Update app.py
Browse files
app.py
CHANGED
@@ -2,155 +2,7 @@ import streamlit as st
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st.markdown("""
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# 2. Streamlit Docker Example
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https://huggingface.co/spaces/DockerTemplates/streamlit-docker-example/tree/main
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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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# 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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# requirements.txt
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streamlit
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numpy
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pandas
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# 2. Gradio Docker Example
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https://huggingface.co/spaces/sayakpaul/demo-docker-gradio/blob/main/Dockerfile
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# Dockerfile:
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# read the doc: https://huggingface.co/docs/hub/spaces-sdks-docker
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# you will also find guides on how best to write your Dockerfile
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FROM python:3.9
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WORKDIR /code
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COPY ./requirements.txt /code/requirements.txt
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RUN pip install --no-cache-dir --upgrade -r /code/requirements.txt
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# Set up a new user named "user" with user ID 1000
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RUN useradd -m -u 1000 user
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# Switch to the "user" user
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USER user
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# Set home to the user's home directory
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ENV HOME=/home/user \
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PATH=/home/user/.local/bin:$PATH
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# Set the working directory to the user's home directory
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WORKDIR $HOME/app
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# Copy the current directory contents into the container at $HOME/app setting the owner to the user
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COPY --chown=user . $HOME/app
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CMD ["python", "main.py"]
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# main.py
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import gradio as gr
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import torch
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import requests
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from torchvision import transforms
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model = torch.hub.load("pytorch/vision:v0.6.0", "resnet18", pretrained=True).eval()
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response = requests.get("https://git.io/JJkYN")
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labels = response.text.split("\n")
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def predict(inp):
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inp = transforms.ToTensor()(inp).unsqueeze(0)
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with torch.no_grad():
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prediction = torch.nn.functional.softmax(model(inp)[0], dim=0)
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confidences = {labels[i]: float(prediction[i]) for i in range(1000)}
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return confidences
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def run():
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demo = gr.Interface(
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fn=predict,
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inputs=gr.inputs.Image(type="pil"),
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outputs=gr.outputs.Label(num_top_classes=3),
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)
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demo.launch(server_name="0.0.0.0", server_port=7860)
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if __name__ == "__main__":
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run()
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# requirements.txt
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gradio
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torch
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torchvision
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requests
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""")
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st.markdown("""
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+
https://github.com/AaronCWacker/Yggdrasil/tree/main
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https://github.com/AaronCWacker/Docker-ACA-Examples
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""")
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