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fix: delete .DS_Store
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.github/workflows/ci-test.yml

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name: Auto CI and Build Tools
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name: Auto CI and Build Test
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pull_request:
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- main
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jobs:
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update-version-and-publish:
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auto-ci-build-test:
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runs-on: ubuntu-latest
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.github/workflows/release.yml

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push:
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branches:
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- main
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paths:
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- "parse_llm_code/**"
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- ".github/workflows/**"
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- "tests/**"
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- "setup.py"
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update-version-and-publish:

.gitignore

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# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
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# and can be added to the global gitignore or merged into this file. For a more nuclear
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# option (not recommended) you can uncomment the following to ignore the entire idea folder.
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#.idea/
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.idea/
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# macos
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.DS_Store

README.md

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# parse_llm_code
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[![Auto CI and Build Tools](https://github.com/aboutmydreams/parse_llm_code/actions/workflows/ci-test.yml/badge.svg)](https://github.com/aboutmydreams/parse_llm_code/actions/workflows/ci-test.yml)
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[![Auto Publish to PyPI and GitHub Release](https://github.com/aboutmydreams/parse_llm_code/actions/workflows/release.yml/badge.svg)](https://github.com/aboutmydreams/parse_llm_code/actions/workflows/release.yml)
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[![Visits](https://komarev.com/ghpvc/?username=aboutmydreams&repo=parse_llm_code)](https://github.com/aboutmydreams/parse_llm_code)
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[![License](https://img.shields.io/github/license/aboutmydreams/parse_llm_code.svg)](https://github.com/aboutmydreams/parse_llm_code/license)
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[![Stars](https://img.shields.io/github/stars/aboutmydreams/parse_llm_code.svg)](https://github.com/aboutmydreams/parse_llm_code/stargazers)
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[![Forks](https://img.shields.io/github/forks/aboutmydreams/parse_llm_code.svg)](https://github.com/aboutmydreams/parse_llm_code/network)
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[![Downloads](https://pepy.tech/badge/parse_llm_code)](https://pepy.tech/project/parse_llm_code)
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[![Release Version](https://img.shields.io/github/release/aboutmydreams/parse_llm_code.svg)](https://github.com/aboutmydreams/parse_llm_code/releases)
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This library serves the purpose of parsing code snippets from text, particularly helpful when dealing with outputs generated by large language models. Typically, such outputs include both code snippets and explanatory text. By using this library, one can effectively filter and extract the meaningful code portions, aiding in the extraction of useful and actionable code from generated responses.
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## USECASE
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## Run test
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`python3 -m unittest discover -s tests`
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## Contributors
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[![Contributors](https://img.shields.io/github/contributors/aboutmydreams/parse_llm_code.svg)](https://github.com/aboutmydreams/parse_llm_code/graphs/contributors)
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setup.py

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setuptools.setup(
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name="parse_llm_code",
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version="0.1.27",
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version="0.1.29",
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author="aboutmydreams",
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author_email="aboutmydreams@163.com",
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description="a lib to parse llm answer to code",

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