How to use

Make sure you have Streamlit and PyTorch installed.

If not, simply use: pip install streamlit pip install torch

In addition, to download and work with the model, we are using the Transformers package from the great people at https://huggingface.co 🤗. pip install transformers

If you want to build upon this code and are using VS Code, like I do, you can use the configuration inside the launch.json in this repository to use the VS Code debugger to debug your streamlit apps.

To run this app, use: streamlit run YOUR_APP_NAME.py from your app folder in your terminal

Understanding Google's ViT Model

Google's ViT (Vision Transformer) is a sophisticated image understanding program. Imagine it as a bright student that excels at comprehending images. Let's break down how it works:

• Patches: Instead of looking at an entire image all at once, ViT divides it into small parts called patches. Think of these as mini-picture snippets, like breaking a jigsaw puzzle into its pieces.

• Learning from Patches: Similar to how you learn from your lessons, ViT learns from these patches. It studies each patch to understand what's in it. For instance, one patch might contain a portion of a tree, while another holds a fragment of a car.

• Assembling the Puzzle: After learning from the patches, ViT organizes them in a sequence to make sense of the bigger picture. This is like reassembling the puzzle pieces to reveal the complete image. Once ViT knows what each patch represents, it can make an educated guess about the entire image—just like you can predict what a puzzle portrays as you fit most of the pieces together.

• Building Memory: ViT's cleverness doesn't end with patches. It builds a memory of what it learned from one image. This memory helps ViT understand other images more effectively. It's akin to you learning math skills and using them to solve various math problems.

In a nutshell, Google's ViT is comparable to an intelligent student. It learns from small image parts (patches), constructs a memory of its learnings, and then employs that memory to comprehend larger images. It's like a master puzzle solver, but for images!

Understanding Streamlit

Streamlit is a tool that makes creating web applications as easy as writing Python code. Imagine it as a magic wand for turning your data scripts into interactive apps without much hassle.

Here's how it works:

• Write Python Code: If you know Python, you're already halfway there. You write your code just like you normally would, but with a twist. Instead of printing stuff in the console, you use Streamlit's functions to show things like graphs, tables, and text on a web page.

• Instant Interactivity: The cool part? As soon as you make changes to your code and save, Streamlit immediately updates the web app. No need to deal with complicated web development stuff. You focus on your code, and Streamlit takes care of the rest.

• Data Comes to Life: Let's say you've got data that you want to share with others. Instead of sending them static charts or asking them to run your code, you create a Streamlit app. They can interact with your data, change parameters, and see results in real time—all through a web browser.

In simple words, Streamlit turns your Python scripts into live web apps that people can use without needing to be coding pros. It's like having a superpower to share your data stories with the world!