Update app.py

app.py

@@ -2,155 +2,7 @@ import streamlit as st
 
 
 st.markdown("""
+https://github.com/AaronCWacker/Yggdrasil/tree/main
 
-
-# 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
-
+https://github.com/AaronCWacker/Docker-ACA-Examples
 """)