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title: Docker Examples Top 5 Demo | |
emoji: π | |
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 | |
""") | |