here

Dockerfile

@@ -0,0 +1,27 @@
+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 \
+    --server.headless true \
+    --server.enableCORS false \
+    --server.enableXsrfProtection false \
+    --server.fileWatcherType none

README.md

@@ -1,11 +1,15 @@
 ---
-title: Testspace
-emoji: 🏃
-colorFrom: green
-colorTo: pink
+title: Streamlit Docker Template
+emoji: 📉
+colorFrom: blue
+colorTo: green
 sdk: docker
+app_port: 8501
 pinned: false
-license: apache-2.0
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+## 🧠 Streamlit Docker Template 🔎
+
+Streamlit Docker Template is a template for creating a Streamlit app with Docker and Hugging Face Spaces.
+
+Code from https://docs.streamlit.io/library/get-started/create-an-app

app.py

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+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_data
+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)
+
+uploaded_file = st.file_uploader("Choose a file")
+if uploaded_file is not None:
+    st.write(uploaded_file.name)
+    bytes_data = uploaded_file.getvalue()
+    st.write(len(bytes_data), "bytes")

requirements.txt

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+streamlit
+numpy
+pandas