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title: Docker Examples Top 5 Demo
emoji: 🏆
colorFrom: pink
colorTo: pink
sdk: streamlit
sdk_version: 1.19.0
app_file: app.py
pinned: false
license: mit
import streamlit as st
st.markdown("""
2. Streamlit Docker Example
https://huggingface.co/spaces/DockerTemplates/streamlit-docker-example/tree/main
Dockerfile:
FROM python:3.8.9 WORKDIR /app COPY ./requirements.txt /app/requirements.txt COPY ./packages.txt /app/packages.txt RUN apt-get update && xargs -r -a /app/packages.txt apt-get install -y && rm -rf /var/lib/apt/lists/* RUN pip3 install --no-cache-dir -r /app/requirements.txt
User
RUN useradd -m -u 1000 user USER user ENV HOME /home/user ENV PATH $HOME/.local/bin:$PATH WORKDIR $HOME RUN mkdir app WORKDIR $HOME/app COPY . $HOME/app EXPOSE 8501 CMD streamlit run app.py
app.py:
import streamlit as st import pandas as pd import numpy as np st.title('Uber pickups in NYC') DATE_COLUMN = 'date/time' DATA_URL = ('https://s3-us-west-2.amazonaws.com/' 'streamlit-demo-data/uber-raw-data-sep14.csv.gz') @st.cache def load_data(nrows): data = pd.read_csv(DATA_URL, nrows=nrows) lowercase = lambda x: str(x).lower() data.rename(lowercase, axis='columns', inplace=True) data[DATE_COLUMN] = pd.to_datetime(data[DATE_COLUMN]) return data data_load_state = st.text('Loading data...') data = load_data(10000) data_load_state.text("Done! (using st.cache)") if st.checkbox('Show raw data'): st.subheader('Raw data') st.write(data) st.subheader('Number of pickups by hour') hist_values = np.histogram(data[DATE_COLUMN].dt.hour, bins=24, range=(0,24))[0] st.bar_chart(hist_values)
Some number in the range 0-23
hour_to_filter = st.slider('hour', 0, 23, 17) filtered_data = data[data[DATE_COLUMN].dt.hour == hour_to_filter] st.subheader('Map of all pickups at %s:00' % hour_to_filter) st.map(filtered_data)
requirements.txt
streamlit numpy pandas
2. Gradio Docker Example
https://huggingface.co/spaces/sayakpaul/demo-docker-gradio/blob/main/Dockerfile
Dockerfile:
read the doc: https://huggingface.co/docs/hub/spaces-sdks-docker
you will also find guides on how best to write your Dockerfile
FROM python:3.9 WORKDIR /code COPY ./requirements.txt /code/requirements.txt RUN pip install --no-cache-dir --upgrade -r /code/requirements.txt
Set up a new user named "user" with user ID 1000
RUN useradd -m -u 1000 user
Switch to the "user" user
USER user
Set home to the user's home directory
ENV HOME=/home/user
PATH=/home/user/.local/bin:$PATH
Set the working directory to the user's home directory
WORKDIR $HOME/app
Copy the current directory contents into the container at $HOME/app setting the owner to the user
COPY --chown=user . $HOME/app CMD ["python", "main.py"]
main.py
import gradio as gr import torch import requests from torchvision import transforms model = torch.hub.load("pytorch/vision:v0.6.0", "resnet18", pretrained=True).eval() response = requests.get("https://git.io/JJkYN") labels = response.text.split("\n") def predict(inp): inp = transforms.ToTensor()(inp).unsqueeze(0) with torch.no_grad(): prediction = torch.nn.functional.softmax(model(inp)[0], dim=0) confidences = {labels[i]: float(prediction[i]) for i in range(1000)} return confidences def run(): demo = gr.Interface( fn=predict, inputs=gr.inputs.Image(type="pil"), outputs=gr.outputs.Label(num_top_classes=3), ) demo.launch(server_name="0.0.0.0", server_port=7860) if name == "main": run()
requirements.txt
gradio torch torchvision requests """)