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

""")