Streamlit App Deployment with nohup in Google Compute Engine

nohup streamlit run my_app.py > streamlit.log 2>&1 &

ps aux | grep "streamlit run"

kill -9 PROCESS_ID

# streamlit_app.py

import streamlit as st
import pandas as pd
import numpy as np
import plotly.express as px
from sklearn import datasets

def load_data():
    iris = datasets.load_iris()
    df = pd.DataFrame(data= np.c_[iris['data'], iris['target']],
                      columns= iris['feature_names'] + ['target'])
    df['species'] = df['target'].map({0: 'setosa', 1: 'versicolor', 2: 'virginica'})
    return df

def main():
    
    df = load_data()

    st.title("Streamlit Iris Data Visualization")

    st.write("""
    ## Explore the Iris dataset
    Use the controls below to explore different visualizations of the Iris dataset.
    """)

    # Sidebar for user controls
    features = df.columns[:-2]  # excluding target and species
    x_axis = st.sidebar.selectbox("Choose X axis", features, index=0)
    y_axis = st.sidebar.selectbox("Choose Y axis", features, index=1)

    # Plotting
    fig = px.scatter(df, x=x_axis, y=y_axis, color='species', 
                    title=f"{x_axis} vs. {y_axis} by Species")
    st.plotly_chart(fig)

    # Display raw data on demand
    if st.checkbox("Show raw data", False):  # False means the checkbox is unchecked by default
        st.write(df)

if __name__ == '__main__':
    main()

(base) ~/mulcampFP$ nohup streamlit run main.py > streamlit.log 2>&1 &
[1] 2593