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Duplicate from DockerTemplates/streamlit-docker-example
Browse filesCo-authored-by: Radamés Ajna <radames@users.noreply.huggingface.co>
- .gitignore +134 -0
- .streamlit/config.toml +9 -0
- Dockerfile +23 -0
- README.md +16 -0
- app.py +36 -0
- packages.txt +0 -0
- requirements.txt +3 -0
.gitignore
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venv/
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# Byte-compiled / optimized / DLL files
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__pycache__/
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*.py[cod]
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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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pip-wheel-metadata/
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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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# 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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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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.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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# PEP 582; used by e.g. github.com/David-OConnor/pyflow
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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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.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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.vscode/
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data/audio/
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.streamlit/config.toml
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[theme]
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primaryColor="#e5ab00"
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backgroundColor="#0e1117"
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secondaryBackgroundColor="#282929"
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textColor = "#ffffff"
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font="sans serif"
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[server]
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maxUploadSize = 200
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Dockerfile
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FROM python:3.8.9
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WORKDIR /app
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COPY ./requirements.txt /app/requirements.txt
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COPY ./packages.txt /app/packages.txt
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RUN apt-get update && xargs -r -a /app/packages.txt apt-get install -y && rm -rf /var/lib/apt/lists/*
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RUN pip3 install --no-cache-dir -r /app/requirements.txt
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# User
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RUN useradd -m -u 1000 user
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USER user
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ENV HOME /home/user
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ENV PATH $HOME/.local/bin:$PATH
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WORKDIR $HOME
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RUN mkdir app
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WORKDIR $HOME/app
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COPY . $HOME/app
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EXPOSE 8501
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CMD streamlit run app.py
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README.md
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---
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title: Streamlit Docker Template
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emoji: 📉
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colorFrom: blue
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colorTo: green
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sdk: docker
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app_port: 8501
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pinned: false
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duplicated_from: DockerTemplates/streamlit-docker-example
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---
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## 🧠 Streamlit Docker Template 🔎
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Streamlit Docker Template is a template for creating a Streamlit app with Docker and Hugging Face Spaces.
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Code from https://docs.streamlit.io/library/get-started/create-an-app
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app.py
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import streamlit as st
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import pandas as pd
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import numpy as np
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st.title('Uber pickups in NYC')
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DATE_COLUMN = 'date/time'
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DATA_URL = ('https://s3-us-west-2.amazonaws.com/'
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'streamlit-demo-data/uber-raw-data-sep14.csv.gz')
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@st.cache
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def load_data(nrows):
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data = pd.read_csv(DATA_URL, nrows=nrows)
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lowercase = lambda x: str(x).lower()
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data.rename(lowercase, axis='columns', inplace=True)
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data[DATE_COLUMN] = pd.to_datetime(data[DATE_COLUMN])
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return data
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data_load_state = st.text('Loading data...')
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data = load_data(10000)
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data_load_state.text("Done! (using st.cache)")
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if st.checkbox('Show raw data'):
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st.subheader('Raw data')
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st.write(data)
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st.subheader('Number of pickups by hour')
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hist_values = np.histogram(data[DATE_COLUMN].dt.hour, bins=24, range=(0,24))[0]
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st.bar_chart(hist_values)
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# Some number in the range 0-23
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hour_to_filter = st.slider('hour', 0, 23, 17)
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filtered_data = data[data[DATE_COLUMN].dt.hour == hour_to_filter]
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st.subheader('Map of all pickups at %s:00' % hour_to_filter)
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st.map(filtered_data)
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packages.txt
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requirements.txt
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streamlit
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numpy
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pandas
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