How do I automate tasks using machine learning models in Streamlit?

To automate tasks using machine learning models in Streamlit, you can use the following steps:

1. Train a machine learning model on a dataset of data.
2. Save the model in a file.
3. Create a Streamlit app that allows users to input data.
4. Use the machine learning model to make predictions on the input data.
5. Display the predictions to the user.

Here is an example of how to automate the task of predicting the price of a house using a machine learning model in Streamlit:

1. Train a machine learning model on a dataset of house prices.
2. Save the model in a file called house_price_model.pkl.
3. Create a Streamlit app called house_price_prediction.py.

import streamlit as st
import pickle

# Load the machine learning model
model = pickle.load(open("house_price_model.pkl", "rb"))

# Get the user input
home_size = st.number_input("Enter the home size (square feet):")
bedrooms = st.number_input("Enter the number of bedrooms:")
bathrooms = st.number_input("Enter the number of bathrooms:")

# Make a prediction
prediction = model.predict([[home_size, bedrooms, bathrooms]])

# Display the prediction
st.write("The predicted price of the house is $", prediction[0])

4. Run the Streamlit app.

When the user inputs the home size, number of bedrooms, and number of bathrooms, the Streamlit app will use the machine learning model to make a prediction of the price of the house. The prediction will be displayed to the user.