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‎.github/dependabot.yml‎

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version: 2
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updates:
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- package-ecosystem: pip
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directory: "/"
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schedule:
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interval: daily
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time: "15:00"
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- package-ecosystem: github-actions
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directory: "/"
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schedule:
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interval: daily
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time: "15:00"

‎.github/workflows/github-docker.yml‎

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---
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name: Docker Image CI(GitHub)
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on:
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push:
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jobs:
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build:
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name: Build & Push
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runs-on: ubuntu-latest
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env:
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REPOSITORY_URL: ghcr.io
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IMAGE_NAME: oldtyt/python-ai-console
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steps:
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- name: Checkout
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uses: actions/checkout@v4.1.2
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- name: Variables
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id: vars
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run: echo "::set-output name=SHORT_COMMIT_ID::$(git rev-parse --short HEAD)"
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- name: Build image
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id: docker-build
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run: >-
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docker build
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-t $IMAGE_NAME
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-t $REPOSITORY_URL/$IMAGE_NAME:latest .
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- name: Docker Login
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uses: docker/login-action@v3
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with:
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registry: ${{ env.REPOSITORY_URL }}
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username: ${{ github.actor }}
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password: ${{ secrets.GITHUB_TOKEN }}
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- name: Publish Image
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id: docker-push
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run: docker push --all-tags $REPOSITORY_URL/$IMAGE_NAME

‎.gitignore‎

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# Byte-compiled / optimized / DLL files
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__pycache__/
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*.py[codz]
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*$py.class
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# C extensions
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*.so
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# Distribution / packaging
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.Python
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build/
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develop-eggs/
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dist/
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downloads/
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eggs/
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.eggs/
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lib/
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lib64/
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parts/
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sdist/
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var/
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wheels/
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share/python-wheels/
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*.egg-info/
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.installed.cfg
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*.egg
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MANIFEST
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# PyInstaller
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# Usually these files are written by a python script from a template
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# before PyInstaller builds the exe, so as to inject date/other infos into it.
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*.manifest
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*.spec
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# Installer logs
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pip-log.txt
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pip-delete-this-directory.txt
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# Unit test / coverage reports
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htmlcov/
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.tox/
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.nox/
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.coverage
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.coverage.*
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.cache
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nosetests.xml
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coverage.xml
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*.cover
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*.py.cover
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.hypothesis/
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.pytest_cache/
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cover/
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# Translations
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*.mo
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*.pot
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# Django stuff:
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*.log
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local_settings.py
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db.sqlite3
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db.sqlite3-journal
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# Flask stuff:
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instance/
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.webassets-cache
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# Scrapy stuff:
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.scrapy
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# Sphinx documentation
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docs/_build/
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# PyBuilder
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.pybuilder/
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target/
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# Jupyter Notebook
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.ipynb_checkpoints
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# IPython
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profile_default/
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ipython_config.py
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# pyenv
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# For a library or package, you might want to ignore these files since the code is
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# intended to run in multiple environments; otherwise, check them in:
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# .python-version
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# pipenv
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# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
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# However, in case of collaboration, if having platform-specific dependencies or dependencies
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# having no cross-platform support, pipenv may install dependencies that don't work, or not
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# install all needed dependencies.
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#Pipfile.lock
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# UV
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# Similar to Pipfile.lock, it is generally recommended to include uv.lock in version control.
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# This is especially recommended for binary packages to ensure reproducibility, and is more
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# commonly ignored for libraries.
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#uv.lock
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# poetry
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# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
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# This is especially recommended for binary packages to ensure reproducibility, and is more
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# commonly ignored for libraries.
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# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
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#poetry.lock
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#poetry.toml
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# pdm
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# Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
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# pdm recommends including project-wide configuration in pdm.toml, but excluding .pdm-python.
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# https://pdm-project.org/en/latest/usage/project/#working-with-version-control
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#pdm.lock
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#pdm.toml
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.pdm-python
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.pdm-build/
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# pixi
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# Similar to Pipfile.lock, it is generally recommended to include pixi.lock in version control.
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#pixi.lock
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# Pixi creates a virtual environment in the .pixi directory, just like venv module creates one
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# in the .venv directory. It is recommended not to include this directory in version control.
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.pixi
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# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
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__pypackages__/
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# Celery stuff
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celerybeat-schedule
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celerybeat.pid
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# SageMath parsed files
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*.sage.py
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# Environments
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.env
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.envrc
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.venv
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env/
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venv/
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ENV/
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env.bak/
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venv.bak/
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# Spyder project settings
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.spyderproject
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.spyproject
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# Rope project settings
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.ropeproject
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# mkdocs documentation
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/site
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# mypy
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.mypy_cache/
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.dmypy.json
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dmypy.json
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# Pyre type checker
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.pyre/
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# pytype static type analyzer
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.pytype/
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# Cython debug symbols
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cython_debug/
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# PyCharm
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# JetBrains specific template is maintained in a separate JetBrains.gitignore that can
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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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# Abstra
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# Abstra is an AI-powered process automation framework.
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# Ignore directories containing user credentials, local state, and settings.
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# Learn more at https://abstra.io/docs
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.abstra/
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# Visual Studio Code
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# Visual Studio Code specific template is maintained in a separate VisualStudioCode.gitignore
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# that can be found at https://github.com/github/gitignore/blob/main/Global/VisualStudioCode.gitignore
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# and can be added to the global gitignore or merged into this file. However, if you prefer,
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# you could uncomment the following to ignore the entire vscode folder
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# .vscode/
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# Ruff stuff:
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.ruff_cache/
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# PyPI configuration file
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.pypirc
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# Marimo
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marimo/_static/
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marimo/_lsp/
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__marimo__/
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# Streamlit
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.streamlit/secrets.toml

‎Dockerfile‎

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ARG IMAGE=python:3.13-alpine
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FROM $IMAGE AS builder
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COPY requirements.txt .
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RUN pip wheel --no-cache-dir --no-deps --wheel-dir /app/wheels -r requirements.txt
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FROM $IMAGE
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WORKDIR /app
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COPY --from=builder /app/wheels /wheels
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RUN apk add --no-cache musl-locales musl-locales-lang
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ENV LANG=C.UTF-8 \
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LC_ALL=C.UTF-8 \
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PYTHONIOENCODING=utf-8
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RUN pip install --no-cache /wheels/*
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COPY . .
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ENTRYPOINT [ "python3" ]
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CMD ["/app/main.py"]

‎README.md‎

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# Python AI Console
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Python AI Console is an interactive command-line tool that allows users to input prompts, generates Python code using an AI model (powered by OpenAI), executes the generated code, and maintains a history of interactions. It captures stdout/stderr from code execution and stores everything in a JSON-based history file for context in future prompts.
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## Features
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- Interactive prompt loop for user inputs.
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- AI-generated Python code based on prompts and conversation history.
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- Safe execution of generated code with output capturing.
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- Persistent history management using Pydantic models.
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- Support for environment variables to customize behavior.
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- Docker support for easy deployment.
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## Installation
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### From Source
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1. Clone the repository:
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```
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git clone https://github.com/OldTyT/python-ai-console.git
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```
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2. Navigate to the project directory:
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```
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cd python-ai-console
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```
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3. Install dependencies:
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```
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pip install -r requirements.txt
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```
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4. Run the application:
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```
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python3 main.py
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```
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### Using Docker
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You can run the project in a Docker container. Ensure you have Docker installed.
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```
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docker run --rm -ti -e HISTORY_PATH=/history/history.json -e OPENAI_API_KEY=YOUR_KEY -v my_history:/history ghcr.io/oldtyt/autopost-python-ai-console
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```
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- Replace `YOUR_KEY` with your actual OpenAI API key.
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## Usage
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Once running, the console will prompt you for input:
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- Enter a prompt (e.g., "Write a function to calculate factorial").
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- The AI generates Python code based on the prompt and recent history.
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- You'll be asked if you want to execute the code (y/n).
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- If executed, outputs (stdout/stderr) are captured and displayed.
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- History is saved to the specified file for context in future interactions.
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To exit, interrupt the process (e.g., Ctrl+C).
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## Environment Variables
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Configure the application using these environment variables:
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- **HISTORY_PATH**: Path to the JSON file for storing interaction history. Default: `history.json`.
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- **HISTORY_SIZE**: Number of recent history entries to include in AI prompts for context. Default: `20`.
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- **OPENAI_API_KEY**: Your OpenAI API key (required for AI code generation).
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Example (on Unix-like systems):
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```
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export OPENAI_API_KEY=your-api-key
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export HISTORY_SIZE=10
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python3 main.py
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```
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## Dependencies
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- Python 3.10+ (tested up to 3.13)
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- Libraries: openai, pydantic, loguru, etc. (see `requirements.txt`)
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## Contributing
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Contributions are welcome! Please open an issue or submit a pull request.

‎history.json‎

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{"history": [{"datetime": "2025-08-11T14:41:08.915812", "user_prompt": null, "ai_model_response": null, "stdout_result_code_execute": null, "stderr_result_code_execute": null}, {"datetime": "2025-08-11T14:41:08.915524", "user_prompt": "\u041f\u0440\u0438\u0432\u0435\u0442! \u041a\u0430\u043a \u0434\u0435\u043b\u0430? \u041a\u0430\u043a\u0430\u044f \u0441\u0435\u0439\u0447\u0430\u0441 \u043f\u043e\u0433\u043e\u0434\u0430 \u0432 \u043c\u043e\u0441\u043a\u0432\u0435?", "ai_model_response": "import datetime, sys\ntry:\n import requests\n def fetch():\n r = requests.get('https://wttr.in/Moscow?format=%l:+%c+%t+%h+%w&lang=ru', timeout=10)\n r.encoding = 'utf-8'\n if r.status_code == 200 and r.text.strip():\n return r.text.strip()\n raise Exception(f'HTTP {r.status_code}')\nexcept Exception:\n from urllib import request, error\n def fetch():\n try:\n with request.urlopen('https://wttr.in/Moscow?format=%l:+%c+%t+%h+%w&lang=ru', timeout=10) as r:\n return r.read().decode('utf-8').strip()\n except error.HTTPError as e:\n raise Exception(f'HTTP {e.code}')\n except Exception as e:\n raise\n\nnow = datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')\ntry:\n weather = fetch()\n print(\"\u041f\u0440\u0438\u0432\u0435\u0442! \u0423 \u043c\u0435\u043d\u044f \u0432\u0441\u0451 \u0445\u043e\u0440\u043e\u0448\u043e, \u0441\u043f\u0430\u0441\u0438\u0431\u043e.\")\n print(f\"\u0412\u0440\u0435\u043c\u044f: {now}\")\n print(f\"\u0421\u0435\u0439\u0447\u0430\u0441 \u043f\u043e\u0433\u043e\u0434\u0430 \u0432 \u041c\u043e\u0441\u043a\u0432\u0435: {weather}\")\nexcept Exception as e:\n print(\"\u041f\u0440\u0438\u0432\u0435\u0442! \u0423 \u043c\u0435\u043d\u044f \u0432\u0441\u0451 \u0445\u043e\u0440\u043e\u0448\u043e, \u0441\u043f\u0430\u0441\u0438\u0431\u043e.\")\n print(f\"\u0412\u0440\u0435\u043c\u044f: {now}\")\n print(\"\u041d\u0435 \u0443\u0434\u0430\u043b\u043e\u0441\u044c \u043f\u043e\u043b\u0443\u0447\u0438\u0442\u044c \u043f\u043e\u0433\u043e\u0434\u0443 \u0432 \u041c\u043e\u0441\u043a\u0432\u0435. \u041f\u043e\u043f\u0440\u043e\u0431\u0443\u0439\u0442\u0435 \u043f\u043e\u0437\u0436\u0435.\")", "stdout_result_code_execute": "\u041f\u0440\u0438\u0432\u0435\u0442! \u0423 \u043c\u0435\u043d\u044f \u0432\u0441\u0451 \u0445\u043e\u0440\u043e\u0448\u043e, \u0441\u043f\u0430\u0441\u0438\u0431\u043e.\n\u0412\u0440\u0435\u043c\u044f: 2025-08-11 14:41:24\n\u0421\u0435\u0439\u0447\u0430\u0441 \u043f\u043e\u0433\u043e\u0434\u0430 \u0432 \u041c\u043e\u0441\u043a\u0432\u0435: Moscow: \u26c5\ufe0f +15\u00b0C 94% \u21977km/h\n", "stderr_result_code_execute": ""}, {"datetime": "2025-08-11T14:41:50.094569", "user_prompt": "\u041c\u043e\u0436\u0435\u0448\u044c \u043f\u043e\u0432\u0442\u043e\u0440\u0438\u0442\u044c? \u0423 \u043c\u0435\u043d\u044f \u0432\u044b\u0432\u043e\u0434 \u043f\u0440\u043e\u043f\u0430\u043b", "ai_model_response": "import datetime, sys\ntry:\n import requests\n def fetch():\n try:\n r = requests.get('https://wttr.in/Moscow?format=%l:+%c+%t+%h+%w&lang=ru', timeout=10)\n r.encoding = 'utf-8'\n if r.status_code == 200 and r.text.strip():\n return r.text.strip()\n raise Exception(f'HTTP {r.status_code}')\n except Exception:\n raise\nexcept Exception:\n from urllib import request, error\n def fetch():\n try:\n with request.urlopen('https://wttr.in/Moscow?format=%l:+%c+%t+%h+%w&lang=ru', timeout=10) as r:\n return r.read().decode('utf-8').strip()\n except error.HTTPError as e:\n raise Exception(f'HTTP {e.code}')\n except Exception:\n raise\n\nnow = datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')\ntry:\n weather = fetch()\n print(\"\u041f\u0440\u0438\u0432\u0435\u0442! \u0423 \u043c\u0435\u043d\u044f \u0432\u0441\u0451 \u0445\u043e\u0440\u043e\u0448\u043e, \u0441\u043f\u0430\u0441\u0438\u0431\u043e.\")\n print(f\"\u0412\u0440\u0435\u043c\u044f: {now}\")\n print(f\"\u0421\u0435\u0439\u0447\u0430\u0441 \u043f\u043e\u0433\u043e\u0434\u0430 \u0432 \u041c\u043e\u0441\u043a\u0432\u0435: {weather}\")\nexcept Exception:\n print(\"\u041f\u0440\u0438\u0432\u0435\u0442! \u0423 \u043c\u0435\u043d\u044f \u0432\u0441\u0451 \u0445\u043e\u0440\u043e\u0448\u043e, \u0441\u043f\u0430\u0441\u0438\u0431\u043e.\")\n print(f\"\u0412\u0440\u0435\u043c\u044f: {now}\")\n print(\"\u041d\u0435 \u0443\u0434\u0430\u043b\u043e\u0441\u044c \u043f\u043e\u043b\u0443\u0447\u0438\u0442\u044c \u043f\u043e\u0433\u043e\u0434\u0443 \u0432 \u041c\u043e\u0441\u043a\u0432\u0435. \u041f\u043e\u043f\u0440\u043e\u0431\u0443\u0439\u0442\u0435 \u043f\u043e\u0437\u0436\u0435.\")", "stdout_result_code_execute": "\u041f\u0440\u0438\u0432\u0435\u0442! \u0423 \u043c\u0435\u043d\u044f \u0432\u0441\u0451 \u0445\u043e\u0440\u043e\u0448\u043e, \u0441\u043f\u0430\u0441\u0438\u0431\u043e.\n\u0412\u0440\u0435\u043c\u044f: 2025-08-11 14:42:03\n\u0421\u0435\u0439\u0447\u0430\u0441 \u043f\u043e\u0433\u043e\u0434\u0430 \u0432 \u041c\u043e\u0441\u043a\u0432\u0435: Moscow: \u26c5\ufe0f +15\u00b0C 94% \u21977km/h\n", "stderr_result_code_execute": ""}, {"datetime": "2025-08-11T14:45:50.573751", "user_prompt": "\u041a\u0430\u043a\u0430\u044f \u043f\u043e\u0433\u043e\u0434\u0430 \u0432 \u043c\u0441\u043a?", "ai_model_response": "import datetime, sys\ntry:\n import requests\n def fetch():\n r = requests.get('https://wttr.in/Moscow?format=%l:+%c+%t+%h+%w&lang=ru', timeout=10)\n r.encoding = 'utf-8'\n if r.status_code == 200 and r.text.strip():\n return r.text.strip()\n raise Exception(f'HTTP {r.status_code}')\nexcept Exception:\n from urllib import request, error\n def fetch():\n try:\n with request.urlopen('https://wttr.in/Moscow?format=%l:+%c+%t+%h+%w&lang=ru', timeout=10) as r:\n return r.read().decode('utf-8').strip()\n except error.HTTPError as e:\n raise Exception(f'HTTP {e.code}')\n except Exception:\n raise\n\nnow = datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')\ntry:\n weather = fetch()\n print(f\"\u0412\u0440\u0435\u043c\u044f: {now}\")\n print(f\"\u0421\u0435\u0439\u0447\u0430\u0441 \u043f\u043e\u0433\u043e\u0434\u0430 \u0432 \u041c\u043e\u0441\u043a\u0432\u0435: {weather}\")\nexcept Exception:\n print(f\"\u0412\u0440\u0435\u043c\u044f: {now}\")\n print(\"\u041d\u0435 \u0443\u0434\u0430\u043b\u043e\u0441\u044c \u043f\u043e\u043b\u0443\u0447\u0438\u0442\u044c \u043f\u043e\u0433\u043e\u0434\u0443 \u0432 \u041c\u043e\u0441\u043a\u0432\u0435. \u041f\u043e\u043f\u0440\u043e\u0431\u0443\u0439\u0442\u0435 \u043f\u043e\u0437\u0436\u0435.\")", "stdout_result_code_execute": "\u0412\u0440\u0435\u043c\u044f: 2025-08-11 14:46:00\n\u0421\u0435\u0439\u0447\u0430\u0441 \u043f\u043e\u0433\u043e\u0434\u0430 \u0432 \u041c\u043e\u0441\u043a\u0432\u0435: Moscow: \u26c5\ufe0f +15\u00b0C 94% \u21977km/h\n", "stderr_result_code_execute": ""}]}

‎history/__init__.py‎

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# history/__init__.py
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from .models import HistoryModel, HistoryList
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from .state import History

‎history/models.py‎

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from __future__ import annotations
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from datetime import datetime
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from typing import List, Optional
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from pydantic import BaseModel
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class HistoryModel(BaseModel):
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datetime: datetime
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user_prompt: Optional[str] = None
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ai_model_response: Optional[str] = None
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stdout_result_code_execute: Optional[str] = None
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stderr_result_code_execute: Optional[str] = None
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class HistoryList(BaseModel):
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history: List[HistoryModel]

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