Llama 3.2 and Gradio Tutorial: Build a Multimodal Web App

Building the Llama 3.2 Multimodal App

Now that we have already set up our imports and have successfully set up our model, let’s move forward with the main part of the app—processing the inputs (text and image) and generating the response.

Text-to-text generation

We start by defining a function that takes in user text and, optionally, an image. This function then uses the Llama 3.2 model to generate a response.

def process_ticket(text, image=None):
    model, processor = load_model()
    
    try:
        if image:
            # Resize the image for consistency
            image = image.convert("RGB").resize((224, 224))
            prompt = f"<|image|><|begin_of_text|>{text}"
            # Process both the image and text input
            inputs = processor(images=[image], text=prompt, return_tensors="pt").to(model.device)
        else:
            prompt = f"<|begin_of_text|>{text}"
            # Process text-only input
            inputs = processor(text=prompt, return_tensors="pt").to(model.device)
        
        # Generate response (restrict token length for faster output)
        outputs = model.generate(**inputs, max_new_tokens=200)
        # Decode the response from tokens to text
        response = processor.decode(outputs[0], skip_special_tokens=True)
        return response
    
    except Exception as e:
        print(f"Error processing ticket: {e}")
        return "An error occurred while processing your request."

This function handles two types of input within the loop:

• Text-only: If no image is provided, the model generates a response based on the text input.
• Text + Image: If an image is provided, the model processes both the text and image before generating the response.

Once the input type is identified, it is passed to a processor sourced from the transformer library to process the input. Then, the model generates an output within the range of max_new_tokens.

Creating the Gradio Interface

The Gradio interface binds everything together and enables us to run tests in a web-based format. This interface allows users to submit text and images of an issue they are facing and see the AI-generated solution.

Let’s take a look at the code and then explain it.

def create_interface():
    text_input = gr.Textbox(
        label="Describe your issue",
        placeholder="Describe the problem you're experiencing",
        lines=4,
    )
    
    image_input = gr.Image(label="Upload a Screenshot (Optional)", type="pil")
    
    # Output element
    output = gr.Textbox(label="Suggested Solution", lines=5)
    
    # Create the Gradio interface
    interface = gr.Interface(
        fn=process_ticket,  # Function to process inputs
        inputs=[text_input, image_input],  # User inputs (text and image)
        outputs=output,  # AI-generated output
        title="Multimodal Customer Support Assistant",
        description="Submit a description of your issue, along with an optional screenshot, and get AI-powered suggestions.",
    )
    
    # Launch the interface with debug mode
    interface.launch(debug=True)

In the code above, we:

• Define two inputs:
• text_input for text
• image_input for image
• Specify an output box to display the response.
• Create the interface, passing parameters such as:
• Inputs
• Function to process input
• Generated output from the model
• Title of the interface
• Description (if required)
• Set up the basic user interface.
• Launch the interface with debug = True to debug the errors. Once the code works fine, switch it back to False.

The final interface will look like this:

Our multimodal customer support assistant Gradio application is ready! To get the desired response, try changing the max_new_tokens parameter or playing around with the prompt a bit.

Use cases for Llama 3.2 and Gradio

In addition to the demo we created in this tutorial, there are a few other use cases that require minimal effort. These include:

• Education and tutoring: Students can upload visual aids like graphs or diagrams alongside their questions, and the model can generate comprehensive responses incorporating both visual and textual information.
• Content creation: Improve the creation of captions, blog posts, and social media content by generating text based on images.
• Real estate virtual assistance: Assist agents and clients by processing property images and answering related questions. Generate property descriptions or analyze visual details from photos.

Best Practices for Developing with Llama 3.2 and Gradio

For every use case, there are a few tips that every developer can use while developing an app like the one we already built. Here are a few best practices which a developer can adopt while working with models like llama3.2.

Handling latency

Since multimodal tasks can be resource-intensive, reducing latency is key. Consider optimizing the model for faster responses by using caching, model pruning, or limiting the number of tokens generated.

Error handling

It’s important to put mechanisms in place to handle errors. In cases where the model fails to generate a meaningful response (e.g., due to poor image quality), we can provide fallback responses or error messages. We can even opt for human feedback, which, in return, helps to improve the model.

Performance monitoring

Tracking the app's performance, such as response times and user interaction data, can help optimize the interface and even improve the user experience. By noting performance time, we can try to optimize the model latency using libraries like bits and bytes.

Conclusion

In this guide, we learned how to combine Llama 3.2's multimodal capabilities and Gradio's intuitive interface. From customer support to education and content creation, the potential applications are vast and varied.

By adhering to best practices like latency management, error handling, and performance monitoring, we can ensure our Llama 3.2 and Gradio applications are robust, efficient, and user-friendly.

To learn more, I recommend these tutorials:

• Llama Stack: A Guide With Practical Examples
• Fine-tuning Llama 3.2 and Using It Locally
• User Interfaces For AI Applications with Gradio in Python