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Streamlit, Flask screenshots (#144)
* readme * screenshot * ml-flask * img * login
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examples/flask/login/README.md

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Sample Flask application that allows creating accounts, signing in, and making requests via an API key.
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![](screenshot.webp)
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This application requires a `FLASK_SECRET_KEY` variable, generate one with:
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```sh
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bcrypt==4.1.2
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blinker==1.7.0
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click==8.1.7
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Flask==3.0.2
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Flask-Login==0.6.3
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Flask-SQLAlchemy==3.1.1
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gunicorn==21.2.0
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humanize==4.9.0
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itsdangerous==2.1.2
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Jinja2==3.1.3
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MarkupSafe==2.1.5
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packaging==23.2
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SQLAlchemy==2.0.28
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typing_extensions==4.10.0
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Werkzeug==3.0.1
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WTForms==3.1.2

examples/flask/login/screenshot.webp

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examples/flask/machine-learning/README.md

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A Flask app that makes predictions using an ML model.
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![](screenshot.webp)
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This Flask app serves a machine learning model trained on the `Walmart Recruiting - Store Sales Forecasting` dataset for predicting store sales.
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## Getting Started
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blinker==1.7.0
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click==8.1.7
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contourpy==1.2.0
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cycler==0.12.1
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Flask==3.0.2
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fonttools==4.49.0
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itsdangerous==2.1.2
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Jinja2==3.1.3
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joblib==1.3.2
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kiwisolver==1.4.5
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MarkupSafe==2.1.5
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matplotlib==3.8.3
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numpy==1.26.4
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packaging==23.2
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pandas==2.2.1
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pillow==10.2.0
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pyparsing==3.1.2
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python-dateutil==2.9.0.post0
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pytz==2024.1
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scikit-learn==1.4.1.post1
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scipy==1.12.0
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seaborn==0.13.2
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six==1.16.0
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threadpoolctl==3.3.0
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tzdata==2024.1
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Werkzeug==3.0.1

examples/flask/machine-learning/requirements.txt

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flask
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seaborn
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joblib
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numpy
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examples/flask/machine-learning/walmart_sales.ipynb

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examples/streamlit/data-viz/README.md

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# Data visualisation
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An interactive app that explores a public Uber dataset for pickups and drop-offs in New York City.
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An interactive app that explores a public Uber dataset for pickups and drop-offs in New York City.
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![](screenshot.webp)
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altair==5.2.0
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attrs==23.2.0
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blinker==1.7.0
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cachetools==5.3.3
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certifi==2024.2.2
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charset-normalizer==3.3.2
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click==8.1.7
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gitdb==4.0.11
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GitPython==3.1.42
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idna==3.6
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Jinja2==3.1.3
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jsonschema==4.21.1
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jsonschema-specifications==2023.12.1
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markdown-it-py==3.0.0
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MarkupSafe==2.1.5
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mdurl==0.1.2
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numpy==1.26.4
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packaging==23.2
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pandas==2.2.1
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pillow==10.2.0
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protobuf==4.25.3
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pyarrow==15.0.1
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pydeck==0.8.1b0
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Pygments==2.17.2
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python-dateutil==2.9.0.post0
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pytz==2024.1
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referencing==0.33.0
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requests==2.31.0
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rich==13.7.1
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rpds-py==0.18.0
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six==1.16.0
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smmap==5.0.1
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streamlit==1.32.0
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tenacity==8.2.3
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toml==0.10.2
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toolz==0.12.1
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tornado==6.4
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typing_extensions==4.10.0
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tzdata==2024.1
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urllib3==2.2.1
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