4ef6\u7684\u76ee\u5f55\u4e2d\n html_files = list(Path('.').glob('*.html'))\n if not html_files:\n print(\"\u5f53\u524d\u76ee\u5f55\u4e0b\u6ca1\u6709\u627e\u5230 HTML \u6587\u4ef6\uff0c\u8bf7\u786e\u4fdd\u5728\u6b63\u786e\u7684\u76ee\u5f55\u4e0b\u8fd0\u884c\u6b64\u811a\u672c\")\n input(\"\\n \u6309\u56de\u8f66\u952e\u9000\u51fa...\")\n exit(1)\n \n # \u6267\u884c\u6dfb\u52a0 Google Analytics \u4ee3\u7801\u7684\u64cd\u4f5c\n stats = check_and_add_google_analytics()\n \n # \u68c0\u67e5\u8fd4\u56de\u503c\u662f\u5426\u6709\u6548\n if stats is None:\n print(\"\\n\u26a0 \u51fd\u6570\u6267\u884c\u5f02\u5e38\uff0c\u65e0\u6cd5\u83b7\u53d6\u7edf\u8ba1\u4fe1\u606f\")\n stats = {'total': 0, 'modified': 0, 'skipped': 0, 'errors': 1}\n \n # \u663e\u793a\u7edf\u8ba1\u7ed3\u679c\n print(\"\\n=== \u6267\u884c\u7edf\u8ba1 ===\")\n print(f\"\u603b\u6587\u4ef6\u6570: {stats['total']}\")\n print(f\"\u6210\u529f\u4fee\u6539: {stats['modified']} \u4e2a\")\n print(f\"\u5df2\u5b58\u5728\u8df3\u8fc7: {stats['skipped']} \u4e2a\")\n print(f\"\u5904\u7406\u5931\u8d25: {stats['errors']} \u4e2a\")\n \n if stats['modified'] > 0:\n print(f\"\\n\u2713 \u6210\u529f\u4e3a {stats['modified']} \u4e2a HTML \u6587\u4ef6\u6dfb\u52a0\u4e86 Google Analytics \u4ee3\u7801\uff01\")\n elif stats['total'] > 0 and stats['skipped'] == stats['total']:\n print(\"\\n\u2713 \u6240\u6709 HTML \u6587\u4ef6\u90fd\u5df2\u5305\u542b Google Analytics \u4ee3\u7801\uff0c\u65e0\u9700\u4fee\u6539\u3002\")\n else:\n print(\"\\n\u26a0 \u6ca1\u6709\u6210\u529f\u4fee\u6539\u4efb\u4f55\u6587\u4ef6\uff0c\u8bf7\u68c0\u67e5\u6587\u4ef6\u683c\u5f0f\u6216\u6743\u9650\u3002\")\n \n print(\"\\n=== \u7a0b\u5e8f\u6267\u884c\u5b8c\u6210 ===\")\n input(\"\\n \u6309\u56de\u8f66\u952e\u9000\u51fa...\")\n \n except KeyboardInterrupt:\n print(\"\\n\\n \u7a0b\u5e8f\u88ab\u7528\u6237\u4e2d\u65ad\")\n input(\"\\n \u6309\u56de\u8f66\u952e\u9000\u51fa...\")\n except Exception as e:\n print(f\"\\n \u7a0b\u5e8f\u6267\u884c\u65f6\u53d1\u751f\u672a\u77e5\u9519\u8bef: {str(e)}\")\n input(\"\\n \u6309\u56de\u8f66\u952e\u9000\u51fa...\")\n

\u6211\u5c31\u4e0d\u53d1\u6210\u54c1\u4e86\uff0c\u81ea\u884c\u6253\u5305\u54c8

\u770b\u56fe

logger \uff0crequests \u90fd\u5c5e\u4e8e\u7b2c\u4e09\u65b9\u6a21\u5757\u3002\u4f46\u662f logger \u5c31\u662f\u7ea2\u7ebf\u3002\u6700\u521d\u4ee5\u4e3a\u662f\u73af\u5883\u6ca1\u8bc6\u522b\uff0c\u4f46\u662f\u8fd0\u884c\u90fd\u662f\u6b63\u5e38\u7684\uff0c\u8fd9\u4e8b\u60c5\u5f88\u8d39\u89e3\u554a\u3002\u6709\u89e3\u51b3\u529e\u6cd5\u4e48\uff1f

\u548b\u4e2a\u6ca1\u56fe\uff0c\u8d34\u4e2a\u5730\u5740\u5427 https://imgur.com/C7tWIvh

\u524d\u51e0\u5929\u5728\u77e5\u4e4e\u4e0a\u770b\u5230\u8fd9\u7bc7\u6587\u7ae0\uff0c\uff0c\u5f53\u65f6\u5b8c\u5168\u6ca1\u8003\u8651\u6587\u7ae0\u7684\u771f\u5b9e\u6027\uff0c\uff0c\u53ea\u662f\u89c9\u5f97 Python \u600e\u4e48\u52a0\u8fd9\u4e48\u591a\u5947\u8469\u8bed\u6cd5\uff0c\uff0c\u8981\u5b8c\u3002\u3002

\u4eca\u5929\u7a0d\u5fae\u641c\u4e86\u4e0b\uff0c\uff0c\u53d1\u73b0\u6587\u7ae0\u4e2d\u63d0\u5230\u7684 PEP 727 \u3001PEP 740 \u5e76\u4e0d\u662f\u6587\u7ae0\u4e2d\u63cf\u8ff0\u7684\u5185\u5bb9\u3002\u3002\u4f3c\u4e4e\u6574\u7bc7\u6587\u7ae0\u90fd\u662f\u80e1\u7f16\u4e71\u9020\u7684\u3002\u3002

\u641e\u4e0d\u61c2\u8c01\u4f1a\u5199\u8fd9\u79cd\u4e1c\u897f\uff1f\uff1f\u96be\u9053\u662f AI \u751f\u6210\u7684\uff1f\uff1f

\u6b63\u5728\u5904\u7406\u4e00\u5ea7\u5c4e\u5c71\uff0c\u5927\u6982\u60c5\u51b5\u662f\u8fd9\u6837\u7684\u3002\u6709\u4e00\u4e2a\u81ea\u5b9a\u4e49\u7684class X\uff0c\u9700\u8981\u6539\u6389\u7684\u4e1c\u897f\u6ce8\u91ca\u5199\u7684\u662fy=Union[X, str]\uff0c\u5b9e\u9645\u60c5\u51b5\u4e5f\u662f\u4e24\u79cd\u7c7b\u578b\u5230\u5904\u90fd\u6df7\u5728\u4e00\u8d77\u6839\u672c\u5206\u4e0d\u6e05\u3002\u540e\u9762\u6240\u6709\u8ddfX\u7c7b\u6709\u5173\u7684\u65b9\u6cd5\u4e00\u70b9\u6ce8\u91ca\u6ca1\u5199\uff0c\u751a\u81f3\u90fd\u4e0d\u505a\u7c7b\u578b\u5224\u65ad\uff0c\u800c\u662f\u5927\u91cf\u7684\u4f7f\u7528getattr(y, 'name', y)\u628a\u6c34\u6405\u5f97\u66f4\u6d51\uff08\u5982\u679cy\u662f\u5b57\u7b26\u4e32\uff0c\u6ca1\u6709name\uff0c\u8fd4\u56de\u5b57\u7b26\u4e32\u672c\u8eab\u3002\u5426\u5219\u8fd4\u56dey.name\u4e5f\u662f\u4e00\u4e2a\u5b57\u7b26\u4e32\uff09\u3002\u6700\u79bb\u8c31\u7684\u662f\u6574\u5ea7\u5c4e\u5c71\u8fd8\u914d\u4e86\u4e00\u5957 30 \u5206\u949f\u624d\u80fd\u8dd1\u5b8c\u7684 pytest \u3002

\u73b0\u5728\u9700\u8981\u628a\u6df7\u4e71\u7684y\u5206\u6210\u786e\u5b9a\u7c7b\u578b\u7684y\u548cy_str\uff0c\u4f7f\u7528 pydantic \u5f3a\u5236\u5b9a\u4e49\u6570\u636e\u7c7b\u578b\u6765\u4e00\u70b9\u70b9\u6392\u9519\u3002\u7136\u800c\u6539\u4e86\u51e0\u767e\u884c\u4ee5\u540e\uff0c\u6240\u6709\u8ddfX\u6709\u5173\u7684\u65b9\u6cd5\u90fd\u88ab\u8feb\u6539\u51fa\u4e24\u79cd\u7248\u672c\u7684\u3002pytest \u4e0d\u518d\u62a5\u7c7b\u578b\u9519\u8bef\u4e86\uff0c\u5374\u5f00\u59cb\u51fa\u73b0\u5404\u79cd\u79bb\u5947\u7684 bug \u6302\u6389\u3002

\u6709\u4ec0\u4e48\u66f4\u5f3a\u5927\u7684\u5de5\u5177\u53ef\u4ee5\u641e\u5b9a\u8fd9\u4ef6\u4e8b\u5417\uff1f\u6bd4\u5982\u540c\u6b65\u68c0\u6d4b\u4e24\u4e2a\u7248\u672c\u7684\u4ee3\u7801\uff0c\u770b\u4ece\u54ea\u4e00\u6b65\u5f00\u59cb\u4e24\u8fb9\u7684\u6570\u636e\u53d8\u5f97\u4e0d\u4e00\u81f4\u4e86\uff1f

\u5f53 Django \u7ed3\u5408\u5f02\u6b65\u4efb\u52a1(\u5982 Celery \u6216\u8005 MQ \u7b49)\uff0c\u6267\u884c\u957f\u8017\u65f6\u4efb\u52a1\uff0cDjango \u4f1a\u4e3a\u8be5\u4efb\u52a1\u7684\u51fd\u6570\u7b2c\u4e00\u6b21\u8c03\u7528 ORM \u65f6\u5206\u914d\u4e00\u6761\u6570\u636e\u5e93\u8fde\u63a5\uff0c\u800c\u4e14\u8fd9\u4e2a\u8fde\u63a5\u5728 ORM \u8c03\u7528\u7ed3\u675f\u65f6\u4e0d\u4f1a\u7acb\u5373\u91ca\u653e\uff0c\u5f53\u4f60\u5728\u540c\u4e00\u4e2a\u4efb\u52a1(\u957f\u8017\u65f6)\u91cc\u5176\u4ed6\u5730\u65b9\u518d\u6b21\u8c03\u7528 ORM \u65f6\uff0c\u5c31\u4f1a\u51fa\u73b0\"MySQL server has gone away\"\u7684\u62a5\u9519(\u539f\u56e0\u662f MySQL \u4e3b\u52a8\u65ad\u5f00\u7684)

\u5b98\u65b9\u63a8\u8350\u89e3\u51b3\u65b9\u6848\uff1a\n\u5982\u679c\u5728 Django \u7684\u8bf7\u6c42-\u54cd\u5e94\u5468\u671f\u4e4b\u5916\u7684\u957f\u8fd0\u884c\u8fdb\u7a0b\u4e2d\u521b\u5efa\u4e86\u8fde\u63a5\uff0c\u8be5\u8fde\u63a5\u5c06\u4fdd\u6301\u6253\u5f00\u72b6\u6001\uff0c\u76f4\u5230\u663e\u5f0f\u5173\u95ed\u6216\u8d85\u65f6\u53d1\u751f\u3002\u4f60\u53ef\u4ee5\u4f7f\u7528 django.db.close_old_connections() \u6765\u5173\u95ed\u6240\u6709\u65e7\u7684\u6216\u4e0d\u53ef\u7528\u7684\u8fde\u63a5\u3002

\u6211\u89c9\u5f97\u592a low \u4e86\u3002\u7406\u8bba\u4e0a\uff0c\u53ea\u9700\u8981\u8bbe\u7f6e\u6bcf\u6b21\u8c03\u7528 ORM \u5f00\u59cb\u524d\u83b7\u53d6\u4e00\u6761\u8fde\u63a5\uff0c\u8c03\u7528\u7ed3\u675f\u540e\u5173\u95ed\u8fde\u63a5\u5373\u53ef\u89e3\u51b3\u8fd9\u4e2a\u95ee\u9898\u3002

\u6709\u6ca1\u6709\u5927\u4f6c\u9047\u5230\u8fc7\u8fd9\u4e2a\u95ee\u9898\uff1f\u5982\u4f55\u66f4\u52a0\u4f18\u96c5\u7684\u89e3\u51b3\uff1f

\u6700\u8fd1\u53d1\u73b0\u4e86\u8fd9\u4e2a\u4e8b\u60c5\u3002\u6709\u70b9\u60ca\u8bb6\uff01

pytest \u7684\u539f\u751f\u5c55\u793a\u4fe1\u606f\u8fd9\u4e48\u591a\u5e74\u4e86\u4e5f\u6ca1\u600e\u4e48\u53d8, \u6211\u53c2\u8003 nextest \u5199\u4e86\u63d2\u4ef6 pytest-modern, \u63d0\u4f9b\u66f4\u73b0\u4ee3\u5316\u7684\u8f93\u51fa

V \u53cb\u4eec\uff0c\u5e2e\u5fd9\u7ed9\u70b9\u6392\u67e5\u610f\u89c1

\u95ee\u9898\u4e0d\u662f\u5fc5\u73b0\u7684\uff0c\u5076\u5c14\u5c31\u51fa\u73b0\u4e00\u6b21

\u95ee\u4e86 AI \u7ed9\u7684\u4e5f\u662f\u6a21\u7cca\u7684\u7684\u56de\u7b54

\u8fde\u63a5 pg \u6570\u636e\u5e93\u7528\u7684\u662f pool = PooledDB()

\u6267\u884c\u4ee3\u7801\u5982\u4e0b

 def dbQuery(self, sql):\n cOnn= pool.connection()\n cur = conn.cursor()\n cur.execute(sql)\n result = cur.fetchall()\n cur.close()\n conn.close()\n return result\n

\u62a5\u9519\u5982\u4e0b\uff1a

could not receive data from server: Software caused connection abort (0x00002745/10053)\n\nTraceback (most recent call last):\n File \"aaTestV5.py\", line 844, in process\n input_values = self.getInputValuesInfo()\n File \"aa_process.py\", line 117, in getInputValuesInfo\n order_list = super(tf, self).getInputValuesInfo()\n File \"aaTestV5.py\", line 555, in getInputValuesInfo\n results_fromno, no_list = self.getFromnoResult()\n File \"aa_process.py\", line 366, in getFromnoResult\n return self.getFromnoResultInner(False)\n File \"aa_process.py\", line 320, in getFromnoResultInner\n results_fromno, no_list = super(tf, self).getFromnoResult(True)\n File \"aaTestV5.py\", line 119, in getFromnoResult\n result_fromno1 = self.dbQuery(query_sql)\n File \"aa_process.py\", line 391, in dbQuery\n return super(tf, self).dbQuery(sql)\n File \"aaTestV5.py\", line 823, in dbQuery\n cur.execute(sql)\n File \"\\python\\lib\\site-packages\\dbutils\\steady_db.py\", line 598, in tough_method\n result = method(*args, **kwargs) # try to execute\npsycopg2.DatabaseError: could not receive data from server: Software caused connection abort (0x00002745/10053)\n

https://github.com/python/pymanager

\u597d\u50cf\u662f\u521a\u53d1\u5e03\u7684, \u6ca1\u770b\u5230\u6709\u4eba\u8ba8\u8bba, \u6211\u4e5f\u662f\u6628\u5929\u770b\u5230\u7684

\u76ee\u524d\u53ea\u652f\u6301 windows , \u4f46\u5b98\u65b9\u4ea7\u54c1\u5e94\u8be5\u4ee5\u540e\u4f1a\u652f\u6301 linux, mac \u628a

\u5b98\u65b9\u603b\u7b97\u8ba4\u8bc6\u5230 python \u7684\u7248\u672c\u7ba1\u7406\u662f\u4e2a\u95ee\u9898\u4e86

\u6c34\u5370\u662f\u672a\u77e5\u7684\uff0c\u6709\u6ca1\u6709\u76f8\u5173\u6a21\u578b\u6216\u8005\u5de5\u5177\u53ef\u4ee5\u8bc6\u522b\u5462

win11 \u4e2d\u6709\u4e00\u4e2a\u6587\u4ef6\u76ee\u5f55\uff0c\u8fd9\u4e2a\u6587\u4ef6\u76ee\u5f55\u4e0b\u6709\u5f88\u591a\u5b50\u76ee\u5f55\u4ee5\u53ca\u5b50\u76ee\u5f55\u7684\u5b50\u76ee\u5f55\u7b49\u7b49\uff0c\u5728\u8fd9\u4e9b\u76ee\u5f55\u4e2d\u6709\u8bb8\u591a\u5e26 vba \u4ee3\u7801\u7684 excel \u6587\u4ef6\uff0c\u4e5f\u6709\u8bb8\u591a\u4e0d\u5e26 vba \u7684 excel \u6587\u4ef6\uff0c\u5927\u7ea6\u51e0\u5341\u4e07\u4e2a\uff0c\u8fd9\u4e9b excel \u6587\u4ef6\u7684\u7248\u672c\u4e0d\u4e00\u6837\u3002\u73b0\u5728\u9700\u8981\u7f16\u5199\u4e00\u4e2a\u7a0b\u5e8f\uff0c\u5728\u6781\u77ed\u65f6\u95f4\u5185\u627e\u51fa\u76ee\u5f55\u4e0b\u6240\u6709\u5e26 vba \u4ee3\u7801\u7684 excel \u6587\u4ef6\uff0c\u5e76\u628a\u5b83\u4eec\u8f6c\u6362\u6210\u6ca1\u6709 vba \u4ee3\u7801\u7684 excel \u6587\u4ef6\uff0c\u4fdd\u6301\u539f\u6765\u7684\u6587\u4ef6\u7248\u672c\u548c\u539f\u6765\u7684\u6587\u4ef6\u540d\uff0c\u5927\u5bb6\u6709\u4ec0\u4e48\u597d\u7684\u65b9\u6848\uff1f

\u4f7f\u7528 SVHN \u7684\u6570\u636e\u96c6\u8fdb\u884c\u6a21\u578b\u7684\u8bad\u7ec3\uff0c\u4f46\u662f\u6574\u4e2a\u6a21\u578b\u5728\u8bad\u7ec3\u96c6\u4e0a\u7684\u51c6\u786e\u7387\u662f\u4e00\u76f4\u5728\u4e0a\u5347\u7684\uff0c\u4f46\u662f\u5230\u4e86\u6d4b\u8bd5\u96c6\u4e0a\u5c31\u4e00\u76f4\u5361\u5728 95%\uff0c\u90fd 3 \u5929\u4e86\uff0c\u6c42\u5404\u4f4d\u5927\u4f6c\u5e2e\u6211\u770b\u4e0b\u6709\u6ca1\u6709\u4f18\u5316\u7684\u65b9\u6848\uff01\u8dea\u8c22!

import torch\nimport torch.nn as nn\nimport torch.optim as optim\nfrom torch.distributed.checkpoint import load_state_dict\nfrom torch.hub import load_state_dict_from_url\nfrom torch.nn.modules.loss import _Loss\nfrom torch.optim import Optimizer\nfrom torch.utils.data import Dataset, random_split, DataLoader\nimport torchvision\nfrom torchvision.transforms import transforms\nimport torchvision.models as m\nimport matplotlib.pyplot as plt\nimport random\nimport gc # \u7528\u4e8e\u5783\u573e\u56de\u6536\nfrom torchinfo import summary\nimport numpy as np\nimport random\n\nimport gc\n\n# \u8bbe\u7f6e\u968f\u673a\u6570\u79cd\u5b50\nSEED = 420\nrandom.seed(SEED)\nnp.random.seed(SEED)\ntorch.manual_seed(SEED)\ntorch.cuda.manual_seed(SEED)\ntorch.cuda.manual_seed_all(SEED)\ntorch.backends.cudnn.deterministic = True\ntorch.backends.cudnn.benchmark = False\n\n# \u8bbe\u7f6e\u4f7f\u7528 gpu \u8fd8\u662f cpu \u8fdb\u884c\u8bad\u7ec3\ndevice = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n\n# \u5b9a\u4e49\u8bad\u7ec3\u7684\u8bba\u8ff0\nepochs = 100\nlr = 0.0001\n\n# \u5b9a\u4e49\u6570\u636e\u96c6\u9700\u8981\u7684\u53c2\u6570\nbatchSize = 64\n\n# \u52a0\u8f7d\u8bad\u7ec3\u96c6\u9700\u8981\u7684\u6570\u636e\u8f6c\u6362\u5668\ntrainT = transforms.Compose([\n transforms.RandomCrop(28),\n transforms.RandomRotation(degrees=[-15, 15]),\n transforms.ToTensor(),\n transforms.Normalize(mean = [0.4377, 0.4438, 0.4728], std = [0.1980, 0.2010, 0.1970])\n])\n\n# \u52a0\u8f7d\u6d4b\u8bd5\u96c6\u9700\u8981\u7684\u6570\u636e\u8f6c\u6362\u5668\ntestT = transforms.Compose([\n transforms.CenterCrop(28),\n transforms.ToTensor(),\n transforms.Normalize(mean = [0.4377, 0.4438, 0.4728], std = [0.1980, 0.2010, 0.1970])\n])\n\n# \u52a0\u8f7d\u8bad\u7ec3\u96c6\u6570\u636e\nsvhn_train = torchvision.datasets.SVHN(root='C:\\\\FashionMNIST'\n , split=\"train\"\n , download=True\n , transform=trainT\n )\n# \u52a0\u8f7d\u6d4b\u8bd5\u96c6\u6570\u636e\nsvhn_test = torchvision.datasets.SVHN(root='C:\\\\FashionMNIST'\n , split=\"test\"\n , download=True\n , transform=testT\n )\n\n# \u5b9a\u4e49\u795e\u7ecf\u7f51\u7edc\uff0c\u56e0\u4e3a\u6211\u4eec\u7684\u56fe\u7247\u7684\u5c3a\u5bf8\u548c\u6837\u672c\u6570\u91cf\u90fd\u4e0d\u662f\u5f88\u5927\uff0c\u6240\u4ee5\u9009\u62e9\u4ece ResNet18 \u548c Vgg16 \u4e2d\u62bd\u53d6\u5c42\u6765\u6784\u5efa\u7f51\u7edc\nresnet18_ = m.resnet18()\n\n\nclass MyResNet(nn.Module): # \u8fd9\u4e2a\u662f\u57fa\u4e8e ResNet18 \u6784\u5efa\u7684\u7f51\u7edc\n def __init__(self):\n super(MyResNet, self).__init__()\n self.block1 = nn.Sequential(\n nn.Conv2d(3, 64, 3, 1, 1),\n resnet18_.bn1,\n resnet18_.relu\n )\n\n self.block2 = resnet18_.layer2 # \u8fde\u6743\u91cd\u90fd\u4f1a\u590d\u7528\u8fc7\u6765\uff0c\u5728 resnet18_ = m.resnet18() \u8fd9\u513f\u5c31\u5df2\u7ecf\u521d\u59cb\u5316\u597d\u4e86\u6743\u91cd\u6570\u636e\uff01\n self.block3 = resnet18_.layer3\n self.block4 = resnet18_.layer4 # \u4ece Resnet18 \u4e2d\u54ea layer \u65b0\u589e\u5230\u81ea\u5df1\u7684\u6a21\u578b\u4e2d\n self.avgpool = resnet18_.avgpool\n\n self.fc = nn.Linear(512, 10, True)\n\n def forward(self, x):\n x = self.block1(x)\n x = self.block2(x)\n x = self.block3(x)\n x = self.block4(x) # \u8fd9\u513f\u65b0\u589e\u4e00\u6761\u5904\u7406\u4ee3\u7801\n x = self.avgpool(x)\n x = x.view(-1, 512)\n return self.fc(x)\n\n\nvgg16_ = m.vgg16()\n\n\nclass MyVgg(nn.Module): # \u8fd9\u4e2a\u662f\u57fa\u4e8e Vgg16 \u6784\u5efa\u7684\u7f51\u7edc\n def __init__(self):\n super(MyVgg, self).__init__()\n\n self.features = nn.Sequential(\n *vgg16_.features[0:9],\n # \u4f7f\u7528*\u662f \u5c06 .features[0:9]\u63d0\u53d6\u51fa\u6765\u7684\u5c42\uff0c\u5168\u90e8\u53d6\u51fa\u6765\uff0c\u4e00\u4e2a\u4e2a\u653e\u5230\u5f53\u524d\u7684 Sequential \u4e2d\uff0c\u800c\u4e0d\u662f\u7ec4\u6210\u4e00\u4e2a Sequential \u653e\u5230\u5f53\u524d\u7684 Sequential \u4e2d\uff01\n nn.Conv2d(128, 128, 3, 1, 1),\n nn.ReLU(inplace=True),\n nn.MaxPool2d(2, 2, padding=0, dilation=1, ceil_mode=False)\n )\n self.avgpool = vgg16_.avgpool\n\n self.fc = nn.Sequential(\n nn.Linear(6272, 4096, True),\n *vgg16_.classifier[1:6],\n nn.Linear(4096, 10, True)\n )\n\n def forward(self, x):\n x = self.features(x)\n x = self.avgpool(x)\n x = x.view(-1, 6272)\n x = self.fc(x)\n return x\n\n\n# summary(MyVgg(), input_size=(10, 3, 28, 28)) # \u4e00\u5b9a\u8981\uff0c\u5b9e\u4f8b\u5316\u8dd1\u4e00\u4e0b\uff0c\u770b\u770b\u6709\u6ca1\u6709\u95ee\u9898\uff01\n\n\nclass earlyStopping():\n\n def __init__(self, patience=5, tol=0.0005):\n # \u5f53\u8fde\u7eed patience=5 \u6b21\uff0c\u672c\u8f6e\u6b21\u7684\u8fed\u4ee3\u7684\u635f\u5931\u4e0e\u5386\u53f2\u6700\u5c0f\u7684\u635f\u5931\u7684\u5dee\u503c\u5927\u4e8e 0.0005 \u8fd9\u4e2a\u9608\u503c\uff0c\u5c31\u4f1a\u505c\u6b62\u8bad\u7ec3\n self.patience = patience\n self.tol = tol\n self.counter = 0 # \u8ba1\u6570\u5668\n self.lowest_loss = None # \u8bb0\u5f55\u5386\u53f2\u6700\u5c0f\u635f\u5931\n self.early_stop = False # \u9700\u8981\u8fd4\u56de\u662f\u5426\u9700\u8981\u63d0\u524d\u505c\u6b62\n\n def __call__(self, val_loss): # val_loss \u662f\u8bb0\u5f55\u6d4b\u8bd5\u96c6\u6216\u8bad\u7ec3\u96c6\u4e0a\u4e00\u6b21 epoch \u7684\u635f\u5931\n if self.lowest_loss is None:\n self.lowest_loss = val_loss\n elif self.lowest_loss - val_loss > self.tol:\n self.lowest_loss = val_loss\n self.counter = 0\n elif self.lowest_loss - val_loss < self.tol:\n self.counter += 1\n print('Notice: Early stopping counter {} of {}'.format(self.counter, self.patience))\n\n if self.counter >= self.patience:\n print('Notice: Early stopping counter Active')\n self.early_stop = True\n return self.early_stop\n\n\n# \u5b9a\u4e49\u8bad\u7ec3\u51fd\u6570\ndef fit(net: nn.Module, lossFunc: _Loss, op: Optimizer, trainData: DataLoader, testData: DataLoader, epochs: int):\n transLost = [] # \u7528\u4e8e\u6536\u96c6\u6bcf\u8f6e\u8bad\u7ec3\u548c\u6d4b\u8bd5\u7684\u7ed3\u679c\uff0c\u7528\u4e8e\u540e\u9762\u753b\u56fe\u8868\u4f7f\u7528\n trainCorrect = []\n testLost = []\n testCorrect = []\n trainedSampleNum = 0\n\n # \u521d\u59cb\u5316 earlystopping \u7c7b\n early_stopping = earlyStopping(patience=15, tol=0.00000005)\n # \u521d\u59cb\u5316\u6d4b\u8bd5\u96c6\u7684\u5386\u53f2\u6700\u9ad8\u51c6\u786e\u7387\n test_highest_correct = None\n test_lowest_loss = None\n\n # \u83b7\u53d6\u5230\u6574\u4e2a\u8bad\u7ec3\u96c6\u4e2d\u7684\u6837\u672c\u6570\u91cf\n trainTotalNum = trainData.dataset.__len__()\n # \u83b7\u53d6\u5230\u6574\u4e2a\u6d4b\u8bd5\u96c6\u4e2d\u7684\u6837\u672c\u6570\u91cf\n testTotalNum = testData.dataset.__len__()\n\n for epoch in range(epochs):\n net.train()\n train_loss = 0\n train_correct = 0\n for batch_index, (data, target) in enumerate(trainData):\n data = data.to(device, non_blocking=True)\n target = target.to(device, non_blocking=True).view(data.shape[0]) # \u786e\u4fdd\u6807\u7b7e\u662f 1 \u7ef4\u7684\u7ed3\u6784\n trainRes = net(data) # \u7ecf\u8fc7\u5b66\u4e60\uff0c\u8fd9\u513f\u6bcf\u4e2a\u6837\u672c\u4f1a\u8f93\u51fa 10 \u4e2a\u7279\u5f81\u7ed3\u679c\u5bf9\u5e94\u7684\u6570\u636e(\u5982\u679c\u6a21\u578b\u4e2d\u6709 softmax \uff0c\u5c31\u662f\u6982\u7387)\uff0c\u53ef\u4ee5\u7528\u4e8e\u540e\u7eed\u8ba1\u7b97\u51c6\u786e\u7387\n loss = lossFunc(trainRes, target)\n op.zero_grad() # \u6e05\u7a7a\u4f18\u5316\u5668\u4e0a\u7684\u68af\u5ea6\n loss.backward()\n op.step()\n # \u5f00\u59cb\u8ba1\u7b97\u51c6\u786e\u6570\uff0c\u5e76\u7d2f\u52a0\n yhat = torch.max(trainRes, 1)[1] # \u4ece trainRes \u4e00\u4e2a\u77e9\u9635\u4e2d\uff0c\u53d6\u51fa\u6bcf\u4e2a\u6837\u672c\u7684\u6700\u5927\u503c\u548c\u6700\u5927\u503c\u6240\u5728\u7684\u7d22\u5f15\uff0c\u5f97\u5230[1,2,1,4]\u8fd9\u79cd\u7c7b\u578b\u7684\u7ed3\u679c\n correct_num = torch.sum(\n yhat == target) # yhat \u3001target \u90fd\u662f\u4e00\u7ef4\u5f20\u91cf\uff0c\u4f7f\u7528 == \u4f1a\u6328\u4e2a\u5bf9\u6bd4\u5f20\u91cf\u4e2d\u7684\u5143\u7d20\u662f\u5426\u76f8\u7b49\uff0c\u6700\u7ec8\u5f97\u5230[False, True, Flase]\u8fd9\u6837\u7684\u6570\u636e\uff0c\u7136\u540e\u4f7f\u7528 torch.sum \u5c31\u53ef\u4ee5\u5f97\u5230\u4e00\u4e2a\u6570\u5b57\uff0c\u56e0\u4e3a True \u4e3a 1 \uff0cFalse \u4e3a 0 \u3002\n train_correct += correct_num # \u5c06\u51c6\u5907\u6570\u7d2f\u52a0\n # \u8ba1\u7b97\u635f\u5931\uff0c\u5e76\u7d2f\u52a0\n train_loss += loss.item() # \u8fd9\u513f\u9700\u8981\u5f97\u5230\u6240\u6709\u6837\u672c\u7684\u635f\u5931\u7684\u548c\n trainedSampleNum += data.shape[0]\n # print(\"\u672c\u6279\u6b21\u8bad\u7ec3\u635f\u5931\u4e3a\uff1a\", loss.item() / data.shape[0])\n if (batch_index + 1) % 125 == 0:\n # \u73b0\u5728\u8fdb\u884c\u5230\u4e86\u54ea\u4e2a epoch \u3001\u603b\u5171\u8981\u8bad\u7ec3\u591a\u5c11\u4e2a\u6837\u672c\u3001\u5df2\u7ecf\u8bad\u7ec3\u4e86\u591a\u5c11\u4e2a\u6837\u672c\u3001\u5df2\u8bad\u7ec3\u7684\u6837\u672c\u7684\u767e\u5206\u6bd4\n print(\"Epoch{}:{} / {} = ({:.0f}%)\".format(\n epoch + 1,\n trainedSampleNum,\n epochs * len(trainData) * batchSize,\n 100 * trainedSampleNum / (epochs * len(trainData) * batchSize)\n ))\n\n print(\"-------------------------------\")\n avg_correct = (float(train_correct) / trainTotalNum) * 100\n # print(\"\u672c\u8f6e\u8bad\u7ec3\u5e73\u5747\u51c6\u786e\u7387\uff1a\", avg_correct)\n trainCorrect.append(avg_correct)\n avg_loss = (float(train_loss) / trainTotalNum) * 100\n # print(\"\u672c\u8f6e\u8bad\u7ec3\u5e73\u5747\u635f\u5931\u7387\uff1a\", avg_loss)\n transLost.append(avg_loss)\n\n del data, target, train_loss, train_correct\n gc.collect()\n torch.cuda.empty_cache()\n\n # \u4e00\u8f6e\u8bad\u7ec3\u7ed3\u675f\uff0c\u5c31\u4f7f\u7528\u6d4b\u8bd5\u96c6\u8fdb\u884c\u6d4b\u8bd5\n net.eval()\n test_loss = 0\n test_correct = 0\n for batch_index, (test_data, test_target) in enumerate(testData):\n with torch.no_grad():\n test_data = test_data.to(device, non_blocking=True)\n test_target = test_target.to(device, non_blocking=True).view(test_data.shape[0]) # \u786e\u4fdd\u6807\u7b7e\u662f 1 \u7ef4\u7684\u7ed3\u6784\n testRes = net(test_data)\n loss = lossFunc(testRes, test_target)\n # \u8ba1\u7b97\u635f\u5931\uff0c\u5e76\u7d2f\u52a0\n test_loss += loss.item()\n # \u8ba1\u7b97\u51c6\u5907\u6570\uff0c\u5e76\u7d2f\u52a0\n yhat = torch.max(testRes, 1)[1] # \u4ece trainRes \u4e00\u4e2a\u77e9\u9635\u4e2d\uff0c\u53d6\u51fa\u6bcf\u4e2a\u6837\u672c\u7684\u6700\u5927\u503c\u548c\u6700\u5927\u503c\u6240\u5728\u7684\u7d22\u5f15\uff0c\u5f97\u5230[1,2,1,4]\u8fd9\u79cd\u7c7b\u578b\u7684\u7ed3\u679c\n correct_num = torch.sum(\n yhat == test_target) # yhat \u3001target \u90fd\u662f\u4e00\u7ef4\u5f20\u91cf\uff0c\u4f7f\u7528 == \u4f1a\u6328\u4e2a\u5bf9\u6bd4\u5f20\u91cf\u4e2d\u7684\u5143\u7d20\u662f\u5426\u76f8\u7b49\uff0c\u6700\u7ec8\u5f97\u5230[False, True, Flase]\u8fd9\u6837\u7684\u6570\u636e\uff0c\u7136\u540e\u4f7f\u7528 torch.sum \u5c31\u53ef\u4ee5\u5f97\u5230\u4e00\u4e2a\u6570\u5b57\uff0c\u56e0\u4e3a True \u4e3a 1 \uff0cFalse \u4e3a 0 \u3002\n test_correct += correct_num # \u5c06\u51c6\u5907\u6570\u7d2f\u52a0\n\n avg_test_correct = (float(test_correct) / testTotalNum) * 100\n # print(\"\u672c\u8f6e\u6d4b\u8bd5\u5e73\u5747\u51c6\u786e\u7387\uff1a\", avg_test_correct)\n testCorrect.append(avg_test_correct)\n avg_test_loss = (float(test_loss) / testTotalNum) * 100\n # print(\"\u672c\u8f6e\u6d4b\u8bd5\u5e73\u5747\u635f\u5931\u7387\uff1a\", avg_test_loss)\n testLost.append(avg_test_loss)\n\n print(\"\u672c\u8f6e\u8bad\u7ec3\u5e73\u5747\u51c6\u786e\u7387\uff1a{}, \u672c\u8f6e\u8bad\u7ec3\u5e73\u5747\u635f\u5931\u7387: {}, \u672c\u8f6e\u6d4b\u8bd5\u5e73\u5747\u51c6\u786e\u7387\uff1a{}, \u672c\u8f6e\u6d4b\u8bd5\u5e73\u5747\u635f\u5931\u7387\uff1a{}\".format(\n avg_correct, avg_loss, avg_test_correct, avg_test_loss))\n\n del test_data, test_target, test_loss, test_correct\n gc.collect()\n torch.cuda.empty_cache()\n\n # \u5982\u679c\u6d4b\u8bd5\u96c6\u635f\u5931\u51fa\u73b0\u65b0\u4f4e\u6216\u8005\u51c6\u786e\u7387\u51fa\u73b0\u65b0\u9ad8\uff0c\u5c31\u4fdd\u5b58\u5728\u6a21\u578b\u7684\u6743\u91cd\uff0c\u9632\u6b62\u4e2d\u9014\u65ad\u7535\u7b49\u539f\u56e0\u9700\u8981\u4ece\u5934\u518d\u6765\n if test_highest_correct is None:\n test_highest_correct = avg_test_correct\n if test_highest_correct < avg_test_correct:\n test_highest_correct = avg_test_correct\n torch.save(net.state_dict(), './v6/model-' + str(epoch + 1) + '.pth')\n print(\"model saved\")\n\n # \u6700\u597d\u5728\u6d4b\u8bd5\u96c6\u4e0a\u4f7f\u7528\u63d0\u524d\u505c\u6b62\uff0c\u5982\u679c\u4f7f\u7528\u8bad\u7ec3\u96c6\u65e0\u6cd5\u9884\u6d4b\u8fc7\u62df\u5408\u8fd9\u79cd\u60c5\u51b5\n early_stop = early_stopping(avg_test_loss) # \u8fd9\u513f\u4f7f\u7528\u63d0\u524d\u505c\u6b62\uff01\n if early_stop:\n break\n print(\"mission completed\")\n return transLost, trainCorrect, testLost, testCorrect\n\n\nmodel = MyResNet().to(device)\n# model.load_state_dict(torch.load(\"./v4/model-49.pth\"))\nloss_func = nn.CrossEntropyLoss(reduction='sum') # \u56e0\u4e3a\u6211\u4eec\u5728\u8bad\u7ec3\u51fd\u6570\u4e2d\uff0c\u5728\u8ba1\u7b97\u635f\u5931\u7684\u65f6\u5019\u662f\u8ba1\u7b97\u7684\u6bcf\u4e2a\u6837\u672c\u7684\u635f\u5931\u7684\u548c\uff0c\u6240\u4ee5\u8fd9\u513f\u9700\u8981\u4f7f\u7528 reduction='sum'\nopt = optim.RMSprop(model.parameters(), lr=lr, weight_decay=0.00005, momentum=0.0001)\n\ntrain_data = DataLoader(svhn_train, batch_size=batchSize, shuffle=True, drop_last=False, pin_memory=True)\ntest_data = DataLoader(svhn_test, batch_size=batchSize, shuffle=False, drop_last=False, pin_memory=True)\n\n# \u5f00\u59cb\u8bad\u7ec3\ntransLost, trainCorrect, testLost, testCorrect = fit(model, loss_func, opt, train_data, test_data, epochs)\n\n# \u8bad\u7ec3\u7ed3\u679c\u53ef\u89c6\u5316\nplt.plot(transLost, label='train loss')\nplt.plot(testLost, label='test loss')\nplt.plot(trainCorrect, label='train correcct')\nplt.plot(testCorrect, label='test correcct')\nplt.xlabel('Epoch')\nplt.ylabel('CrossEntropy Loss')\nplt.title('Training Loss')\nplt.legend()\nplt.grid(True)\nplt.show()

\u6a21\u578b\u8f93\u51fa audio \u548c transcript\uff1a

audio_string = \"\"\nfor chunk in completion:\n if chunk.choices:\n if hasattr(chunk.choices[0].delta, \"audio\"):\n try:\n audio_string += chunk.choices[0].delta.audio[\"data\"]\n except Exception as e:\n print(chunk.choices[0].delta.audio[\"transcript\"])\n else:\n print(chunk.usage)\n\nwav_bytes = base64.b64decode(audio_string)\naudio_np = np.frombuffer(wav_bytes, dtype=np.int16)\nsf.write(\"audio_assistant_py.wav\", audio_np, samplerate=24000)\n

\u7b2c 7 \u30018 \u884c\uff1a\u4e3a\u4ec0\u4e48\u628a print(chunk.choices[0].delta.audio[\"transcript\"]) \u653e\u5728 Exception \u540e\u9762\u8f93\u51fa\uff1f

\u76ee\u524d\u7684\u9879\u76ee\u662f\u7b80\u9053\u4e91\uff0c\u4f1a\u6709\u6d89\u53ca\u5230\u7b80\u9053\u4e91\u548c\u5c0f\u7a0b\u5e8f\uff0c\u4ee5\u53ca\u4e00\u4e9b\u7b2c\u4e09\u65b9 API \u63a5\u53e3\uff0c\u63d2\u4ef6\u5bf9\u63a5\u7684\u9700\u6c42\uff0c\u8bf7\u95ee\u6709\u4ec0\u4e48\u53ef\u4ee5\u5feb\u901f\u5b66\u4e60\u8fd9\u65b9\u9762\u7684\u4e66\u7c4d\u548c\u6e20\u9053\u5417\uff1f

\u4f1a\u9891\u7e41\u7684\u83b7\u53d6\u6570\u636e\uff0c\u63a8\u9001\u6570\u636e\uff0c\u5bf9\u53cc\u65b9\u7684\u6570\u636e\u8fdb\u884c\u589e\u5220\u6539\u3002

\u6025\uff01\uff01\uff01\u5728\u7ebf\u7b49\uff0c\uff0c\uff0c\u4e0d\u751a\u611f\u8c22\u3002

\u80cc\u666f

\u672c\u4eba 6-7 \u5e74\u4ea7\u54c1\uff0c\u4eca\u5e74\u4e4b\u524d\u5b8c\u5168\u6ca1\u6709\u4efb\u4f55\u7f16\u7a0b\u7684\u7ecf\u9a8c\uff0c\u5927\u5b66\u5b66\u7684 c \u7b97\u5168\u8fd8\u7ed9\u8001\u5e08\u4e86\uff0c\u4e0d\u8fc7\u65e9\u671f\u5de5\u4f5c\u6709\u63a5\u89e6\u8fc7\u4e00\u4e9b esxi \uff0cnginx \u3001mysql \u5b89\u88c5\u914d\u7f6e\u8fd9\u4e9b\u6d3b\u5427..\n\u7136\u540e\u4eca\u5e74\u5165\u804c\u7684\u65b0\u516c\u53f8\u786e\u5b9e\u5f88\u95f2\uff0c\u5c31\u7528\u4e0a\u73ed\u6478\u9c7c\u7684\u65f6\u95f4\uff0c\u7528 gemini \u64b8\u4e86\u4e00\u5957\u81ea\u7528\u7684\u5e01\u4ef7\u76d1\u63a7\u7684\u7cfb\u7edf.

\u5927\u6982\u662f\u8fd9\u6837\u7684\uff1a

1.\u524d\u7aef\u5b8c\u5168 gemini \u751f\u6210\u7684\uff0c\u6211\u5c31\u628a\u6211\u7684\u9700\u6c42\u63d0\u7ed9\u4ed6..\u6309\u94ae\u4f4d\u7f6e\uff0c\u8272\u8c03\u4ec0\u4e48\u7684\uff0c\u590d\u5236\u9ecf\u8d34\uff0c0 \u7f16\u8f91\uff1b

2.mysql \u7684\u5e93\u8868\u90fd\u662f gemini \u7ed9\u7684\u547d\u4ee4\uff0c\u590d\u5236\u9ecf\u8d34\u6267\u884c\uff1b

3.\u540e\u7aef\u7528 python \uff0c\u5404\u79cd\u5305\u63a5\u53e3\u4ec0\u4e48\u7684\u6709\u62a5\u9519\u590d\u5236\u9ecf\u8d34\u8fdb\u53bb\uff0cgemini \u7ed9\u7684\u4fee\u6539\u65b9\u6848\uff0c\u4e5f\u8dd1\u8d77\u6765\u4e86\uff0c\u8fd9\u4e2a\u8c03\u7684\u4e45\u4e00\u70b9\uff0c\u5927\u6982\u7528\u4e86 3-4 \u4e2a\u5c0f\u65f6\u5427\uff0c\u4e3b\u8981\u6709\u4e00\u4e9b\u548c nginx \u914d\u7f6e\u7684\u5947\u5947\u602a\u602a\u7684\u95ee\u9898\uff1b

4.\u76d1\u63a7\u7684 python \u901a\u8fc7 Ricky \u7684\u5b9a\u65f6\u4efb\u52a1\u6267\u884c\uff0c\u6bcf\u5206\u949f\u6267\u884c\u4e00\u6b21\uff0c\u4ece\u5e93\u91cc\u53d6\u914d\u7f6e\u7684\u5e01\u79cd\u4ef7\u683c\uff0c\u7136\u540e\u901a\u8fc7 cmp \u548c gecko \uff08\u5907\u7528\uff09\u7684\u63a5\u53e3\u62ff\u7684\u5b9e\u65f6\u6570\u636e\u505a\u6bd4\u5bf9\uff0c\u6ee1\u8db3\u62a5\u8b66\u6761\u4ef6\u5c31\u63a8\u65b9\u7cd6\u5927\u4f6c\u5fae\u4fe1\u516c\u4f17\u53f7\u548c tg \u7684 bot \u7ed9\u6211\u81ea\u5df1\u3002\u6709\u4e00\u4e9b\u7b80\u5355\u7684\u903b\u8f91\uff0c\u6bd4\u5982 24 \u5c0f\u65f6\u5185\u53ea\u62a5\u8b66\u4e00\u6b21\uff0c\u4ef7\u683c\u8dcc\u843d 5%\u91cd\u7f6e\u9884\u8b66\u72b6\u6001\u7b49\u7b49..\u8fd9\u4e2a\u7b2c\u4e00\u4e2a\u7248\u672c\u5f88\u5feb\uff0c\u5c31\u662f\u540e\u9762\u6362\u6570\u636e\u6e90\uff0c\u8c03\u6574\u9884\u8b66\u903b\u8f91\u6539\u4e86\u597d\u51e0\u8f6e\uff0c\u4f46\u662f\u6bcf\u6b21\u90fd\u5f88\u8fc5\u901f\uff0c2-3 \u4e2a\u5bf9\u8bdd\u5c31\u80fd\u89e3\u51b3\u5427\u3002

5.nginx \u7684\u914d\u7f6e\uff0c\u514d\u8d39\u7684 ssl \u8bc1\u4e66\u7533\u8bf7\u4ec0\u4e48\u7684\u901a\u8fc7 gemini \u7ed9\u7684\u547d\u4ee4\u548c\u914d\u7f6e\u5904\u7406\u7684\uff0c\u524d\u540e\u5927\u6982\u534a\u4e2a\u5c0f\u65f6\u5427.

\u7136\u540e\u8fd9\u4e2a\u81ea\u7528\u7684\u4ef7\u683c\u9884\u8b66\u76d1\u63a7\u5c31\u8dd1\u8d77\u6765\u4e86..\u524d\u540e\u7b97\u8d77\u6765\u5927\u6982\u5341\u51e0\u4e2a\u5c0f\u65f6\u5427\uff0c\u4e0a\u73ed\u7684\u65f6\u5019\u4e00\u8fb9\u6478\u9c7c\u4e00\u8fb9\u5f04\uff0c\u5927\u6982 3.4 \u5929\u5f04\u5b8c\u7684\u3002

\u73b0\u5728\u7ed9\u6211\u7684\u611f\u89c9\u5c31\u662f AI \u73b0\u5728\u771f\u7684\u53d1\u5c55\u592a\u5feb\u4e86\u2026\u6211\u611f\u89c9\u4ee5\u540e\u662f\u4e0d\u662f\u6ca1\u6709\u521d\u7ea7\u7a0b\u5e8f\u5458\u7684\u7a7a\u95f4\u4e86\uff1f\u4e00\u4e2a\u4e2d\u7ea7\u4ee5\u4e0a\u7684\u7a0b\u5e8f\u5458\uff0c\u901a\u8fc7\u62c6\u5206\u548c\u63cf\u8ff0\u9700\u6c42\uff0c\u5c31\u80fd\u628a\u4e00\u4e9b\u539f\u6765\u4e8b\u52a1\u6027\u7684\u5206\u914d\u7ed9\u521d\u7ea7\u7a0b\u5e8f\u5458\u7684\u5de5\u4f5c\u7ed9\u53d6\u4ee3\u4e86\uff1f\u751a\u81f3\u4ee3\u7801\u66f4\u89c4\u8303\uff0c\u6548\u7387\u66f4\u9ad8..

\u7f51\u7ad9\u94fe\u63a5\u5c31\u4e0d\u653e\u4e86\uff0c\u6ca1\u6709\u505a\u767b\u5f55\u6821\u9a8c\u5565\u7684\u3002

python async \u542f\u52a8\u534f\u7a0b\u65f6\uff0c\u5982\u679c\u5f53\u524d\u534f\u7a0b\u4e0d\u4e3b\u52a8 await \u91ca\u653e\uff0c\u90a3\u5c31\u4f1a\u4e00\u76f4\u5360\u7528\u4e8b\u4ef6\u5faa\u73af\uff0c\u5176\u4ed6\u534f\u7a0b\u65e0\u6cd5\u6267\u884c\u3002

\u80fd\u60f3\u5230\u7684\u4e00\u4e9b\u975e\u5e38\u6709\u98ce\u9669\u7684\u70b9\uff1a

\n
1. \u7a0b\u5e8f\u5458\u5fd8\u4e86\u5199 await \uff0c\u5355\u7eaf\u9760 cr \u5f88\u96be\u5b8c\u5168\u53d1\u73b0\uff0c\u5bfc\u81f4\u963b\u585e
\n
2. python \u7b2c\u4e09\u65b9\u5e93\u6ca1\u6709\u505a async \u6539\u9020\uff0c\u4e0d\u5c0f\u5fc3\u7528\u4e86\uff0c\u5bfc\u81f4\u963b\u585e
\n
3. \u6709\u53ef\u80fd\u672c\u5730\u8fd0\u884c\u65f6\u95f4\u77ed\uff0c\u5230\u4e86\u7ebf\u4e0a\u6570\u636e\u91cf\u5927\u4e86\uff0c\u963b\u585e\u65f6\u95f4\u53d8\u957f
\n

\u95ee\u4e86 ds \u548c chatgpt \uff0c \u770b\u8d77\u6765\u90fd\u6ca1\u6709\u5f88\u597d\u7684\u89e3\u51b3\u65b9\u6848

\u4e4b\u524d\u5199 go \uff0cgo \u8c03\u7528\u51fd\u6570\u7684\u65f6\u5019\uff0c\u6709\u95ee\u9898\u5c31\u662f\u901a\u8fc7 \u8fd4\u56de\u503c \u6709\u6ca1\u6709 err \u6765\u5224\u65ad\u3002\u5fc3\u667a\u8d1f\u62c5\u5f88\u5c0f\uff0c\u76f4\u63a5\u64b8\u5c31\u884c\u3002

\u4e00\u4e2a\u7b80\u5355\u7684\u6848\u4f8b\uff0c\u6253\u5f00\u6587\u4ef6\uff0c\u7136\u540e json \u89e3\u6790\u3002\ngo \u7248\u672c\u662f

func TestA(t *testing.T) {\n\tfd, err := os.OpenFile(\"a.txt\", os.O_RDWR, 0666)\n\tif err != nil {\n\t\tfmt.Println(\"\u6253\u5f00\u6587\u4ef6\u5931\u8d25\", err)\n\t}\n\tdefer fd.Close()\n\n\tvar data map[string]string\n\terr = json.NewDecoder(fd).Decode(&data)\n\tif err != nil {\n\t\tfmt.Println(\"\u89e3\u6790\u6587\u4ef6\u5931\u8d25\", err)\n\t}\n\tfmt.Println(data)\n}\n

\u4f46\u662f\u5230 python \u8fd9\u8fb9

f = open(\"a.json\")\ndata = json.load(f)\nf.close()\n

\u4f46\u662f\u5427

\n
1. \u5982\u679c\u6587\u4ef6\u4e0d\u5b58\u5728\u9700\u8981\u5904\u7406
\n
2. \u6587\u4ef6\u5b58\u5728\uff0copen() \u6267\u884c\u5f02\u5e38\u4e86\u8981\u5904\u7406
\n
3. json.load() \u4f1a\u4e0d\u4f1a\u5f02\u5e38\uff1f\u6211\u8df3\u8f6c\u6e90\u7801\u770b josn.load()\u6e90\u7801\u4e5f\u6ca1\u770b\u5230\u4ed6\u4f1a\u8fd4\u56de\u54ea\u4e9b\u5f02\u5e38\u4fe1\u606f\u3002
\n

\u6240\u4ee5\u6211\u5199\u51fa\u8fd9\u79cd\u4ee3\u7801

try:\n\twith open(\"a.json\", \"r\") as f:\n\t\tdata = json.load(f)\nexcept Exception as e:\n\tprint(e)\n

\u4f46\u662f\u8fd9\u6837\u628a\u591a\u79cd\u5f02\u5e38\u5408\u5230\u4e00\u4e2a Exception \u4e86\uff0c\u5982\u679c\u51fa\u73b0\u5f02\u5e38\uff0c\u4f60\u4e0d\u77e5\u9053\u662f\u54ea\u4e2a\u51fd\u6570\u8c03\u7528\u51fa\u73b0\u7684\u5f02\u5e38\u3002\u6240\u4ee5\u5c31\u9700\u8981\u8fd9\u6837\u3002

try:\n\twith open(\"a.json\", \"r\") as f:\n \ttry:\n\t\t\tdata = json.load(f)\n \texcept Exception as e:\n\t\t\tprint(e) \nexcept Exception as e:\n\tprint(e)\n

\u7136\u540e\u6211\u53d1\u73b0\uff0c\u6700\u5927\u7684\u95ee\u9898\u5c31\u662f\uff0c\u6211\u6bcf\u6b21\u8c03\u7528\u4e00\u4e2a\u5916\u90e8\u51fd\u6570\u7684\u65f6\u5019\uff0cTMD \u6839\u672c\u4e0d\u77e5\u9053\u8fd9\u4e2a\u51fd\u6570\u4f1a\u4e0d\u4f1a\u5f02\u5e38\uff0c\u603b\u4e0d\u80fd\u6bcf\u4e2a\u51fd\u6570\u8c03\u7528\u90fd\u7528 try/except \u5904\u7406\u4e00\u4e0b\uff1f

try:\n\tf1()\nexcept Exception as e:\n\tprint(e)\n \ntry:\n\tf2()\nexcept Exception as e:\n\tprint(e)\n \ntry:\n\tf3()\nexcept Exception as e:\n\tprint(e)\n

\u5199 python \u7ed9\u6211\u7684\u611f\u53d7\u5c31\u662f\uff0c\u60f3\u5199\u597d\u4e00\u4e2a\u5065\u58ee\u7684\u7a0b\u5e8f\uff0c\u6709\u5f88\u5927\u7684\u5fc3\u667a\u8d1f\u62c5\uff0c\u6211\u603b\u662f\u8981\u8003\u8651\u6211\u8c03\u7528\u7684\u51fd\u6570\u4f1a\u4e0d\u4f1a\u6709\u5f02\u5e38\u3002

\u5927\u5bb6\u597d\uff0c

\u6700\u8fd1\u6211\u6298\u817e\u4e86\u4e00\u4e2a\u5c0f\u5e94\u7528\uff0c\u60f3\u548c\u5927\u5bb6\u5206\u4eab\uff0c\u540c\u65f6\u4e5f\u6b22\u8fce\u5bf9 WebAssembly WASM \u6216\u524d\u7aef\u8fd0\u884c Python \u611f\u5174\u8da3\u7684\u670b\u53cb\u4e00\u8d77\u4ea4\u6d41\u3002

\u9879\u76ee\u5730\u5740\uff1a https://github.com/huangsen365/python-wasm-demo

\u9879\u76ee\u80cc\u666f

\u5728 AI \u5927\u6f6e\u4e0b\uff0c\u8d8a\u6765\u8d8a\u591a\u4eba\u591a\u5c11\u90fd\u4f1a\u63a5\u89e6\u5230\u4e00\u70b9\u7f16\u7a0b\uff0cPython \u53c8\u662f\u6700\u5bb9\u6613\u5165\u95e8\u7684\u8bed\u8a00\u4e4b\u4e00\u3002\u4f46\u5f88\u591a\u521d\u5b66\u8005\u5728\u5b89\u88c5\u73af\u5883\u8fd9\u4e00\u6b65\u5c31\u5bb9\u6613\u88ab\u529d\u9000\u3002\u4e8e\u662f\u6211\u60f3\u5230\u628a Python \u8fd0\u884c\u65f6\u642c\u5230\u6d4f\u89c8\u5668\u91cc\uff0c\u7701\u53bb\u5b89\u88c5\u8fc7\u7a0b\uff0c\u5728\u4efb\u4f55\u5730\u65b9\u90fd\u80fd\u968f\u65f6\u7ec3\u624b\u3002

\u9879\u76ee\u57fa\u4e8e Pyodide \uff0c\u5c06 Python \u7f16\u8bd1\u6210 WebAssembly \uff0c\u5728\u6d4f\u89c8\u5668\u7aef\u6267\u884c\u3002\u6574\u4e2a\u5e94\u7528\u662f\u7eaf\u9759\u6001\u9875\u9762\uff0c\u4e0d\u9700\u8981\u4efb\u4f55\u540e\u7aef\u670d\u52a1\u5668\u3002\u53ea\u8981\u6253\u5f00\u7f51\u9875\uff0c\u5c31\u53ef\u4ee5\u9a6c\u4e0a\u5199 Python \u4ee3\u7801\u5e76\u8fd0\u884c\u3002

\u4f53\u9a8c\u5730\u5740

\n
• \u5728\u7ebf IDE \uff08\u5b9e\u65f6\u7f16\u8f91 + \u8fd0\u884c\uff09\uff1a https://python.yunbiyun.com/editor.html
\n
• \u6a21\u62df Shell \uff08\u4ea4\u4e92\u5f0f REPL \uff09\uff1a https://python.yunbiyun.com
\n

\u7279\u6027

\n
• \u5b8c\u5168\u524d\u7aef\u8fd0\u884c\uff1a \u6240\u6709\u4ee3\u7801\u6267\u884c\u90fd\u5728\u6d4f\u89c8\u5668\u4e2d\u5b8c\u6210\uff0c\u65e0\u9700\u90e8\u7f72\u540e\u7aef\u3002
\n
• \u57fa\u672c\u7c7b VSCode \u7684\u7f16\u8f91\u4f53\u9a8c\uff1a \u96c6\u6210 Monaco Editor \uff0c\u652f\u6301\u591a\u6587\u4ef6\u7ba1\u7406\u548c\u7b80\u5355\u7684\u6587\u4ef6\u7cfb\u7edf\uff08\u9875\u9762\u5237\u65b0\u524d\u4fdd\u5b58\u5728\u5185\u5b58\u4e2d\uff09\u3002
\n
• \u793a\u4f8b\u6559\u7a0b\uff1a \u5185\u7f6e\u82e5\u5e72 Python \u793a\u4f8b\u6587\u4ef6\uff0c\u9002\u5408\u65b0\u624b\u8ddf\u7740\u7ec3\u4e60\u3002
\n
• \u53ef\u81ea\u5b9a\u4e49\u6269\u5c55\uff1a \u9879\u76ee\u6e90\u4ee3\u7801\u5f00\u653e\uff0c\u6b22\u8fce\u5927\u5bb6 Star \u3001Fork \u3001\u63d0 Issue \u6216 PR \u3002
\n

\u4f7f\u7528\u65b9\u5f0f

\n
1. \u6253\u5f00\u5728\u7ebf IDE \uff0c\u53f3\u4fa7\u4ee3\u7801\u533a\u5c31\u662f\u7f16\u8f91\u5668\u3002\u53ef\u4ee5\u65b0\u5efa\u6587\u4ef6\u6216\u6253\u5f00\u5df2\u6709\u793a\u4f8b\u6587\u4ef6\u3002
\n
2. \u70b9\u51fb \"Run\" \u6216\u5728\u7ec8\u7aef\u5185\u8f93\u5165 python \u6587\u4ef6\u540d.py \u76f4\u63a5\u8fd0\u884c\uff0c\u8f93\u51fa\u4f1a\u663e\u793a\u5728\u4e0b\u65b9\u7ec8\u7aef\u533a\u57df\u3002
\n
3. \u7eaf\u9759\u6001\u90e8\u7f72\uff0c\u81ea\u5df1\u4e0b\u8f7d\u540e\u76f4\u63a5\u7528\u4efb\u4f55\u9759\u6001\u670d\u52a1\u5668\uff08\u751a\u81f3 file://\uff09\u90fd\u80fd\u8bbf\u95ee\u3002\u4e5f\u53ef\u4ee5\u57fa\u4e8e Docker \u955c\u50cf\u90e8\u7f72\uff0c\u5728 README \u4e2d\u63d0\u4f9b\u4e86\u793a\u4f8b\u3002
\n

\u672a\u6765\u8ba1\u5212

\n
• \u589e\u5f3a\u6587\u4ef6\u6301\u4e45\u5316\u80fd\u529b\uff0c\u8ba9\u7528\u6237\u53ef\u4ee5\u9009\u62e9\u5b58\u5230\u6d4f\u89c8\u5668\u7684 IndexedDB \u6216\u4e0b\u8f7d\u5230\u672c\u5730\u3002
\n
• \u652f\u6301\u66f4\u591a Python \u5e93\u7684\u52a0\u8f7d\uff0c\u6216\u63d0\u4f9b\u81ea\u5b9a\u4e49\u79bb\u7ebf\u5305\u3002
\n
• \u671f\u5f85\u6709\u5174\u8da3\u7684\u5c0f\u4f19\u4f34\u4e00\u8d77\u8ba8\u8bba\u3001\u5b8c\u5584\u3001\u5199\u6559\u7a0b\u7b49\u3002
\n

\u9879\u76ee\u76ee\u524d\u8fd8\u6bd4\u8f83\u7b80\u5355\uff0c\u4f46\u5e0c\u671b\u80fd\u8ba9\u66f4\u591a\u60f3\u5165\u95e8 Python \u6216\u5bf9 WebAssembly WASM \u611f\u5174\u8da3\u7684\u670b\u53cb\u5feb\u901f\u4f53\u9a8c\uff0c\u6b22\u8fce\u5927\u5bb6\u63d0\u51fa\u5efa\u8bae\u6216\u53c2\u4e0e\u8d21\u732e\u3002\u4e5f\u6b22\u8fce\u5728 V2EX \u6216\u4ed3\u5e93 Issues \u533a\u8ba8\u8bba\uff0c\u8ba9\u6211\u4eec\u4e00\u8d77\u63a2\u7d22\u5728\u6d4f\u89c8\u5668\u91cc\u8dd1 Python \u7684\u66f4\u591a\u53ef\u80fd\u6027\u3002

hello \uff0c \u5728\u9879\u76ee\u5185\u90e8\u524d\u540e\u7aef api \u5bf9\u63a5\u7684\u573a\u666f\u4e2d\uff0c \u5173\u4e8e\u6570\u636e\u7ec4\u88c5\u884d\u751f\u51fa\u4e86\u975e\u5e38\u591a\u7684\u65b9\u6848\u548c\u60f3\u6cd5

\u6bd4\u5982 GraphQL \u6280\u672f\u6808\uff0c \u6216\u8005\u65b0\u589e BFF \u62bd\u8c61\u5c42\u7b49\u7b49\u7b49\u7b49

\u4eca\u5929\u8f6c\u5e16\u4e00\u4e2a\u6bd4\u8f83\u4e0d\u540c\u7684\u601d\u8def\uff0c \u57fa\u4e8e pydantic \u5bf9\u8c61\u7ec4\u5408 + resolver \u83b7\u53d6\u6570\u636e + post \u4fee\u6539\u6570\u636e\u4e09\u90e8\u66f2\u7684 Resolver \u6a21\u5f0f\uff0c\u5728\u907f\u514d\u4e86\u624b\u52a8\u6570\u636e\u904d\u5386\u7684\u60c5\u51b5\u4e0b\uff0c\u5b8c\u6210\u89c6\u56fe\u6570\u636e\u7684\u6784\u5efa\u3002

\u6b22\u8fce\u5927\u5bb6\u8bc4\u8bba\u6307\u6b63\uff0c \u8c22\u8c22!!

https://github.com/allmonday/compare-graphql-vs-resolver

\u6700\u8fd1 Python \u73af\u5883\u7ba1\u7406\u4ece conda \u5207\u6362\u5230\u4e86uv\uff0c\u627e\u5230\u4e4b\u524d\u7528 cargo \u7684\u611f\u89c9\u4e86\uff0c\u771f\u723d\u3002

\u6211\u53d1\u73b0 uv \u53ef\u4ee5\u4e0b\u8f7d\u72ec\u7acb\u7684 Python \uff0c\u975e\u5e38\u611f\u5174\u8da3\uff0c\u56e0\u4e3a\u4e4b\u524d\u7ecf\u5e38\u4f7f\u7528 Embedded Python \u6253\u5305\u4e1c\u897f\u3002Embedded Python \u53ea\u652f\u6301 Windows \uff0c\u800c uv \u652f\u6301 Windows/Linux/MacOS \u3002

\u540c\u65f6\u6211\u53c8\u9047\u89c1\u4e86\u4e00\u4e2a\u5f88 amazing \u7684\u9879\u76ee\uff0c\u53ebcosmopolitan\uff0c\u5b83\u53ef\u4ee5\u5c06 C/C++\u7f16\u8bd1\u4e3a\u4e00\u79cd\u53ebAPE\u7684\u683c\u5f0f\u7684\u6587\u4ef6\uff0c\u8fd9\u79cd\u795e\u5947\u7684\u683c\u5f0f\u540c\u65f6\u6ee1\u8db3\u591a\u79cd\u6587\u4ef6\u683c\u5f0f\uff0c\u56e0\u6b64\u5b83\u53ef\u4ee5\u5728\u5927\u591a\u6570\u4e3b\u6d41\u7684\u5e73\u53f0\u4e0a\u8fd0\u884c\u3002

\u53d7\u4ee5\u4e0a\u4e24\u4e2a\u9879\u76ee\u542f\u53d1\uff0cpyfuze \u8bde\u751f\u4e86\u3002\u5b83\u53ef\u4ee5\u5c06\u4f60\u7684 Python \u9879\u76ee\u6253\u5305\uff0c\u4f7f\u5176\u53ef\u4ee5\u5728\u5927\u591a\u6570\u4e3b\u6d41\u5e73\u53f0\u4e0a\u8fd0\u884c\u3002pyfuze \u8de8\u5e73\u53f0\u7684\u903b\u8f91\u662f\u8fd9\u6837\u7684\uff1a

\n
• pyfuze APE \u4f5c\u4e3a\u542f\u52a8\u5668\uff0c\u68c0\u6d4b\u5f53\u524d\u5e73\u53f0\u662f Windows \u8fd8\u662f Unix \uff0c\u4e0b\u8f7d\u5bf9\u5e94 uv
\n
• uv \u4e0b\u8f7d\u5f53\u524d\u5e73\u53f0\u7684 Python \u4ee5\u53ca\u9879\u76ee\u4f9d\u8d56
\n
• uv run \u8fd0\u884c\u6e90\u4ee3\u7801
\n

\u9879\u76ee\u7f3a\u70b9\u662f\u9700\u8981\u7f51\u7edc\u4e0b\u8f7d\u4e1c\u897f\uff0c\u4e0d\u8fc7\u4f60\u53ef\u4ee5\u8fd0\u884c\u4e00\u4e0b\u518d\u6253\u5305\uff0c\u4f46\u8fd9\u6837\u5c31\u56fa\u5b9a\u5728\u4f60\u8fd0\u884c\u7684\u5e73\u53f0\u4e86\u3002

\u622a\u81f3\u76ee\u524d\uff0cpyfuze \u9879\u76ee\u5df2\u83b7\u5f97 25 \u9897\u661f\uff0c\u662f\u6211\u83b7\u5f97 star \u6700\u591a\u7684\u4e00\u4e2a\u9879\u76ee\u3002\u5982\u679c\u4f60\u4e5f\u559c\u6b22\u8fd9\u4e2a\u9879\u76ee\uff0c\u4e0d\u59a8\u4e5f\u70b9\u9897\u2b50:)

P.S. uv \u3001python \u3001\u4f9d\u8d56\u90fd\u662f\u4ece\u56fd\u5916\u7f51\u7edc\u73af\u5883\u4e0b\u8f7d\u7684\uff0c\u5982\u9700\u5728\u56fd\u5185\u7f51\u7edc\u73af\u5883\u4e0b\u8fd0\u884c\uff0c\u9700\u4f7f\u7528--env \u3001--uv-install-script-windows \u4ee5\u53ca--uv-install-script-unix \u8fd9\u4e09\u4e2a\u9009\u9879\uff0c\u540e\u4e24\u4e2a\u9009\u9879\u652f\u6301\u4f7f\u7528\u6587\u4ef6\u8def\u5f84\u3002

\u8bf7\u5404\u4f4d\u8001\u5e08\u6307\u6559

\u539f\u6587\uff1a https://mp.weixin.qq.com/s/v4n33U1Q2lG7zNUE9o02gg

\u6211\u7528 nuikta \u6253\u5305\u6210 exe \u5de5\u5177\uff0c\u56e0\u4e3a\u8981\u7528\u5230 comtypes \u7684\u539f\u56e0\uff0c\u52a0\u4e0a\u4e86--include-package=comtypes --include-package=comtypes.client --include-package=comtypes.gen \uff0c\u6253\u5305\u6210\u7684 exe \u5de5\u5177\u5728\u672c\u5730\u8fd0\u884c\u53ef\u4ee5\uff0c\u4f46\u662f\u5728\u4e00\u4e2a\u5168\u65b0\u673a\u5668\u4e0a\u5c31\u4f1a\u51fa\u73b0 omtypes\\gen_944DE083_8FB8_45CF_BCB7_C477ACB2F897_0_1_0.py\", line 9204, in <module>\n\\comtypes_tlib_version_checker.py\", line 18, in _check_version\nImportError: Typelib different than module \uff0c\u7136\u540e\u6211\u5728\u4ee3\u7801\u91cc\u6307\u5b9a\u4e86 comtypes.gen_dir = COMTYPES_GEN_DIR \uff0c\u8fd8\u662f\u4e0d\u884c\uff0c\u6709\u6ca1\u6709\u5927\u4f6c\u9047\u5230\u8fc7\u8fd9\u79cd\u60c5\u51b5\uff0c\u6c42\u6307\u6559

\u8fd9\u79cd\u5199\u70b9\u4ee3\u7801\u5c31\u80fd\u5b9e\u73b0\u6548\u679c\uff0c\u6bd4\u62d6\u62c9\u62fd\u81ea\u7531\u5ea6\u9ad8\uff0c\u6bd4\u4ece 0 \u5f00\u59cb\u5de5\u4f5c\u91cf\u4f4e\n\u4e2a\u4eba\u8fd8\u662f\u504f\u597d\u8fd9\u79cd\u5de5\u5177