Streamlit vs Gradio — Choosing the right framework for your ML app

Streamlit and Gradio have gained significant popularity as Python libraries for building and sharing machine learning applications.

Both Streamlit and Gradio have easy-to-use interfaces and need little coding. This helps developers create interactive demos and web applications quickly. However, they focus on different areas and offer unique features and customization options.

This article compares Streamlit and Gradio, highlights their latest features, and shows similar functions with quick demos to help you choose the right framework for your needs.

Streamlit

Streamlit is a free Python library that helps you create and share data-focused web apps easily. It is made for building interactive dashboards, visualizations, and machine learning demos without much work. Streamlit’s easy-to-use interface and quick setup make it a popular tool for data scientists and machine learning engineers.

There are different Streamlit products available:

• Streamlit library: This is the core open-source Python library.
• Streamlit Community Cloud: This is a UI/playground available at streamlit.io, where you can write and run Streamlit apps for free. However, hardware resources are limited, and there are some additional limitations. Support is provided by Snowflake but is limited to bugs, access, and a few other things.
• Streamlit-in-Snowflake: This is the Enterprise version of Streamlit, available directly in the Snowflake UI.

Key features

• Simple API: Streamlit’s API is designed to be intuitive and easy to learn, allowing developers to build applications with minimal code.
• Interactive widgets: Streamlit provides a range of interactive widgets, such as sliders, buttons, and text inputs, to enhance user engagement.
• Data visualization: It supports various data visualization libs, including Matplotlib, Seaborn, and Plotly, enabling developers to create informative and visually appealing dashboards.
• Caching: The built-in caching mechanism optimizes performance by storing the results of expensive computations, reducing loading times.
• Deployment: The library offers various deployment options, including Streamlit Community Cloud, Docker, and Kubernetes, making it easy to share applications with others.

Latest features

In addition to its core features, Streamlit is constantly evolving with new updates and enhancements. Some of the latest features include:

• Support for Python 3.12: The library now supports Python 3.12, ensuring compatibility with the newest language features and libraries.
• Non-fullscreen chat layouts: Developers can use chat messages and chat input in containers, expanders, and the sidebar, providing more flexibility in designing chat-based applications.
• Link column formatting: Streamlit allows developers to change the display text of link columns in st.dataframe and st.data_editor, improving the presentation of tabular data.
• Scroll container: Developers can set a fixed height on st.container and make it scrollable, enhancing the user experience for long-form content.
• Stacked area charts: Streamlit’s built-in charting capabilities now include stacked area charts, providing more options for data visualization3.

Streamlit also has exciting upcoming features on its roadmap, such as:

• Theming configuration options: Streamlit will consolidate a set of new theming configuration options, allowing for greater customization of fonts, button border radius, and default text size and spacing.
• Selection and click events for maps: Selection and click events will be enabled on maps, allowing developers to capture user interactions and provide more dynamic map-based applications.
• Multimodal chat input: The library will introduce multimodal chat input with a new accept_file argument, enabling users to upload images and other files within chat interfaces.

See this video with more explanations about those topics:

 

Quick demo

Here’s a simple Streamlit app that demonstrates how to display a Pandas DataFrame and a Matplotlib chart.

First:

pip install streamlit pandas matplotlib numpy

Then:

import streamlit as st
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np

# Create a title
st.title("Data Visualization Demo")

# Sample data
data = {
    'Name': ['Alice', 'Bob', 'Charlie', 'David', 'Eve'],
    'Score': [85, 92, 78, 95, 88],
    'Department': ['Sales', 'Engineering', 'Marketing', 'Engineering', 'Sales']
}
df = pd.DataFrame(data)

# Create two columns
left_col, right_col = st.columns([1, 2])

# Left column - Controls
with left_col:
    st.subheader("Analysis Controls")
    selected_dept = st.selectbox(
        "Select Department",
        df['Department'].unique()
    )
    
    # Add some space between controls
    st.write("")
    
    col1, col2 = st.columns(2)
    with col1:
        st.button("Clear")
    with col2:
        st.button("Submit", type="primary")

# Right column - Results
with right_col:
    # Filter data
    filtered_df = df[df['Department'] == selected_dept]
    
    # Display filtered data
    st.subheader("Filtered Data")
    st.dataframe(filtered_df, hide_index=True)
    
    # Visualization
    st.subheader("Score Visualization")
    fig, ax = plt.subplots(figsize=(8, 5))
    ax.bar(filtered_df['Name'], filtered_df['Score'])
    ax.set_xlabel('Name')
    ax.set_ylabel('Score')
    ax.set_title(f'Scores in {selected_dept} Department')
    plt.xticks(rotation=45)
    st.pyplot(fig)
    
    # Statistics
    st.subheader("Statistics")
    st.write(f"Average score in {selected_dept}: {filtered_df['Score'].mean():.2f}")

Run with the following command:

# Save the code above in app.py and then run
streamlit run app.py

This code example showcases Streamlit’s ability to display data and generate visualizations within a web application.

Visualization:

Gradio

Gradio is an open-source Python library designed for building machine learning demos and web apps. It focuses on simplifying the process of creating interactive interfaces for ML models, making it easy to share and test them.

Gradio’s tight integration with Hugging Face, further enhances its capabilities. The library is particularly well-suited for creating ML demos, especially when you want to embed them in a Jupyter Notebook or share them quickly with others.

Key features

• Model integration: Gradio integrates with various libraries and frameworks, including TensorFlow, PyTorch, and Hugging Face Transformers.
• Interactive components: The library offers a wide range of interactive components, such as image inputs, text boxes, and sliders, to facilitate user interaction with machine learning models. More in docs.
• Sharing and collaboration: It allows developers to easily share their applications with others by generating shareable links or embedding them in websites and notebooks.
• Customization: Gradio provides options for customizing the appearance and behavior of applications, allowing developers to tailor the user interface to their specific needs.
• Examples and tutorials: It offers a rich collection of examples and tutorials, making it easy for beginners to get started and learn the library’s functionalities.

Latest features

Gradio is continuously evolving with new features and improvements (link). Some of the latest features in Gradio 5 include:

• AI playground: Gradio 5 introduces an experimental AI Playground that allows users to design and preview AI-powered applications using natural language prompts (link).
• Enhanced web development capabilities: The version includes improvements to its web development capabilities, enabling developers to build more sophisticated and interactive applications.
• Bolstered security measures: It incorporates enhanced security measures to protect user data and ensure the integrity of applications.
• Support for streaming use cases: Gradio provides extensive documentation and example demos focused on common streaming use cases, such as webcam-based object detection, video streaming, and real-time speech transcription.
• Multi-page gradio apps: It now supports multi-page applications with native navbars and sidebars, enabling developers to create more complex and organized user interfaces.

Gradio also has ambitious plans for the future, including:

• Mobile support: Gradio aims to support running Gradio apps on mobile devices using Progressive Web Apps (PWAs) and potentially native app support.
• Richer DataFrame component:  Plans to enhance the DataFrame component with support for common spreadsheet-type operations.

Quick demo

Here’s a simple Gradio app that demonstrates how to create an interface for a sentiment analysis model.
First:

pip install gradio pandas matplotlib numpy

Then:

import gradio as gr
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np

# Sample data
data = {
    'Name': ['Alice', 'Bob', 'Charlie', 'David', 'Eve'],
    'Score': [85, 92, 78, 95, 88],
    'Department': ['Sales', 'Engineering', 'Marketing', 'Engineering', 'Sales']
}
df = pd.DataFrame(data)

def analyze_data(department):
    # Filter data
    filtered_df = df[df['Department'] == department]
    
    # Create visualization
    fig, ax = plt.subplots(figsize=(8, 5))
    ax.bar(filtered_df['Name'], filtered_df['Score'])
    ax.set_xlabel('Name')
    ax.set_ylabel('Score')
    ax.set_title(f'Scores in {department} Department')
    plt.xticks(rotation=45)
    
    # Calculate statistics
    avg_score = filtered_df['Score'].mean()
    
    return (
        filtered_df,
        fig,
        f"Average score in {department}: {avg_score:.2f}"
    )

# Convert departments to a list
departments = list(df['Department'].unique())

# Create Gradio interface
demo = gr.Interface(
    fn=analyze_data,
    inputs=gr.Dropdown(choices=departments, label="Select Department"),
    outputs=[
        gr.DataFrame(label="Filtered Data"),
        gr.Plot(label="Score Visualization"),
        gr.Textbox(label="Statistics")
    ],
    title="Data Visualization Demo",
    description="Analyze scores by department"
)

if __name__ == "__main__":
    demo.launch()

# Save the code above in app_gradio.py and then run
python app_gradio.py

Visualization:

Comparison of functionalities

Both Streamlit and Gradio offer similar functionalities for building ML/AI apps.

Here’s a table comparing some key aspects:

Feature	Streamlit	Gradio
Ease of Use	Beginner-friendly, intuitive API	Extremely easy to use, focused on rapid prototyping
Customization	Highly customizable with extensive options	More limited customization compared to Streamlit
Deployment	Various deployment options, including Streamlit Community Cloud and Docker	Flexible deployment options, including Hugging Face Spaces
Focus	Building interactive dashboards and data-centric applications	Creating machine learning demos and web applications

Community and support

When choosing a framework, it’s essential to consider the community and support available.

Streamlit:

• Streamlit has a large and active community of developers and data scientists;
• Users can connect with other app creators, ask questions, and share their latest apps through the community forum;
• Streamlit provides extensive documentation, tutorials, and examples to help users get started and learn the library’s functionalities;
• Snowflake provides support for Streamlit Community Cloud, although it is limited to bugs, access, and a few other things.

Gradio:

• Gradio have a supportive community with active forums and resources;
• The community offers tutorials, examples, and a platform for users to seek help and share insights;
• Gradio provides good documentation and actively encourages community feedback on its development through platforms like GitHub;
• Gradio has a dedicated page for community discussions and support.

Choosing the right framework

The choice between Streamlit and Gradio depends on your specific needs and priorities (as always).

While both tools are powerful, Streamlit excels in advanced customization and interactive dashboards for complex projects, while Gradio prioritizes ease of use and rapid development for simpler applications.

Choose Streamlit if:

• You need a highly customizable framework for building interactive dashboards and data-centric applications;
• You require a wide range of deployment options and strong community support;
• You prefer a more mature and feature-rich library.

Choose Gradio if:

• You want to quickly create and share AI demos with minimal effort;
• You need seamless integration with Hugging Face and other machine learning libraries;
• You prioritize ease of use and rapid prototyping over extensive customization.

Gradio’s focus on streaming use cases and its potential for real-time applications like object detection and speech recognition make it a strong contender for projects requiring these capabilities.

One notable difference between the frameworks lies in their approach to handling user input updates.

Streamlit employs a reactive programming model where any change in user inputs automatically triggers a rerun of the entire script, immediately updating all downstream components. This creates a fluid, interactive experience but can lead to higher computational overhead for complex applications.

Gradio, by default, takes a more conservative approach – updates occur only when users explicitly click the Submit button, giving developers more control over when computations are performed. However, Gradio does offer a live=True parameter that can be added to components to mimic Streamlit’s immediate update behavior, though this should be used judiciously depending on the computational intensity of your application.

Conclusion

Both Streamlit and Gradio are valuable tools for building and sharing machine learning applications.

Streamlit shines with its advanced customization options and its ability to create visually appealing and interactive dashboards, making it good for complex projects that require a high degree of flexibility and a polished user interface.

On the other hand, Gradio excels in its simplicity and ease of use, allowing for prototyping and the creation of user-friendly interfaces with minimal code. This makes it ideal for quickly demonstrating ML models and building simpler applications.

Ultimately, the best choice depends on your specific needs and priorities. Consider the complexity of your project, the level of customization required, the importance of real-time functionalities, and the strength of community support when making your decision.