Streamlit is an open-source Python library that makes it easy to create custom web apps for machine learning and data science. It turns data scripts into shareable web apps in minutes, not weeks.
import streamlit as st
import pandas as pd
st.title('My First Streamlit App')
st.write('Hello, World!')
# Load some data
data = pd.DataFrame({
'x': [1, 2, 3, 4],
'y': [10, 20, 30, 40]
})
# Create a chart
st.line_chart(data)
pip install streamlit
# Save as app.py
import streamlit as st
st.title('Hello Streamlit!')
name = st.text_input('Enter your name')
if name:
st.write(f'Hello {name}!')
streamlit run app.pyst.write() - Write text/datast.markdown() - Format textst.sidebar - Add sidebar elementsst.button() - Create buttonsst.plotly_chart() - Display Plotly charts
import streamlit as st
import pandas as pd
import plotly.express as px
st.title('Data Explorer')
# File uploader
uploaded_file = st.file_uploader("Choose a CSV file")
if uploaded_file is not None:
# Load data
df = pd.read_csv(uploaded_file)
# Show data
st.write("### Data Preview")
st.write(df.head())
# Plot
col = st.selectbox("Select column to plot", df.columns)
fig = px.histogram(df, x=col)
st.plotly_chart(fig)
import streamlit as st
from sklearn.datasets import load_iris
from sklearn.ensemble import RandomForestClassifier
st.title('Iris Flower Classifier')
# Load data
iris = load_iris()
model = RandomForestClassifier()
model.fit(iris.data, iris.target)
# Input features
st.sidebar.header('Input Parameters')
sepal_length = st.sidebar.slider('Sepal length', 4.0, 8.0, 5.0)
sepal_width = st.sidebar.slider('Sepal width', 2.0, 5.0, 3.0)
petal_length = st.sidebar.slider('Petal length', 1.0, 7.0, 4.0)
petal_width = st.sidebar.slider('Petal width', 0.1, 2.5, 1.0)
# Prediction
prediction = model.predict([[
sepal_length, sepal_width,
petal_length, petal_width
]])
st.write('### Prediction:', iris.target_names[prediction[0]])