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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
""") |