Fifi 
The usefulness of machine learning models directly depends on the interfaces built to interact with them. In today’s AI-driven world, rapidly prototyping and sharing machine learning applications is a vital skill, especially for data scientists looking to bridge the gap between development and deployment.
Two of the most popular tools for creating quick, interactive web applications for ML models are Streamlit and Gradio. Both frameworks make it easy to turn Python scripts into shareable apps without writing frontend code. But which one should you use?
In this guide, we will explore the features, strengths, and differences between Streamlit and Gradio to help you decide which is the best fit for your workflow. Whether you are building your first ML app or you are deep into a Data Scientist Course, this comparison will set you on the right path.
What Are Streamlit and Gradio?
Streamlit
Streamlit is an open-source Python library that allows you to create interactive web apps for data science and machine learning projects. With a few lines of code, you can turn a Python script into a dynamic app that lets users interact with your data and models through sliders, buttons, file uploads, and more.
Gradio
Gradio is another open-source Python library designed specifically to build machine-learning model interfaces. What sets Gradio apart is its simplicity in showcasing ML models through clean input/output components such as images, text, audio, and more. Gradio was built with ML in mind, making it easy to demo models without diving deep into web development.
If you are enrolled in a reputed data course, for instance, a Data Scientist Course in Pune and such cities, you are likely to encounter both tools as part of your curriculum for model deployment and UI building.
Installation and Getting Started
Both libraries are easy to install and get running:
- pip install streamlit
- pip install gradio
To launch a Streamlit app:
streamlit run app.py
To launch a Gradio app:
import gradio as gr
def greet(name):
return f”Hello {name}!”
gr.Interface(fn=greet, inputs=”text”, outputs=”text”).launch()
Gradio apps typically take less boilerplate to get started, especially for simple ML demos, whereas Streamlit provides a more flexible layout system.
Use Cases and Flexibility
Streamlit
- Streamlit is best for data dashboards, exploratory data analysis tools, and ML model visualisations. It gives you more control over layout and interactivity, allowing you to build rich applications that go beyond just model input/output.
- Features like session state, layout control (columns, expanders), and media support make Streamlit ideal for apps that require UI customisation.
Gradio
- Gradio shines when your main goal is to demo a model. It abstracts away layout details so you can focus on inputs and outputs. Its interface is structured around your ML model: you define a function, set the input and output types, and Gradio handles the rest.
- This makes it perfect for quick testing, especially when showcasing computer vision, audio, or text models in a Data Scientist Course project.
Customisation and UI Control
When it comes to UI customisation:
- Streamlit gives you more freedom to control how things look. You can use Markdown, write custom CSS via components, and structure complex layouts with st.columns(), st.sidebar(), and more.
- Gradio offers a minimal and consistent UI. You can control some aspects, like labels and examples, but the design is more restrictive to keep things simple.
If your ML app requires a tailored user interface or data visualisations with charts and plots (for example, via Plotly or Altair), Streamlit is the better choice.
Deployment Options
Both frameworks make it easy to share apps:
- Streamlit Cloud lets you deploy apps directly from a GitHub repo.
- Gradio apps can be shared via a public link instantly upon running .launch(share=True).
- Gradio has built-in Hugging Face integration, making deploying models trained in Hugging Face’s ecosystem incredibly simple. This is especially useful for NLP models, which are often covered in the advanced modules of a professional-level data course; for example, in those of a Data Scientist Course in Pune.
For more advanced deployment, both tools support Docker, cloud hosting (like AWS, GCP, Azure), and CI/CD pipelines.
Integration with ML Libraries
- Gradio is tightly integrated with libraries like Transformers, TensorFlow, Keras, and PyTorch. It supports features like model introspection and visualisations out of the box, which is perfect for sharing models trained during a Data Scientist Course.
- Streamlit integrates well with data-focused libraries like pandas, NumPy, matplotlib, Plotly, and Altair, making it excellent for building dashboards or exploring datasets interactively.
- Gradio’s ML-first design makes it better suited for tasks like image classification demos, while Streamlit is more flexible for data exploration or multi-step model workflows.
Community and Ecosystem
- Streamlit has a rapidly growing community and a wide ecosystem of components, including custom plugins and integrations.
- Gradio benefits from strong community support, especially after becoming part of Hugging Face.
Both tools have great documentation and active GitHub repositories. Many data course program instructors now use both platforms to help students quickly prototype and present their ML models. For example, in a Data Science Course in Pune, both platforms are often covered in equal detail.
Performance and Scalability
For individual use or lightweight demos, both tools perform well. However:
- Streamlit is better suited for multi-user applications that need session handling or caching of expensive computations.
- Gradio is ideal for showcasing models with fixed input/output behaviour but might require additional backend work for stateful apps or complex interactions.
For production-scale apps, you will likely wrap either tool in a backend server or containerise it, so the performance gap becomes negligible.
Security and Privacy
- Streamlit offers more backend flexibility, allowing you to integrate authentication and secure APIs.
- Gradio includes options to disable sharing links and anonymise user inputs. Still, for advanced use cases, you might need to deploy behind your firewall or in a secure cloud environment.
Both tools are sufficient for educational and prototyping purposes. However, if you are building enterprise-grade apps, additional layers of security will be needed.
Which One Should You Choose?
Choose Streamlit if:
- You want a fully customisable dashboard or data app.
- You need to build multi-page, complex UIs.
- Your use case involves a lot of visualisations or user input customisation.
Choose Gradio if:
- Your main goal is to demo a model with minimal code.
- You are working with image, text, or audio models.
- You want instant sharing and Hugging Face integration.
In many cases, the right answer is to use both, depending on the stage of your project. For example, you might use Gradio for internal model testing and Streamlit for the final presentation or data exploration.
For Data Scientist Students, practising with both tools offers valuable hands-on experience. Prototyping and sharing ML apps quickly is a high-demand skill, and mastering both Streamlit and Gradio makes you a more versatile practitioner.
Conclusion
Both Streamlit and Gradio are excellent tools for building machine learning applications without needing a web development background. Streamlit gives you the power to build highly customisable and interactive data apps, while Gradio offers simplicity and speed for model demos.
In summary, what is best for you will depend on your goals, your app’s complexity, and the user experience you want to deliver. Whether you are a beginner or a pro sharpening your skills in a Data Science Course, understanding how to leverage these tools will dramatically speed up your ability to create and share impactful ML applications.
Business Name: ExcelR – Data Science, Data Analyst Course Training
Address: 1st Floor, East Court Phoenix Market City, F-02, Clover Park, Viman Nagar, Pune, Maharashtra 411014
Phone Number: 096997 53213
Email Id: [email protected]
