How To Build a UI for LLM with Gradio

Large Language Models are powerful, but without a usable interface, their value stays locked behind code. I’m writing this guide for builders who want to move faster, from model output to something users can actually interact with.

This article focuses on using Gradio to create practical, lightweight UIs for LLMs, helping bridge the gap between experimentation and real usage without introducing unnecessary frontend complexity.

Importance of User Interfaces for LLMs

LLMs are powerful, but without a clear interface, their capabilities remain inaccessible to many users. A well-designed UI reduces cognitive load, clarifies intent, and enables meaningful interaction without exposing underlying model complexity. This is where user interfaces become crucial. A well-designed UI serves several important purposes:

1. Accessibility: Interfaces allow non-technical users to interact with LLMs directly, expanding adoption beyond engineering teams.

2. Ease of use: A good interface simplifies the interaction process, allowing users to focus on their tasks rather than the technicalities of the model.

3. Rapid prototyping: For developers and researchers, a quick UI can help in testing and iterating ideas faster.

4. Demonstration: UIs provide an effective way to showcase the capabilities of LLMs to stakeholders or potential users. For instance, check out our AI POC collection where we've built intuitive interfaces for healthcare, finance, and legal document processing systems.

What is Gradio

Gradio is a Python library that enables rapid creation of web-based interfaces for machine learning models. Its strength lies in minimizing UI boilerplate while preserving flexibility across different model types and interaction patterns. It offers several advantages:

1. Simplicity: With just a few lines of code, you can create a functional Gradio interface for your LLM.

2. Flexibility: Gradio supports various input and output types, making it suitable for different kinds of LLM applications.

3. Customization: While simple to use, Gradio also offers options for more advanced customization of the UI.

4. Sharing: Gradio makes it easy to share your LLM demo with others, either by generating a public link or by embedding it in a webpage.

In this blog, we will explore how to use Gradio to create a simple yet effective UI for an LLM.

A Step-by-Step Guide to Creating a UI for LLM using Gradio

A. Installing Gradio

Gradio installation assumes a working Python environment, ensuring the focus remains on interface logic rather than setup complexity on our system. Download the latest version from the official Python website. Once installed, open the terminal or command prompt and run the following command to install Gradio.

pip install gradio

B. Importing Gradio and other required libraries

Importing Gradio alongside model and framework dependencies establishes a clean separation between UI logic and inference logic.

import gradio as gr
import torch
from transformers import pipeline

Here, we’re importing:

• “Gradio” for creating the interactive demo
• “torch” for working with PyTorch models
• “transformers” for loading pre-trained LLMs and tokenizers

C. Defining the LLM function

The LLM function acts as the interface contract, transforming user input into model output. Keeping this function focused simplifies debugging and UI iteration. For this example, we’ll use the Hugging Face ‘google/gemma-2-2b’ model

def llm_generate(input):
pipe = pipeline(
      "text-generation",
       model="google/gemma-2-2b",
       device="cuda",  
	)
	outputs = pipe(input, max_new_tokens=256)
	response = outputs[0]["generated_text"]
	return response

D. Creating a Gradio interface

The Gradio interface binds user input components directly to the LLM function, enabling real-time interaction without additional frontend frameworks.

demo = gr.Interface(
    fn=llm_generate,
    inputs=gr.Text(),
    outputs=gr.Text(),
    title="Large Language Model Demo",
    description="Enter a sentence or paragraph to generate a response",
)

Here, we’re creating a Gradio interface with:

• ‘fn’: the LLM function we defined earlier
• ‘inputs’: a text input field for the user to enter their prompt
• ‘outputs’: a text field to display the generated output
• ‘title’ and ‘description’: Customizing the interface with a title and description

E. Launching the demo

Finally, launch the demo by running the following command:

demo.launch()

Launching the demo spins up a lightweight local server, making it easy to test and iterate before sharing or deploying externally, and we can access the demo by navigating to ‘http://localhost:7860’ in our web browser.

If you'd like to share your app with others, simply include `share=True` in the launch function. This will create a public link that you can share with anyone. The recipient can then open the link in their browser and try out the model directly.

demo.launch(share=True)

A shareable link typically appears like this: https://07ff8706ab.gradio.live. 

SCREENSHOT :

Customization Options

A. Modifying Input and Output Types

Gradio supports multiple input and output formats, allowing the UI to adapt to different LLM capabilities without changing core logic, including text, images, audio, and more. We can modify the input and output formats to match the specific requirements of our LLM demo.

Input Types

There are many input formats available. Now let’s discuss only a few types of input

• ‘text’: a text input field
• ‘image’: an image upload field
• ‘audio’: an audio upload field
• ‘video’: a video upload field
• ‘number’: a numeric input field
• ‘select’: a dropdown select input field

Output Types

• ‘text’: a text output field
• ‘image’: an image output field
• ‘audio’: an audio output field
• ‘html’: an HTML output field
• ‘JSON’: a JSON output field

To modify the input and output formats, simply update the ‘inputs’ and ‘outputs’ parameters in our Gradio interface.

B. Adjusting the UI Layout

Layout controls in Gradio help structure interactions clearly, improving usability without requiring custom frontend development of our demo. We can adjust the layout to better suit our needs and make our demo more user-friendly.

Layout Options

• vertical: a vertical layout with input fields and output fields stacked on top of each other
• horizontal: a horizontal layout with input fields and output fields side by side
• tabbed: a tabbed layout with input fields and output fields organized into separate tabs

C. Adding Additional Features

Gradio provides several additional features that we can use to enhance our demo, including examples, flags, and more.

Examples

Gradio examples are predefined inputs that users can quickly select to see how the model or function responds. These examples are usually displayed as buttons, and when clicked, they automatically populate the input fields, making it easier to demonstrate the capabilities of the app.

demo = gr.Interface(
    fn=llm_generate,
    inputs=gr.Text(),
    outputs=gr.Text(),
    title="Large Language Model Demo",
    examples=["What is AI","What is ML"]
    description="Enter a sentence or paragraph to generate a response",
)

Suggested Reads- How To Use Open Source LLMs

Benefits of using Gradio

A. Rapid Prototyping

Rapid Prototyping: Gradio reduces time-to-demo, allowing faster validation of LLM behavior and interaction design, allowing us to quickly test and validate our ideas without investing a lot of time and resources. With Gradio, we can create a demo in just a few lines of code, making it an ideal tool for researchers and developers.

• Faster iteration:  Rapid prototyping enables us to quickly test and refine our ideas, reducing the time and effort required to develop a working prototype.
• Improved collaboration: Gradio demos can be easily shared with colleagues and stakeholders, facilitating collaboration and feedback.
• Reduced costs: By prototyping quickly, we can avoid investing in expensive infrastructure or development costs.

B. Easy Sharing and Deployment

Easy Sharing & Deployment: Gradio enables quick sharing of demos, making collaboration and feedback loops shorter and more effective, allowing us to showcase our work to a wider audience. With Gradio, we can deploy our demo to a variety of platforms, including web servers, cloud services, and even mobile devices.

• Broader Reach: Gradio demos can be easily shared with anyone, regardless of their technical expertise, making it easier to showcase our work to a wider audience.
• Increased Adoption: By making it easy to deploy and share demos, Gradio encourages the adoption of AI models and technologies.

C. Versatility for Different AI Models

Model Versatility: Gradio supports multiple AI modalities, making it suitable for experimentation across text, vision, and audio models, including LLMs, computer vision models, etc. This versatility makes Gradio an ideal tool for developers and researchers working with AI models, regardless of their specific application or domain.

• Flexibility: Gradio's versatility enables us to work with a wide range of AI models, making it easier to experiment with new ideas and applications.
• Interoperability: Gradio demos can be easily integrated with other tools and platforms, facilitating collaboration and innovation.
• Future Spoofing: By supporting a wide range of AI models, Gradio ensures that our demos remain relevant and effective, even when new models and technologies emerge.

Conclusion

Gradio provides a practical way to expose LLM capabilities through simple, interactive interfaces. By focusing on usability and speed, it enables faster experimentation, clearer demonstrations, and smoother adoption of language models, and customizing them to suit our needs. We started by installing Gradio and created a basic demo for an LLM. We explored various customization options, including modifying input and output formats, adjusting the UI layout, and adding additional features. We also discussed the advantages of using Gradio, including rapid prototyping, easy sharing and deployment, and versatility for different AI models.

As LLMs continue to evolve, we can expect to see new and innovative interfaces emerge. Some potential future possibilities include

• Multimodal interfaces
• Conversational interfaces
• Explainable AI interfaces

Frequently Asked Questions