test1

README.md

@@ -32,6 +32,8 @@
    ```bash
    # 激活虚拟环境
    source .venv/bin/activate
+   #Windows用户 
+   .venv/Scripts/activate
    
    # 或者使用 uv run 来直接在虚拟环境中运行命令
    uv run python your_script.py

pyproject.toml

@@ -13,6 +13,7 @@ dependencies = [
     "yfinance>=0.2.65",
     "ipywidgets",
     "sqlalchemy"
+    "pandas>=2.3.1",
 ]
 
 [dependency-groups]

uv.lock

@@ -1,6 +1,10 @@
 version = 1
 revision = 2
 requires-python = ">=3.11"
+resolution-markers = [
+    "python_full_version >= '3.12'",
+    "python_full_version < '3.12'",
+]
 
 [[package]]
 name = "ai-learning"
@@ -9,6 +13,7 @@ source = { virtual = "." }
 dependencies = [
     { name = "dotenv" },
     { name = "openai" },
+    { name = "pandas" },
 ]
 
 [package.dev-dependencies]
@@ -20,6 +25,7 @@ dev = [
 requires-dist = [
     { name = "dotenv", specifier = ">=0.9.9" },
     { name = "openai", specifier = ">=1.93.0" },
+    { name = "pandas", specifier = ">=2.3.1" },
 ]
 
 [package.metadata.requires-dev]
@@ -28,7 +34,7 @@ dev = [{ name = "ipykernel", specifier = ">=6.29.5" }]
 [[package]]
 name = "annotated-types"
 version = "0.7.0"
-source = { registry = "https://mirrors.aliyun.com/pypi/simple/" }
+source = { registry = "https://mirrors.aliyun.com/pypi/simple" }
 sdist = { url = "https://mirrors.aliyun.com/pypi/packages/ee/67/531ea369ba64dcff5ec9c3402f9f51bf748cec26dde048a2f973a4eea7f5/annotated_types-0.7.0.tar.gz", hash = "sha256:aff07c09a53a08bc8cfccb9c85b05f1aa9a2a6f23728d790723543408344ce89" }
 wheels = [
     { url = "https://mirrors.aliyun.com/pypi/packages/78/b6/6307fbef88d9b5ee7421e68d78a9f162e0da4900bc5f5793f6d3d0e34fb8/annotated_types-0.7.0-py3-none-any.whl", hash = "sha256:1f02e8b43a8fbbc3f3e0d4f0f4bfc8131bcb4eebe8849b8e5c773f3a1c582a53" },
@@ -37,7 +43,7 @@ wheels = [
 [[package]]
 name = "anyio"
 version = "4.9.0"
-source = { registry = "https://mirrors.aliyun.com/pypi/simple/" }
+source = { registry = "https://mirrors.aliyun.com/pypi/simple" }
 dependencies = [
     { name = "idna" },
     { name = "sniffio" },
@@ -51,7 +57,7 @@ wheels = [
 [[package]]
 name = "appnope"
 version = "0.1.4"
-source = { registry = "https://mirrors.aliyun.com/pypi/simple/" }
+source = { registry = "https://mirrors.aliyun.com/pypi/simple" }
 sdist = { url = "https://mirrors.aliyun.com/pypi/packages/35/5d/752690df9ef5b76e169e68d6a129fa6d08a7100ca7f754c89495db3c6019/appnope-0.1.4.tar.gz", hash = "sha256:1de3860566df9caf38f01f86f65e0e13e379af54f9e4bee1e66b48f2efffd1ee" }
 wheels = [
     { url = "https://mirrors.aliyun.com/pypi/packages/81/29/5ecc3a15d5a33e31b26c11426c45c501e439cb865d0bff96315d86443b78/appnope-0.1.4-py2.py3-none-any.whl", hash = "sha256:502575ee11cd7a28c0205f379b525beefebab9d161b7c964670864014ed7213c" },
@@ -60,7 +66,7 @@ wheels = [
 [[package]]
 name = "asttokens"
 version = "3.0.0"
-source = { registry = "https://mirrors.aliyun.com/pypi/simple/" }
+source = { registry = "https://mirrors.aliyun.com/pypi/simple" }
 sdist = { url = "https://mirrors.aliyun.com/pypi/packages/4a/e7/82da0a03e7ba5141f05cce0d302e6eed121ae055e0456ca228bf693984bc/asttokens-3.0.0.tar.gz", hash = "sha256:0dcd8baa8d62b0c1d118b399b2ddba3c4aff271d0d7a9e0d4c1681c79035bbc7" }
 wheels = [
     { url = "https://mirrors.aliyun.com/pypi/packages/25/8a/c46dcc25341b5bce5472c718902eb3d38600a903b14fa6aeecef3f21a46f/asttokens-3.0.0-py3-none-any.whl", hash = "sha256:e3078351a059199dd5138cb1c706e6430c05eff2ff136af5eb4790f9d28932e2" },
@@ -69,7 +75,7 @@ wheels = [
 [[package]]
 name = "certifi"
 version = "2025.6.15"
-source = { registry = "https://mirrors.aliyun.com/pypi/simple/" }
+source = { registry = "https://mirrors.aliyun.com/pypi/simple" }
 sdist = { url = "https://mirrors.aliyun.com/pypi/packages/73/f7/f14b46d4bcd21092d7d3ccef689615220d8a08fb25e564b65d20738e672e/certifi-2025.6.15.tar.gz", hash = "sha256:d747aa5a8b9bbbb1bb8c22bb13e22bd1f18e9796defa16bab421f7f7a317323b" }
 wheels = [
     { url = "https://mirrors.aliyun.com/pypi/packages/84/ae/320161bd181fc06471eed047ecce67b693fd7515b16d495d8932db763426/certifi-2025.6.15-py3-none-any.whl", hash = "sha256:2e0c7ce7cb5d8f8634ca55d2ba7e6ec2689a2fd6537d8dec1296a477a4910057" },
@@ -78,7 +84,7 @@ wheels = [
 [[package]]
 name = "cffi"
 version = "1.17.1"
-source = { registry = "https://mirrors.aliyun.com/pypi/simple/" }
+source = { registry = "https://mirrors.aliyun.com/pypi/simple" }
 dependencies = [
     { name = "pycparser" },
 ]
@@ -123,7 +129,7 @@ wheels = [
 [[package]]
 name = "colorama"
 version = "0.4.6"
-source = { registry = "https://mirrors.aliyun.com/pypi/simple/" }
+source = { registry = "https://mirrors.aliyun.com/pypi/simple" }
 sdist = { url = "https://mirrors.aliyun.com/pypi/packages/d8/53/6f443c9a4a8358a93a6792e2acffb9d9d5cb0a5cfd8802644b7b1c9a02e4/colorama-0.4.6.tar.gz", hash = "sha256:08695f5cb7ed6e0531a20572697297273c47b8cae5a63ffc6d6ed5c201be6e44" }
 wheels = [
     { url = "https://mirrors.aliyun.com/pypi/packages/d1/d6/3965ed04c63042e047cb6a3e6ed1a63a35087b6a609aa3a15ed8ac56c221/colorama-0.4.6-py2.py3-none-any.whl", hash = "sha256:4f1d9991f5acc0ca119f9d443620b77f9d6b33703e51011c16baf57afb285fc6" },
@@ -132,7 +138,7 @@ wheels = [
 [[package]]
 name = "comm"
 version = "0.2.2"
-source = { registry = "https://mirrors.aliyun.com/pypi/simple/" }
+source = { registry = "https://mirrors.aliyun.com/pypi/simple" }
 dependencies = [
     { name = "traitlets" },
 ]
@@ -144,7 +150,7 @@ wheels = [
 [[package]]
 name = "debugpy"
 version = "1.8.14"
-source = { registry = "https://mirrors.aliyun.com/pypi/simple/" }
+source = { registry = "https://mirrors.aliyun.com/pypi/simple" }
 sdist = { url = "https://mirrors.aliyun.com/pypi/packages/bd/75/087fe07d40f490a78782ff3b0a30e3968936854105487decdb33446d4b0e/debugpy-1.8.14.tar.gz", hash = "sha256:7cd287184318416850aa8b60ac90105837bb1e59531898c07569d197d2ed5322" }
 wheels = [
     { url = "https://mirrors.aliyun.com/pypi/packages/67/e8/57fe0c86915671fd6a3d2d8746e40485fd55e8d9e682388fbb3a3d42b86f/debugpy-1.8.14-cp311-cp311-macosx_14_0_universal2.whl", hash = "sha256:1b2ac8c13b2645e0b1eaf30e816404990fbdb168e193322be8f545e8c01644a9" },
@@ -165,7 +171,7 @@ wheels = [
 [[package]]
 name = "decorator"
 version = "5.2.1"
-source = { registry = "https://mirrors.aliyun.com/pypi/simple/" }
+source = { registry = "https://mirrors.aliyun.com/pypi/simple" }
 sdist = { url = "https://mirrors.aliyun.com/pypi/packages/43/fa/6d96a0978d19e17b68d634497769987b16c8f4cd0a7a05048bec693caa6b/decorator-5.2.1.tar.gz", hash = "sha256:65f266143752f734b0a7cc83c46f4618af75b8c5911b00ccb61d0ac9b6da0360" }
 wheels = [
     { url = "https://mirrors.aliyun.com/pypi/packages/4e/8c/f3147f5c4b73e7550fe5f9352eaa956ae838d5c51eb58e7a25b9f3e2643b/decorator-5.2.1-py3-none-any.whl", hash = "sha256:d316bb415a2d9e2d2b3abcc4084c6502fc09240e292cd76a76afc106a1c8e04a" },
@@ -174,7 +180,7 @@ wheels = [
 [[package]]
 name = "distro"
 version = "1.9.0"
-source = { registry = "https://mirrors.aliyun.com/pypi/simple/" }
+source = { registry = "https://mirrors.aliyun.com/pypi/simple" }
 sdist = { url = "https://mirrors.aliyun.com/pypi/packages/fc/f8/98eea607f65de6527f8a2e8885fc8015d3e6f5775df186e443e0964a11c3/distro-1.9.0.tar.gz", hash = "sha256:2fa77c6fd8940f116ee1d6b94a2f90b13b5ea8d019b98bc8bafdcabcdd9bdbed" }
 wheels = [
     { url = "https://mirrors.aliyun.com/pypi/packages/12/b3/231ffd4ab1fc9d679809f356cebee130ac7daa00d6d6f3206dd4fd137e9e/distro-1.9.0-py3-none-any.whl", hash = "sha256:7bffd925d65168f85027d8da9af6bddab658135b840670a223589bc0c8ef02b2" },
@@ -183,7 +189,7 @@ wheels = [
 [[package]]
 name = "dotenv"
 version = "0.9.9"
-source = { registry = "https://mirrors.aliyun.com/pypi/simple/" }
+source = { registry = "https://mirrors.aliyun.com/pypi/simple" }
 dependencies = [
     { name = "python-dotenv" },
 ]
@@ -194,7 +200,7 @@ wheels = [
 [[package]]
 name = "executing"
 version = "2.2.0"
-source = { registry = "https://mirrors.aliyun.com/pypi/simple/" }
+source = { registry = "https://mirrors.aliyun.com/pypi/simple" }
 sdist = { url = "https://mirrors.aliyun.com/pypi/packages/91/50/a9d80c47ff289c611ff12e63f7c5d13942c65d68125160cefd768c73e6e4/executing-2.2.0.tar.gz", hash = "sha256:5d108c028108fe2551d1a7b2e8b713341e2cb4fc0aa7dcf966fa4327a5226755" }
 wheels = [
     { url = "https://mirrors.aliyun.com/pypi/packages/7b/8f/c4d9bafc34ad7ad5d8dc16dd1347ee0e507a52c3adb6bfa8887e1c6a26ba/executing-2.2.0-py2.py3-none-any.whl", hash = "sha256:11387150cad388d62750327a53d3339fad4888b39a6fe233c3afbb54ecffd3aa" },
@@ -203,7 +209,7 @@ wheels = [
 [[package]]
 name = "h11"
 version = "0.16.0"
-source = { registry = "https://mirrors.aliyun.com/pypi/simple/" }
+source = { registry = "https://mirrors.aliyun.com/pypi/simple" }
 sdist = { url = "https://mirrors.aliyun.com/pypi/packages/01/ee/02a2c011bdab74c6fb3c75474d40b3052059d95df7e73351460c8588d963/h11-0.16.0.tar.gz", hash = "sha256:4e35b956cf45792e4caa5885e69fba00bdbc6ffafbfa020300e549b208ee5ff1" }
 wheels = [
     { url = "https://mirrors.aliyun.com/pypi/packages/04/4b/29cac41a4d98d144bf5f6d33995617b185d14b22401f75ca86f384e87ff1/h11-0.16.0-py3-none-any.whl", hash = "sha256:63cf8bbe7522de3bf65932fda1d9c2772064ffb3dae62d55932da54b31cb6c86" },
@@ -212,7 +218,7 @@ wheels = [
 [[package]]
 name = "httpcore"
 version = "1.0.9"
-source = { registry = "https://mirrors.aliyun.com/pypi/simple/" }
+source = { registry = "https://mirrors.aliyun.com/pypi/simple" }
 dependencies = [
     { name = "certifi" },
     { name = "h11" },
@@ -225,7 +231,7 @@ wheels = [
 [[package]]
 name = "httpx"
 version = "0.28.1"
-source = { registry = "https://mirrors.aliyun.com/pypi/simple/" }
+source = { registry = "https://mirrors.aliyun.com/pypi/simple" }
 dependencies = [
     { name = "anyio" },
     { name = "certifi" },
@@ -240,7 +246,7 @@ wheels = [
 [[package]]
 name = "idna"
 version = "3.10"
-source = { registry = "https://mirrors.aliyun.com/pypi/simple/" }
+source = { registry = "https://mirrors.aliyun.com/pypi/simple" }
 sdist = { url = "https://mirrors.aliyun.com/pypi/packages/f1/70/7703c29685631f5a7590aa73f1f1d3fa9a380e654b86af429e0934a32f7d/idna-3.10.tar.gz", hash = "sha256:12f65c9b470abda6dc35cf8e63cc574b1c52b11df2c86030af0ac09b01b13ea9" }
 wheels = [
     { url = "https://mirrors.aliyun.com/pypi/packages/76/c6/c88e154df9c4e1a2a66ccf0005a88dfb2650c1dffb6f5ce603dfbd452ce3/idna-3.10-py3-none-any.whl", hash = "sha256:946d195a0d259cbba61165e88e65941f16e9b36ea6ddb97f00452bae8b1287d3" },
@@ -249,7 +255,7 @@ wheels = [
 [[package]]
 name = "ipykernel"
 version = "6.29.5"
-source = { registry = "https://mirrors.aliyun.com/pypi/simple/" }
+source = { registry = "https://mirrors.aliyun.com/pypi/simple" }
 dependencies = [
     { name = "appnope", marker = "sys_platform == 'darwin'" },
     { name = "comm" },
@@ -273,7 +279,7 @@ wheels = [
 [[package]]
 name = "ipython"
 version = "9.4.0"
-source = { registry = "https://mirrors.aliyun.com/pypi/simple/" }
+source = { registry = "https://mirrors.aliyun.com/pypi/simple" }
 dependencies = [
     { name = "colorama", marker = "sys_platform == 'win32'" },
     { name = "decorator" },
@@ -295,7 +301,7 @@ wheels = [
 [[package]]
 name = "ipython-pygments-lexers"
 version = "1.1.1"
-source = { registry = "https://mirrors.aliyun.com/pypi/simple/" }
+source = { registry = "https://mirrors.aliyun.com/pypi/simple" }
 dependencies = [
     { name = "pygments" },
 ]
@@ -307,7 +313,7 @@ wheels = [
 [[package]]
 name = "jedi"
 version = "0.19.2"
-source = { registry = "https://mirrors.aliyun.com/pypi/simple/" }
+source = { registry = "https://mirrors.aliyun.com/pypi/simple" }
 dependencies = [
     { name = "parso" },
 ]
@@ -319,7 +325,7 @@ wheels = [
 [[package]]
 name = "jiter"
 version = "0.10.0"
-source = { registry = "https://mirrors.aliyun.com/pypi/simple/" }
+source = { registry = "https://mirrors.aliyun.com/pypi/simple" }
 sdist = { url = "https://mirrors.aliyun.com/pypi/packages/ee/9d/ae7ddb4b8ab3fb1b51faf4deb36cb48a4fbbd7cb36bad6a5fca4741306f7/jiter-0.10.0.tar.gz", hash = "sha256:07a7142c38aacc85194391108dc91b5b57093c978a9932bd86a36862759d9500" }
 wheels = [
     { url = "https://mirrors.aliyun.com/pypi/packages/1b/dd/6cefc6bd68b1c3c979cecfa7029ab582b57690a31cd2f346c4d0ce7951b6/jiter-0.10.0-cp311-cp311-macosx_10_12_x86_64.whl", hash = "sha256:3bebe0c558e19902c96e99217e0b8e8b17d570906e72ed8a87170bc290b1e978" },
@@ -379,7 +385,7 @@ wheels = [
 [[package]]
 name = "jupyter-client"
 version = "8.6.3"
-source = { registry = "https://mirrors.aliyun.com/pypi/simple/" }
+source = { registry = "https://mirrors.aliyun.com/pypi/simple" }
 dependencies = [
     { name = "jupyter-core" },
     { name = "python-dateutil" },
@@ -395,7 +401,7 @@ wheels = [
 [[package]]
 name = "jupyter-core"
 version = "5.8.1"
-source = { registry = "https://mirrors.aliyun.com/pypi/simple/" }
+source = { registry = "https://mirrors.aliyun.com/pypi/simple" }
 dependencies = [
     { name = "platformdirs" },
     { name = "pywin32", marker = "platform_python_implementation != 'PyPy' and sys_platform == 'win32'" },
@@ -409,7 +415,7 @@ wheels = [
 [[package]]
 name = "matplotlib-inline"
 version = "0.1.7"
-source = { registry = "https://mirrors.aliyun.com/pypi/simple/" }
+source = { registry = "https://mirrors.aliyun.com/pypi/simple" }
 dependencies = [
     { name = "traitlets" },
 ]
@@ -421,16 +427,74 @@ wheels = [
 [[package]]
 name = "nest-asyncio"
 version = "1.6.0"
-source = { registry = "https://mirrors.aliyun.com/pypi/simple/" }
+source = { registry = "https://mirrors.aliyun.com/pypi/simple" }
 sdist = { url = "https://mirrors.aliyun.com/pypi/packages/83/f8/51569ac65d696c8ecbee95938f89d4abf00f47d58d48f6fbabfe8f0baefe/nest_asyncio-1.6.0.tar.gz", hash = "sha256:6f172d5449aca15afd6c646851f4e31e02c598d553a667e38cafa997cfec55fe" }
 wheels = [
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 ]
 
 [[package]]
+name = "numpy"
+version = "2.3.1"
+source = { registry = "https://mirrors.aliyun.com/pypi/simple" }
+sdist = { url = "https://mirrors.aliyun.com/pypi/packages/2e/19/d7c972dfe90a353dbd3efbbe1d14a5951de80c99c9dc1b93cd998d51dc0f/numpy-2.3.1.tar.gz", hash = "sha256:1ec9ae20a4226da374362cca3c62cd753faf2f951440b0e3b98e93c235441d2b" }
+wheels = [
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+    { url = "https://mirrors.aliyun.com/pypi/packages/b0/25/93b621219bb6f5a2d4e713a824522c69ab1f06a57cd571cda70e2e31af44/numpy-2.3.1-cp311-cp311-macosx_14_0_x86_64.whl", hash = "sha256:e344eb79dab01f1e838ebb67aab09965fb271d6da6b00adda26328ac27d4a66e" },
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+    { url = "https://mirrors.aliyun.com/pypi/packages/75/c9/9bec03675192077467a9c7c2bdd1f2e922bd01d3a69b15c3a0fdcd8548f6/numpy-2.3.1-cp311-cp311-manylinux_2_28_x86_64.whl", hash = "sha256:afed2ce4a84f6b0fc6c1ce734ff368cbf5a5e24e8954a338f3bdffa0718adffb" },
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 sdist = { url = "https://mirrors.aliyun.com/pypi/packages/51/89/c72771c81d25d53fe33e3dca61c233b665b2780f21820ba6fd2c6793c12b/tornado-6.5.1.tar.gz", hash = "sha256:84ceece391e8eb9b2b95578db65e920d2a61070260594819589609ba9bc6308c" }
 wheels = [
     { url = "https://mirrors.aliyun.com/pypi/packages/77/89/f4532dee6843c9e0ebc4e28d4be04c67f54f60813e4bf73d595fe7567452/tornado-6.5.1-cp39-abi3-macosx_10_9_universal2.whl", hash = "sha256:d50065ba7fd11d3bd41bcad0825227cc9a95154bad83239357094c36708001f7" },
@@ -764,7 +878,7 @@ wheels = [
 [[package]]
 name = "tqdm"
 version = "4.67.1"
-source = { registry = "https://mirrors.aliyun.com/pypi/simple/" }
+source = { registry = "https://mirrors.aliyun.com/pypi/simple" }
 dependencies = [
     { name = "colorama", marker = "sys_platform == 'win32'" },
 ]
@@ -776,7 +890,7 @@ wheels = [
 [[package]]
 name = "traitlets"
 version = "5.14.3"
-source = { registry = "https://mirrors.aliyun.com/pypi/simple/" }
+source = { registry = "https://mirrors.aliyun.com/pypi/simple" }
 sdist = { url = "https://mirrors.aliyun.com/pypi/packages/eb/79/72064e6a701c2183016abbbfedaba506d81e30e232a68c9f0d6f6fcd1574/traitlets-5.14.3.tar.gz", hash = "sha256:9ed0579d3502c94b4b3732ac120375cda96f923114522847de4b3bb98b96b6b7" }
 wheels = [
     { url = "https://mirrors.aliyun.com/pypi/packages/00/c0/8f5d070730d7836adc9c9b6408dec68c6ced86b304a9b26a14df072a6e8c/traitlets-5.14.3-py3-none-any.whl", hash = "sha256:b74e89e397b1ed28cc831db7aea759ba6640cb3de13090ca145426688ff1ac4f" },
@@ -785,7 +899,7 @@ wheels = [
 [[package]]
 name = "typing-extensions"
 version = "4.14.0"
-source = { registry = "https://mirrors.aliyun.com/pypi/simple/" }
+source = { registry = "https://mirrors.aliyun.com/pypi/simple" }
 sdist = { url = "https://mirrors.aliyun.com/pypi/packages/d1/bc/51647cd02527e87d05cb083ccc402f93e441606ff1f01739a62c8ad09ba5/typing_extensions-4.14.0.tar.gz", hash = "sha256:8676b788e32f02ab42d9e7c61324048ae4c6d844a399eebace3d4979d75ceef4" }
 wheels = [
     { url = "https://mirrors.aliyun.com/pypi/packages/69/e0/552843e0d356fbb5256d21449fa957fa4eff3bbc135a74a691ee70c7c5da/typing_extensions-4.14.0-py3-none-any.whl", hash = "sha256:a1514509136dd0b477638fc68d6a91497af5076466ad0fa6c338e44e359944af" },
@@ -794,7 +908,7 @@ wheels = [
 [[package]]
 name = "typing-inspection"
 version = "0.4.1"
-source = { registry = "https://mirrors.aliyun.com/pypi/simple/" }
+source = { registry = "https://mirrors.aliyun.com/pypi/simple" }
 dependencies = [
     { name = "typing-extensions" },
 ]
@@ -804,9 +918,18 @@ wheels = [
 ]
 
 [[package]]
+name = "tzdata"
+version = "2025.2"
+source = { registry = "https://mirrors.aliyun.com/pypi/simple" }
+sdist = { url = "https://mirrors.aliyun.com/pypi/packages/95/32/1a225d6164441be760d75c2c42e2780dc0873fe382da3e98a2e1e48361e5/tzdata-2025.2.tar.gz", hash = "sha256:b60a638fcc0daffadf82fe0f57e53d06bdec2f36c4df66280ae79bce6bd6f2b9" }
+wheels = [
+    { url = "https://mirrors.aliyun.com/pypi/packages/5c/23/c7abc0ca0a1526a0774eca151daeb8de62ec457e77262b66b359c3c7679e/tzdata-2025.2-py2.py3-none-any.whl", hash = "sha256:1a403fada01ff9221ca8044d701868fa132215d84beb92242d9acd2147f667a8" },
+]
+
+[[package]]
 name = "wcwidth"
 version = "0.2.13"
-source = { registry = "https://mirrors.aliyun.com/pypi/simple/" }
+source = { registry = "https://mirrors.aliyun.com/pypi/simple" }
 sdist = { url = "https://mirrors.aliyun.com/pypi/packages/6c/63/53559446a878410fc5a5974feb13d31d78d752eb18aeba59c7fef1af7598/wcwidth-0.2.13.tar.gz", hash = "sha256:72ea0c06399eb286d978fdedb6923a9eb47e1c486ce63e9b4e64fc18303972b5" }
 wheels = [
     { url = "https://mirrors.aliyun.com/pypi/packages/fd/84/fd2ba7aafacbad3c4201d395674fc6348826569da3c0937e75505ead3528/wcwidth-0.2.13-py2.py3-none-any.whl", hash = "sha256:3da69048e4540d84af32131829ff948f1e022c1c6bdb8d6102117aac784f6859" },

曹航/caohang.ipynb

@@ -0,0 +1,164 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 简单实践"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from openai import OpenAI\n",
+    "from dotenv import load_dotenv \n",
+    "import os\n",
+    "load_dotenv()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# # 免费api的尝试\n",
+    "# chatanywhere_client = OpenAI(\n",
+    "#     base_url=os.getenv(\"CHATANYWHERE_API_BASE_URL\"),\n",
+    "#     api_key=os.getenv(\"CHATANYWHERE_API_KEY\")\n",
+    "# )\n",
+    "# completion = chatanywhere_client.chat.completions.create(\n",
+    "#     model=\"gpt-3.5-turbo\",\n",
+    "#     messages=[\n",
+    "#         {\"role\": \"user\", \"content\": \"你是谁？\"},\n",
+    "#     ],\n",
+    "# )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### openAI文档内容实践\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "我最爱的城市是杭州，  \n",
+      "湖光山色美如画。\n",
+      "我爱杭州，风景如画，  \n",
+      "西湖美景，令人心。\n",
+      "我爱杭州，风景如画，  \n",
+      "西湖美景，心神啊。\n"
+     ]
+    }
+   ],
+   "source": [
+    "# simple example\n",
+    "client = OpenAI(\n",
+    "    base_url=os.getenv(\"BAILIAN_API_BASE_URL\"),\n",
+    "    api_key=os.getenv(\"BAILIAN_API_KEY\")\n",
+    ")\n",
+    "\n",
+    "system_message = \"\"\"你是一个很有用的AI助手.\n",
+    "                使用中文来回答每次用户的问题.\n",
+    "                并且每次回答都要求押韵\"\"\"\n",
+    "\n",
+    "completion = client.chat.completions.create(\n",
+    "    # 模型列表：https://help.aliyun.com/zh/model-studio/getting-started/models\n",
+    "    model=\"qwen3-30b-a3b\",\n",
+    "    messages=[\n",
+    "        {\"role\": \"system\", \"content\": system_message},\n",
+    "        {\"role\": \"user\", \"content\": \"你最喜欢的城市是什么？请用简洁明了的语言回答我的问题。\"},\n",
+    "    ],\n",
+    "    # Qwen3模型通过enable_thinking参数控制思考过程（开源版默认True，商业版默认False）\n",
+    "    # 使用Qwen3开源版模型时，若未启用流式输出，请将下行取消注释，否则会报错\n",
+    "    extra_body={\"enable_thinking\": False},\n",
+    "\n",
+    "    # 返回可能的下一个token的log概率\n",
+    "    # logprobs=True,\n",
+    "    # top_logprobs=5\n",
+    "\n",
+    "    #控制随机和多样\n",
+    "    # temperature=0.1,\n",
+    "    # top_p=0.95,\n",
+    "\n",
+    "    # 控制token的生成概率\n",
+    "    # logit_bias={\n",
+    "    #     \"151377\": -100,\n",
+    "    #     \"151378\": -100,\n",
+    "    # }\n",
+    "\n",
+    "    #使文本生成过程更具有确定性\n",
+    "    #seed=2048,\n",
+    "\n",
+    "    #控制响应生成数量\n",
+    "    #n=3,\n",
+    "    #返回得分最高的n个响应\n",
+    "    #best_of=2,\n",
+    "\n",
+    "\n",
+    ")\n",
+    "#print(completion.choices[0].message.logprobs[\"content\"])\n",
+    "print(completion.choices[0].message.content)\n",
+    "# print(completion.choices[1].message.content)\n",
+    "# print(completion.choices[2].message.content)\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

曹航/sentiment_prediction.ipynb

@@ -0,0 +1,341 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "33d34c29",
+   "metadata": {},
+   "source": [
+    "### 评价情感判断"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ca680f71",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 导入所需库\n",
+    "from openai import OpenAI\n",
+    "from dotenv import load_dotenv \n",
+    "from IPython.display import display, HTML\n",
+    "import pandas as pd\n",
+    "import json\n",
+    "import os\n",
+    "load_dotenv()\n",
+    "# 用openAI client 调用模型\n",
+    "\n",
+    "client = OpenAI(\n",
+    "    base_url=os.getenv(\"BAILIAN_API_BASE_URL\"),\n",
+    "    api_key=os.getenv(\"BAILIAN_API_KEY\")\n",
+    ")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f9c40826",
+   "metadata": {},
+   "source": [
+    "**用于大模型的函数调用**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fe3620f8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 用于function calling的实践\n",
+    "def handle_positive_sentiment(reason, result):\n",
+    "    \"\"\"处理积极情感的方法\n",
+    "    Args:\n",
+    "        reason: 大模型给出的积极判断原因\n",
+    "        result: 大模型返回的完整结果\n",
+    "    \"\"\"\n",
+    "    print(\"这是一个积极的评价!\")\n",
+    "    print(f\"判断原因: {reason}\")\n",
+    "    print(f\"完整结果: {result}\")\n",
+    "    \n",
+    "def handle_negative_sentiment(reason, result):\n",
+    "    \"\"\"处理消极情感的方法\n",
+    "    Args:\n",
+    "        reason: 大模型给出的消极判断原因\n",
+    "        result: 大模型返回的完整结果\n",
+    "    \"\"\" \n",
+    "    print(\"这是一个消极的评价!\")\n",
+    "    print(f\"判断原因: {reason}\")\n",
+    "    print(f\"完整结果: {result}\")\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f1fb34ca",
+   "metadata": {},
+   "source": [
+    "**定义预测方法**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e874b885",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#预测方法\n",
+    "def predict_sentiment(text,model=\"qwen3-30b-a3b\"):\n",
+    "    # 定义提示词\n",
+    "    system_message = \"\"\"You are a sentiment analysis expert. Please analyze the sentiment tendency of the provided review text.\"\"\"\n",
+    "    user_message = f\"\"\"\n",
+    "        Please analyze the sentiment tendency (positive or negative) of the following review text (provided within <>), and return the result in JSON format.\n",
+    "        Review text: <{text}>\n",
+    "\n",
+    "        Please only return a JSON containing the following fields:\n",
+    "        - sentiment: The sentiment tendency (positive or negative)\n",
+    "        - reason: The reason for the sentiment tendency\n",
+    "    \"\"\"\n",
+    "\n",
+    "    # 定义工具用以大模型调用\n",
+    "    tools=[]\n",
+    "    \n",
+    "    # 调用大模型进行情感分析\n",
+    "    response = client.chat.completions.create(\n",
+    "        model=model ,  # 如果model参数为空则使用默认值\n",
+    "        messages=[\n",
+    "            # 添加系统提示词\n",
+    "            {\"role\": \"system\", \"content\": system_message},\n",
+    "            # TODO few-shot/one-shot 来增强表现的实践\n",
+    "            # {\"role\":\"user\", \"content\": \"\"\"Once again Mr. Costner has dragged out a movie for far longer than necessary. \n",
+    "            # Aside from the terrific sea rescue sequences, of which there are very few I just did not care about any of the characters. \n",
+    "            # Most of us have ghosts in the closet, and Costner's character are realized early on, and then forgotten until much later, \n",
+    "            # by which time I did not care. The character we should really care about is a very cocky, overconfident Ashton Kutcher. \n",
+    "            # The problem is he comes off as kid who thinks he's better than anyone else around him and shows no signs of a cluttered closet.\n",
+    "            #  His only obstacle appears to be winning over Costner. \n",
+    "            #  Finally when we are well past the half way point of this stinker, Costner tells us all about Kutcher's ghosts. \n",
+    "            #  We are told why Kutcher is driven to be the best with no prior inkling or foreshadowing. \n",
+    "            #  No magic here, it was all I could do to keep from turning it off an hour in.\"\"\"},\n",
+    "            # {\"role\":\"assistant\", \"content\": \"\"},\n",
+    "            {\"role\": \"user\", \"content\": user_message}\n",
+    "        ],\n",
+    "        # Qwen3模型通过enable_thinking参数控制思考过程（开源版默认True，商业版默认False）\n",
+    "        # 使用Qwen3开源版模型时，若未启用流式输出，请将下行取消注释，否则会报错\n",
+    "        extra_body={\"enable_thinking\": False},\n",
+    "\n",
+    "        #tools=tools,\n",
+    "        temperature=0.3,\n",
+    "        response_format={\"type\": \"json_object\"}  # 指定返回JSON格式\n",
+    "    )\n",
+    "    \n",
+    "    # 获取返回结果\n",
+    "    result = response.choices[0].message.content\n",
+    "    return result\n",
+    "\n",
+    "#测试\n",
+    "print(predict_sentiment(\"你好!我很感谢你\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "df14f73d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#文件读取方法\n",
+    "def load_data():\n",
+    "    # 定义数据文件夹路径\n",
+    "    data_dir = \"../data/acllmdb_sentiment_small\"\n",
+    "\n",
+    "    # 读取正面评价数据\n",
+    "    positive_dir = os.path.join(data_dir, \"positive\")\n",
+    "    positive_files = os.listdir(positive_dir)\n",
+    "    positive_texts = []\n",
+    "    for file in positive_files:\n",
+    "        with open(os.path.join(positive_dir, file), 'r', encoding='utf-8') as f:\n",
+    "            text = f.read()\n",
+    "            positive_texts.append({'text': text, 'sentiment': 'positive'})\n",
+    "\n",
+    "    # 读取负面评价数据        \n",
+    "    negative_dir = os.path.join(data_dir, \"negative\") \n",
+    "    negative_files = os.listdir(negative_dir)\n",
+    "    negative_texts = []\n",
+    "    for file in negative_files:\n",
+    "        with open(os.path.join(negative_dir, file), 'r', encoding='utf-8') as f:\n",
+    "            text = f.read()\n",
+    "            negative_texts.append({'text': text, 'sentiment': 'negative'})\n",
+    "\n",
+    "    # 合并数据并创建DataFrame\n",
+    "    df = pd.DataFrame(positive_texts + negative_texts)\n",
+    "    print(f\"总共读取了 {len(df)} 条评价数据\")\n",
+    "    print(f\"其中正面评价 {len(positive_texts)} 条，负面评价 {len(negative_texts)} 条\")\n",
+    "    return df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b6c31df9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 批量预测情感倾向\n",
+    "def predict_sentiment_batch(model):\n",
+    "    \n",
+    "    try:\n",
+    "        # 数据加载\n",
+    "        data_to_predict = load_data()\n",
+    "        if data_to_predict.empty:\n",
+    "            #加载失败\n",
+    "            print(\"数据加载为空,请检查数据文件路径是否正确\")\n",
+    "        else:\n",
+    "            #加载成功\n",
+    "            predictions = []\n",
+    "\n",
+    "            print(f\"开始进行情感预测...(model={model})\")\n",
+    "            # 遍历每条评论数据\n",
+    "            for i in range(len(data_to_predict)):\n",
+    "                row = data_to_predict.iloc[i]\n",
+    "                text = row['text']\n",
+    "                true_sentiment=row['sentiment']\n",
+    "                try:\n",
+    "                    # 调用大模型进行预测当前行\n",
+    "                    result = predict_sentiment(text,model)\n",
+    "                    #得到json格式结果\n",
+    "                    if result is not None:\n",
+    "                        result_dict = json.loads(result)\n",
+    "                        predicted_sentiment = result_dict.get('sentiment', 'unknown')\n",
+    "                        reason = result_dict.get('reason', 'unknown')\n",
+    "                    \n",
+    "                    # 保存预测结果\n",
+    "                    predictions.append({\n",
+    "                        'text': text,\n",
+    "                        'true_sentiment': true_sentiment,\n",
+    "                        'predicted_sentiment': predicted_sentiment,\n",
+    "                        'reason':reason\n",
+    "                    })\n",
+    "                    \n",
+    "                    # 打印进度 每完成三十条打印一次\n",
+    "                    if  i%30 == 0 or i == len(data_to_predict)-1 :\n",
+    "                        print(f\"已完成 {len(predictions)}/{len(data_to_predict)} 条预测\")\n",
+    "\n",
+    "                except Exception as e:\n",
+    "                    print(f\"第 {i + 1} 条数据预测失败: {str(e)}\")\n",
+    "                    continue\n",
+    "            \n",
+    "            # 将预测结果转换为DataFrame\n",
+    "            predictions_df = pd.DataFrame(predictions)\n",
+    "            print(f\"成功预测 {len(predictions_df)} 条数据\")\n",
+    "            return predictions_df\n",
+    "    except Exception as e:\n",
+    "        print(f\"数据加载出错: {str(e)}\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e56e5bd3",
+   "metadata": {},
+   "source": [
+    "**比较以下大模型在这个任务上的 accuracy 差异**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f99ac855",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 计算预测准确率\n",
+    "# 解决思路:\n",
+    "# 1. 遍历不同的模型名称列表 models\n",
+    "# 2. 对每个模型调用 predict_sentiment_batch() 获取预测结果\n",
+    "# 3. 计算每个模型的预测准确率:\n",
+    "#    - 使用 predictions_df 中的 true_sentiment 和 predicted_sentiment 列进行比较\n",
+    "#    - 使用 == 运算符比较两列的值是否相等\n",
+    "#    - 使用 mean() 计算相等的比例得到准确率\n",
+    "# 4. 将每个模型的准确率结果以百分比格式打印输出\n",
+    "# 5. 对比不同模型的准确率,分析性能差异\n",
+    "\n",
+    "# 定义要测试的模型列表\n",
+    "models = [\"qwen3-32b\", \"qwen3-30b-a3b\", \"qwen3-0.6b\"]\n",
+    "\n",
+    "# 存储每个模型的准确率结果\n",
+    "model_accuracies = {}\n",
+    "\n",
+    "# 遍历每个模型进行预测和评估\n",
+    "for model_using in models:\n",
+    "    predictions_df=predict_sentiment_batch(model_using)\n",
+    "    if predictions_df is not None and len(predictions_df) > 0:\n",
+    "        # 计算准确率\n",
+    "        accuracy = (predictions_df['true_sentiment'] == predictions_df['predicted_sentiment']).mean()\n",
+    "        model_accuracies[model_using] = accuracy\n",
+    "        \n",
+    "        # 打印当前模型的准确率\n",
+    "        print(f\"模型 {model_using} 的预测准确率: {accuracy:.2%}\")\n",
+    "        \n",
+    "        # 统计错误预测的样本\n",
+    "        wrong_predictions = predictions_df[predictions_df['true_sentiment'] != predictions_df['predicted_sentiment']]\n",
+    "        print(f\"错误预测数量: {len(wrong_predictions)}/{len(predictions_df)}\")\n",
+    "        \n",
+    "        # # 显示一些错误预测的例子\n",
+    "        # if len(wrong_predictions) > 0:\n",
+    "        #     print(\"\\n错误预测示例:\")\n",
+    "        #     for _, row in wrong_predictions.head(3).iterrows():\n",
+    "        #         print(f\"文本: {row['text']}\")\n",
+    "        #         print(f\"真实情感: {row['true_sentiment']}\")\n",
+    "        #         print(f\"预测情感: {row['predicted_sentiment']}\")\n",
+    "        #         print(f\"预测理由: {row['reason']}\\n\")\n",
+    "    else:\n",
+    "        print(f\"模型 {model_using} 预测结果为空\")\n",
+    "\n",
+    "# 比较不同模型的性能\n",
+    "if model_accuracies:\n",
+    "    print(\"\\n模型性能对比:\")\n",
+    "    best_model = max(model_accuracies.items(), key=lambda x: x[1])\n",
+    "    print(f\"最佳模型: {best_model[0]}, 准确率: {best_model[1]:.2%}\")\n",
+    "    \n",
+    "# import matplotlib.pyplot as plt\n",
+    "#     # 绘制准确率对比图\n",
+    "#     plt.figure(figsize=(10, 6))\n",
+    "#     plt.bar(model_accuracies.keys(), [acc * 100 for acc in model_accuracies.values()])\n",
+    "#     plt.title('不同模型的准确率对比')\n",
+    "#     plt.xlabel('模型')\n",
+    "#     plt.ylabel('准确率 (%)')\n",
+    "#     plt.xticks(rotation=45)\n",
+    "#     plt.tight_layout()\n",
+    "#     plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "47872e36",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}