Streamlit app

Streamlit is a popular framework for building interactive web applications and dashboards. Flyte makes it easy to deploy Streamlit apps as long-running services.

Basic Streamlit app

The simplest way to deploy a Streamlit app is to use the built-in Streamlit “hello” demo:

basic_streamlit.py 
"""A basic Streamlit app using the built-in hello demo."""

import flyte
import flyte.app

image = flyte.Image.from_debian_base(python_version=(3, 12)).with_pip_packages("streamlit==1.41.1")

app_env = flyte.app.AppEnvironment(
    name="streamlit-hello",
    image=image,
    command="streamlit hello --server.port 8080",
    port=8080,
    resources=flyte.Resources(cpu="1", memory="1Gi"),
    requires_auth=False,
)

if __name__ == "__main__":
    flyte.init_from_config()
    app = flyte.deploy(app_env)
    print(f"App URL: {app[0].url}")

Custom Streamlit app

For a custom Streamlit app, use the include parameter to bundle your app files:

custom_streamlit.py 
"""A custom Streamlit app with multiple files."""

import pathlib
import flyte
import flyte.app

image = flyte.Image.from_debian_base(python_version=(3, 12)).with_pip_packages(
    "streamlit==1.41.1",
    "pandas==2.2.3",
    "numpy==2.2.3",
)

app_env = flyte.app.AppEnvironment(
    name="streamlit-custom-app",
    image=image,
    args="streamlit run main.py --server.port 8080",
    port=8080,
    include=["main.py", "utils.py"],  # Include your app files
    resources=flyte.Resources(cpu="1", memory="1Gi"),
    requires_auth=False,
)

if __name__ == "__main__":
    flyte.init_from_config(root_dir=pathlib.Path(__file__).parent)
    app = flyte.deploy(app_env)
    print(f"App URL: {app[0].url}")

Your main.py file would contain your Streamlit app code:

main.py 
all_columns = ["Apples", "Orange", "Pineapple"]
with st.container(border=True):
    columns = st.multiselect("Columns", all_columns, default=all_columns)

all_data = st.cache_data(generate_data)(columns=all_columns, seed=101)

data = all_data[columns]

tab1, tab2 = st.tabs(["Chart", "Dataframe"])
tab1.line_chart(data, height=250)
tab2.dataframe(data, height=250, use_container_width=True)
st.write(f"Environment: {os.environ}")

Multi-file Streamlit app

For apps with multiple files, include all necessary files:

app_env = flyte.app.AppEnvironment(
    name="streamlit-multi-file",
    image=image,
    args="streamlit run main.py --server.port 8080",
    port=8080,
    include=["main.py", "utils.py", "components.py"],  # Include all files
    resources=flyte.Resources(cpu="1", memory="1Gi"),
)

Structure your project like this:

project/
├── main.py           # Main Streamlit app
├── utils.py          # Utility functions
└── components.py     # Reusable components

Example: Data visualization dashboard

Here’s a complete example of a Streamlit dashboard:

# main.py
import streamlit as st
import pandas as pd
import numpy as np

st.title("Sales Dashboard")

# Load data
@st.cache_data
def load_data():
    return pd.DataFrame({
        "date": pd.date_range("2024-01-01", periods=100, freq="D"),
        "sales": np.random.randint(1000, 5000, 100),
    })

data = load_data()

# Sidebar filters
st.sidebar.header("Filters")
start_date = st.sidebar.date_input("Start date", value=data["date"].min())
end_date = st.sidebar.date_input("End date", value=data["date"].max())

# Filter data
filtered_data = data[
    (data["date"] >= pd.Timestamp(start_date)) &
    (data["date"] <= pd.Timestamp(end_date))
]

# Display metrics
col1, col2, col3 = st.columns(3)
with col1:
    st.metric("Total Sales", f"${filtered_data['sales'].sum():,.0f}")
with col2:
    st.metric("Average Sales", f"${filtered_data['sales'].mean():,.0f}")
with col3:
    st.metric("Days", len(filtered_data))

# Chart
st.line_chart(filtered_data.set_index("date")["sales"])

Deploy with:

import flyte
import flyte.app

image = flyte.Image.from_debian_base(python_version=(3, 12)).with_pip_packages(
    "streamlit==1.41.1",
    "pandas==2.2.3",
    "numpy==2.2.3",
)

app_env = flyte.app.AppEnvironment(
    name="sales-dashboard",
    image=image,
    args="streamlit run main.py --server.port 8080",
    port=8080,
    include=["main.py"],
    resources=flyte.Resources(cpu="2", memory="2Gi"),
    requires_auth=False,
)

if __name__ == "__main__":
    flyte.init_from_config()
    app = flyte.deploy(app_env)
    print(f"Dashboard URL: {app[0].url}")

Custom domain

You can use a custom subdomain for your Streamlit app:

app_env = flyte.app.AppEnvironment(
    name="streamlit-app",
    image=image,
    command="streamlit hello --server.port 8080",
    port=8080,
    domain=flyte.app.Domain(subdomain="dashboard"),  # Custom subdomain
    resources=flyte.Resources(cpu="1", memory="1Gi"),
)

Best practices

  1. Use include for custom apps: Always include your app files when deploying custom Streamlit code
  2. Set the port correctly: Ensure your Streamlit app uses --server.port 8080 (or match your port setting)
  3. Cache data: Use @st.cache_data for expensive computations to improve performance
  4. Resource sizing: Adjust resources based on your app’s needs (data size, computations)
  5. Public vs private: Set requires_auth=False for public dashboards, True for internal tools

Troubleshooting

App not loading:

  • Verify the port matches (use --server.port 8080)
  • Check that all required files are included
  • Review container logs for errors

Missing dependencies:

  • Ensure all required packages are in your image’s pip packages
  • Check that file paths in include are correct

Performance issues:

  • Increase CPU/memory resources
  • Use Streamlit’s caching features (@st.cache_data, @st.cache_resource)
  • Optimize data processing