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How to Build a Simple Generative AI Application with Gradio
Latest   Machine Learning

How to Build a Simple Generative AI Application with Gradio

Last Updated on February 2, 2024 by Editorial Team

Author(s): Saqib Jan

Originally published on Towards AI.

Gradio is simply a great choice for creating a customizable user interface for machine learning models to test your proof of concept.

Image Source: Hugging Face blog

When you have a specific idea in mind, say summarizing an article, a small specialist model that is designed for that specific task can perform just as well as a general-purpose Large Language model. And interestingly, a smaller specialist model can also be cheaper and faster to run.

You can, if you want, create an application that aggregates news articles from various sources and provides summarized versions of the articles for quick browsing. Or, you could develop a plugin that integrates with email services and automatically summarizes long emails, allowing users to quickly grasp the main points without reading the entire message.

But how would you do that? I’ll show it in this brisk tutorial so that you can also give it a try. And the best part? It will not take hours but minutes.

Import libraries

import gradio as gr
from transformers import pipeline

Gradio is an open-source Python library, and you can accomplish a lot with it in minutes, like allowing users to input data, make predictions, and visualize results with just a few lines of code.

And we’re also importing the pipeline function from the Hugging Face Transformers library, which is very good for working with pre-trained transformer models in NLP.

Initialize a Summarization Pipeline

get_completion = pipeline("summarization", model="sshleifer/distilbart-cnn-12-6")

First off, we’re going to use sshleifer/distilbart-cnn-12–6 model for text summarization because it is one of the state-of-the-art models known for its exceptional performance and accuracy in generating concise summaries. Another factor is if we use the Transformers Pipeline function for text summarization without specifying the model explicitly, it will still default to Distilbart CNN 12–6.

Most interestingly, the effective way to improve cost and speed is to create a smaller version of the model that has a very similar performance. This process, called distillation, is quite common. Distillation involves using the predictions of a large model to train a smaller one. The model we’re using (Distilbart CNN 12–6) is actually a distilled version of the larger model trained by Facebook, known as the BART Large CNN model.

And since this model is built specifically for summarization, let’s write some functions for any text that you feed into the model so that it will output a summary of it.

Create a Summarization Function

def summarize(input_text):
# Generate the summary for the input text
output = get_completion(input_text)
# Extract and return the summary text
return output[0]['summary_text']

Now, we define a function summarize that takes input text as a parameter, generates a summary using the initialized summarization pipeline, and returns the summary text. This function simplifies usage and maintenance within the application.

Create Gradio Interface

gr.close_all()
demo = gr.Interface(
fn=summarize,
inputs=[gr.Textbox(label="Text to summarize", lines=6)],
outputs=[gr.Textbox(label="Result", lines=3)],
title="Text Summarization with DistilBART-CNN",
description="Summarize text using the `sshleifer/distilbart-cnn-12-6` model!"
)

Here, we set up the Gradio interface with input and output components, specify the summarization function, and provide a title and description to inform users about the interface’s functionality.

Now, let’s launch the interface so we can input text and receive summarized output using the DistilBART-CNN model.

demo.launch(share=True)

It will launch an interface like this.

Gradio running on the localhost

Now that our application is up and running, the Gradio interface is accessible both locally and via the live link provisioned by Gradio. And we are now ready to test.

Unfortunately, there was a tragic train accident in Odisha, a state in India, last year. And if we summarize the text of this BBC article about it, we can see the output it gives.

The model throws an error if your text exceeds 800 words. It's best to keep it between 700-800 words.

Gradio User interface

This works perfectly fine. You can do a lot of things if you have some experience in Python and use Gradio to build interfaces for your AI applications. Try this!

If you want to summarize books and papers, this advanced-level tutorial by Raghavan Muthuregunathan about How to Summarize and Find Similar ArXiv Articles on Lablab.ai is a very helpful resource on the internet.

Credits

I’d be remiss not to give credit for this oversimplified tutorial to Apolinário Passos (Poli), a Machine Learning Art Engineer at Hugging Face. His free short course on Deeplearning.ai with Andrew NG is an exhilarating resource for learning how to build AI-powered applications.

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} strongTag.remove(); }); }); } removeStrongFromHeadings(); "use strict"; window.onload = () => { /* //This is an object for each category of subjects and in that there are kewords and link to the keywods let keywordsAndLinks = { //you can add more categories and define their keywords and add a link ds: { keywords: [ //you can add more keywords here they are detected and replaced with achor tag automatically 'data science', 'Data science', 'Data Science', 'data Science', 'DATA SCIENCE', ], //we will replace the linktext with the keyword later on in the code //you can easily change links for each category here //(include class="ml-link" and linktext) link: 'linktext', }, ml: { keywords: [ //Add more keywords 'machine learning', 'Machine learning', 'Machine Learning', 'machine Learning', 'MACHINE LEARNING', ], //Change your article link (include class="ml-link" and linktext) link: 'linktext', }, ai: { keywords: [ 'artificial intelligence', 'Artificial intelligence', 'Artificial Intelligence', 'artificial Intelligence', 'ARTIFICIAL INTELLIGENCE', ], //Change your article link (include class="ml-link" and linktext) link: 'linktext', }, nl: { keywords: [ 'NLP', 'nlp', 'natural language processing', 'Natural Language Processing', 'NATURAL LANGUAGE PROCESSING', ], //Change your article link (include class="ml-link" and linktext) link: 'linktext', }, des: { keywords: [ 'data engineering services', 'Data Engineering Services', 'DATA ENGINEERING SERVICES', ], //Change your article link (include class="ml-link" and linktext) link: 'linktext', }, td: { keywords: [ 'training data', 'Training Data', 'training Data', 'TRAINING DATA', ], //Change your article link (include class="ml-link" and linktext) link: 'linktext', }, ias: { keywords: [ 'image annotation services', 'Image annotation services', 'image Annotation services', 'image annotation Services', 'Image Annotation Services', 'IMAGE ANNOTATION SERVICES', ], //Change your article link (include class="ml-link" and linktext) link: 'linktext', }, l: { keywords: [ 'labeling', 'labelling', ], //Change your article link (include class="ml-link" and linktext) link: 'linktext', }, pbp: { keywords: [ 'previous blog posts', 'previous blog post', 'latest', ], //Change your article link (include class="ml-link" and linktext) link: 'linktext', }, mlc: { keywords: [ 'machine learning course', 'machine learning class', ], //Change your article link (include class="ml-link" and linktext) link: 'linktext', }, }; //Articles to skip let articleIdsToSkip = ['post-2651', 'post-3414', 'post-3540']; //keyword with its related achortag is recieved here along with article id function searchAndReplace(keyword, anchorTag, articleId) { //selects the h3 h4 and p tags that are inside of the article let content = document.querySelector(`#${articleId} .entry-content`); //replaces the "linktext" in achor tag with the keyword that will be searched and replaced let newLink = anchorTag.replace('linktext', keyword); //regular expression to search keyword var re = new RegExp('(' + keyword + ')', 'g'); //this replaces the keywords in h3 h4 and p tags content with achor tag content.innerHTML = content.innerHTML.replace(re, newLink); } function articleFilter(keyword, anchorTag) { //gets all the articles var articles = document.querySelectorAll('article'); //if its zero or less then there are no articles if (articles.length > 0) { for (let x = 0; x < articles.length; x++) { //articles to skip is an array in which there are ids of articles which should not get effected //if the current article's id is also in that array then do not call search and replace with its data if (!articleIdsToSkip.includes(articles[x].id)) { //search and replace is called on articles which should get effected searchAndReplace(keyword, anchorTag, articles[x].id, key); 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} } } //arrays which will contain all the achor tags found with the class (ds-link, ml-link, ailink) in each article inserted using search and replace let dslinks; let mllinks; let ailinks; let nllinks; let deslinks; let tdlinks; let iaslinks; let llinks; let pbplinks; let mlclinks; const content = document.querySelectorAll('article'); //all articles content.forEach((c) => { //to skip the articles with specific ids if (!articleIdsToSkip.includes(c.id)) { //getting all the anchor tags in each article one by one dslinks = document.querySelectorAll(`#${c.id} .entry-content a.ds-link`); mllinks = document.querySelectorAll(`#${c.id} .entry-content a.ml-link`); ailinks = document.querySelectorAll(`#${c.id} .entry-content a.ai-link`); nllinks = document.querySelectorAll(`#${c.id} .entry-content a.ntrl-link`); deslinks = document.querySelectorAll(`#${c.id} .entry-content a.des-link`); tdlinks = document.querySelectorAll(`#${c.id} .entry-content a.td-link`); iaslinks = document.querySelectorAll(`#${c.id} .entry-content a.ias-link`); mlclinks = document.querySelectorAll(`#${c.id} .entry-content a.mlc-link`); llinks = document.querySelectorAll(`#${c.id} .entry-content a.l-link`); pbplinks = document.querySelectorAll(`#${c.id} .entry-content a.pbp-link`); //sending the anchor tags list of each article one by one to remove extra anchor tags removeLinks(dslinks); removeLinks(mllinks); removeLinks(ailinks); removeLinks(nllinks); removeLinks(deslinks); removeLinks(tdlinks); removeLinks(iaslinks); removeLinks(mlclinks); removeLinks(llinks); removeLinks(pbplinks); } }); } //To remove extra achor tags of each category (ds, ml, ai) and only have 2 of each category per article cleanLinks(); */ //Recommended Articles var ctaLinks = [ /* ' ' + '

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