Streamlit vs Gradio: A Guide to Building Interactive ML Apps in Python

Machine learning is becoming increasingly ubiquitous, with applications spanning across industries from healthcare to finance to e-commerce. As ML models grow more powerful and complex, there is a growing need for easy-to-use tools that allow data scientists and ML engineers to quickly build interactive web apps to showcase their work and collaborate with others. Two of the most popular open-source Python libraries for building ML web apps are Streamlit and Gradio.

In this article, we‘ll provide an in-depth comparison of Streamlit vs Gradio, exploring the strengths and weaknesses of each framework. We‘ll dive into their architectures, discuss code examples, and provide recommendations on when to use each library based on your specific needs and requirements. Whether you‘re a beginner looking to get started with building ML web apps, or an experienced practitioner evaluating tools, this guide will help you make an informed decision.

What are Streamlit and Gradio?

Before we compare the two frameworks, let‘s first define what Streamlit and Gradio are and what they are commonly used for.

Streamlit

Streamlit is an open-source Python library that allows you to quickly build interactive web-based data apps and machine learning tools. It is designed to be easy to use, even for those with limited web development experience. With Streamlit, you can create beautiful, customizable apps with just a few lines of Python code. Some common use cases for Streamlit include:

• Building dashboards to visualize and explore datasets
• Creating web-based tools for data annotation and labeling
• Showcasing interactive demos of machine learning models
• Rapid prototyping of data and ML applications

One of the key advantages of Streamlit is its simplicity. You can have a working app up and running in minutes, without having to worry about HTML, CSS, or Javascript. Streamlit takes care of all the web development complexity behind the scenes, allowing you to focus on your Python code and data.

Gradio

Gradio is another open-source Python library for building web-based machine learning interfaces and demos. It allows you to create a UI for your ML models, making them accessible to non-technical users through an easy-to-use web interface. With Gradio, you can quickly prototype and share your ML models without having to build a web app from scratch.

Some common applications of Gradio include:

• Building demos of ML models for sharing and collaboration
• Creating human-in-the-loop interfaces for ML models
• Debugging and testing ML models interactively
• Deploying ML models as web-based tools for end users

Like Streamlit, Gradio aims to simplify the process of building web interfaces for ML. However, it has a more narrow focus on ML models specifically, whereas Streamlit is a more general purpose tool for building data apps.

Comparing Streamlit and Gradio

Now that we understand what Streamlit and Gradio are, let‘s compare them across a few key dimensions:

Ease of Use and Learning Curve

Both Streamlit and Gradio are designed with ease of use in mind. They allow you to create web apps using pure Python code, without requiring knowledge of front-end technologies like HTML, CSS, and Javascript.

Streamlit has a very simple and intuitive API. You can add UI elements like sliders, buttons, and text inputs with single line commands. Displaying data is also straightforward – you can render dataframes, charts, and more with simple functions. The Streamlit documentation is comprehensive, with tutorials and examples for beginners and more advanced topics like caching for experts.

Gradio has a more declarative, annotation-based style where you define your interface using Python function decorators. This can make the code more concise, but may have a slightly steeper learning curve than Streamlit‘s imperative API at first. However, Gradio‘s documentation and examples are also quite thorough and the usage becomes intuitive after getting over the initial hump.

Overall, both Streamlit and Gradio have developer-friendly APIs that allow you to build apps quickly. Streamlit may have a slight edge in terms of simplicity and beginner-friendliness, but the difference is not huge.

Customization and Flexibility

When it comes to customization, Streamlit offers a lot more options and flexibility compared to Gradio. Streamlit provides a variety of built-in themes and lets you tweak the look and feel of every element in your app. You can easily modify things like fonts, colors, sizing and more. Streamlit also gives you the ability to inject raw HTML, CSS, and JS when needed for more advanced customization.

In contrast, Gradio is more opinionated and has a relatively fixed interface without too much visual customization available. It‘s great if you want to build something quickly that looks polished out of the box, but not ideal if you need a lot of fine-grained control over the app‘s appearance. The upside is that Gradio‘s default aesthetic is very slick and modern.

So in summary, Streamlit is the clear winner if customizability is a priority, while Gradio is better suited for projects where you are happy with a pre-styled, standard look and feel.

Performance and Scalability

Both Streamlit and Gradio use Tornado as the web server and WebSockets for bidirectional communication between the web app and Python backend. However, there are some differences in their architectures that impact performance.

Streamlit‘s architecture is designed for interactivity and fast feedback loops. Whenever a user interacts with a widget, the entire Python script is re-executed quickly to update the output. Streamlit also has built-in caching and memoization primitives that help optimize performance by avoiding redundant computation. Serving a Streamlit app is simple as it is bundled as a single Python script.

Gradio‘s architecture is a bit different and geared more towards ML inference workloads. In a Gradio app, you define a function that takes in user input, runs it through an ML model, and returns the output. Gradio then handles serving this function through a web interface, taking care of aspects like serialization, setting up API endpoints, and creating the input and output UI components. One downside is that the initial startup of a Gradio app can be slow, as the ML model needs to be loaded into memory.

In terms of scalability, Streamlit allows you to build highly interactive single-page apps that can handle a lot of user interaction smoothly. But as the app gets very complex, you may start observing slowdowns since the entire app is re-executed on every interaction. Gradio is better optimized for handling high volumes of inference requests as the ML model is kept in memory and requests are served efficiently. So Gradio may be more suitable for deploying production-scale ML web services.

Community and Ecosystem

Both Streamlit and Gradio have active and growing communities and ecosystems around them. Let‘s compare them:

Streamlit has been around longer and has a larger user base and community. It has over 22k stars on Github and an active community forum with over 6k members. There are also many community-contributed extensions and components available in the Streamlit ecosystem that enhance its functionality. Streamlit has been widely adopted by major companies like 7-Eleven, Ford, Uber and more.

Gradio is younger and has a smaller but rapidly expanding community. It has around 10k stars on Github currently. While the community is not as large as Streamlit‘s yet, it is very active and responsive on platforms like Twitter and Discord. Gradio has been gaining a lot of traction, with adoption from well known organizations like Hugging Face and fast growth in usage and contributors recently.

Both Streamlit and Gradio have well-maintained open-source codebases and regular new releases with enhancements and bug fixes. They also have extensive documentation and tutorials to help users get started.

Overall, Streamlit has an edge over Gradio in terms of community size and maturity, but both have healthy ecosystems that are actively evolving. As of 2024, Streamlit‘s community continues to grow steadily while Gradio is catching up quickly.

Comparing Streamlit and Gradio Code

To illustrate the differences between Streamlit and Gradio‘s development styles, let‘s walk through a simple example of building an app that takes in user input, runs it through an ML model, and displays the output. We‘ll use a text classification model that predicts the sentiment (positive or negative) of movie reviews.

Streamlit Version

Here‘s what the Streamlit version of the app would look like:

import streamlit as st
from transformers import pipeline

# Load model
classifier = pipeline(‘sentiment-analysis‘)

# Set page config and title
st.set_page_config(page_title="Sentiment Analyzer")
st.title("Movie Review Sentiment Analyzer")

# Add text input
review = st.text_area("Enter a movie review")

# Run inference if button is clicked 
if st.button("Analyze Sentiment"):
    result = classifier(review)[0]
    sentiment = "Positive" if result[‘label‘] == ‘POSITIVE‘ else "Negative"
    score = result[‘score‘]

    # Display output
    st.subheader(f"Predicted Sentiment: {sentiment}")
    st.write(f"Confidence: {score:.2f}")

The Streamlit code follows an imperative style where we add UI elements one by one and control the app flow with if-else statements. We load the sentiment analysis model, display the title and text input box, and use a button to trigger running the model inference. The predicted sentiment and confidence score are then displayed using header and text elements.

Gradio Version

Here‘s how you would build the same app in Gradio:

import gradio as gr
from transformers import pipeline

# Load model
classifier = pipeline(‘sentiment-analysis‘)

# Define prediction function
def predict(review):
    result = classifier(review)[0]  
    sentiment = "Positive" if result[‘label‘] == ‘POSITIVE‘ else "Negative"
    score = result[‘score‘]
    return sentiment, score

# Define input and output components    
review = gr.Textbox(label="Enter a movie review")
sentiment = gr.Textbox(label="Predicted Sentiment")  
confidence = gr.Number(label="Confidence")

# Create and launch interface
demo = gr.Interface(
    fn=predict, 
    inputs=review,
    outputs=[sentiment, confidence],
    title="Movie Review Sentiment Analyzer"    
)
demo.launch()

With Gradio, we first define a predict function that contains the core model inference logic. We then specify the input component (Textbox) and output components (Textbox and Number) along with their labels. Finally, we create a Gradio Interface by passing the predict function, inputs, and outputs. Launching the demo will automatically create a web app with the specified input box, run the predict function whenever user input is received, and display the returned outputs.

As you can see, the Gradio code is quite concise as a lot of the web app details are abstracted away. We only need to focus on the ML-specific logic and Gradio takes care of generating the UI and handling the client-server interaction.

When to Use Streamlit vs Gradio

So when should you use Streamlit vs Gradio for your project? Here are some recommendations:

Choose Streamlit if:

• You want to build general purpose data apps, not just ML model demos
• You need a lot of flexibility to customize the look and feel of the app
• Your app is complex and needs added performance optimizations like caching
• You want access to a large community and ecosystem of extensions

Choose Gradio if:

• Your primary goal is to demo an ML model in a web UI quickly
• You want a polished visual aesthetic without having to customize CSS
• You plan to deploy the model in production and serve predictions at scale
• You prefer a concise, annotation-based API style

Of course, the lines are blurry and you can build any type of app with either framework. It ultimately depends on your specific requirements and preferences.

Streamlit and Gradio in 2024 and Beyond

Over the past few years, both Streamlit and Gradio have been releasing regular updates with new features and enhancements. Here are some notable recent updates as of 2024:

Streamlit 2.0

In early 2024, Streamlit released version 2.0 with significant new features, including:

• Streamlit Components: A full-fledged framework for building, distributing, and consuming custom Streamlit UI widgets
• Streamlit Cloud Autopilot: An automated way to deploy, scale, and monitor Streamlit apps in a hosted cloud environment
• Session State Enhancements: Improvements to Streamlit‘s session state primitives for building complex stateful apps
• App-wide Configuration: Ability to set default settings for an entire app, like color theme and page layout

These updates make Streamlit an even more powerful and flexible tool for building data apps of all kinds.

Gradio 3.5

Gradio also had a major new release in late 2023 with version 3.5. Highlights include:

• Blocks: Gradio Blocks is a new API that enables more modular and customizable UI layouts and components
• Theme Support: Ability to customize the color schemes and visual themes of Gradio apps
• Hosted Spaces: A new hosting service to deploy and share Gradio apps easily, integrated with Hugging Face Spaces
• Improved Performance: Optimized app startup times and resource usage, especially for large ML models

With these updates, Gradio continues to get more flexible and scalable, while still retaining its core focus on fast, no-code ML app building.

Conclusion

Streamlit and Gradio are both excellent tools for rapidly building interactive web apps in Python, especially for data science and machine learning projects. While they share similarities, they each have their own strengths. Streamlit is a great choice for general purpose data apps where customization is important, while Gradio excels at quickly deploying ML model demos and scaling to production.

Ultimately, the best choice depends on your specific needs and use case. But with their ease of use, active development, and growing communities, you can‘t go wrong with either Streamlit or Gradio for your next ML web app project. The landscape of data app building tools is constantly evolving, and these two frameworks are leading the pack. We‘re excited to see what new capabilities future versions will bring.