Efficient Document Data Extraction with Sparrow UI: Streamlit, FastAPI, and Hugging Face's Donut ML

In this easy-to-follow video, I show you how I built Sparrow UI, a tool for pulling data from documents using Streamlit. With Sparrow UI, you can upload a document and quickly run a data extraction task. I'll walk you through how the system works, using a FastAPI app on the backend to run a fine-tuned Donut ML model from Hugging Face. I'll also explain the code that sends POST requests from the Streamlit app, including how it sends files and text to the FastAPI endpoint. This way, you'll get a JSON response with the extracted info from your document. 

 

Deploying FastAPI Applications to Hugging Face Spaces

In this video, I demonstrate how to deploy a FastAPI backend API to Hugging Face Spaces using Docker. I cover creating a Dockerfile, setting up secrets for FastAPI, and deploying the application on the platform.

     

Build a Structured API with FastAPI

Learn how to create a structured API using FastAPI. In this tutorial, we explore the benefits of FastAPI, its powerful code structuring capabilities, and how it connects services within Sparrow to build scalable and efficient applications. Unleash the true potential of FastAPI and enhance your app development process! 

 

Donut ML Model Fine-Tuning with Hugging Face API

I explain how Donut ML model can be fine-tuned on your own dataset by following different approaches. Either with PyTorch Lighting or Hugging Face Trainer API. I explain the pros and cons of both and what works best for me.

 

How I'm Using ChatGPT/GPT-4 as a Solo Python Developer

I'm working as a solo Python developer and using ChatGPT to speed up the development process. In this video, I explain how ChatGPT is helping me with various tasks, from code explanation to suggesting solutions.

 

Hugging Face Dataset for Donut Model Fine-Tuning (Document AI)

Hugging Face Dataset is a very convenient way to store and share data for ML model fine-tuning. In this post, I share my experience creating a dataset for fine-tuning the Donut model. I made a set of scripts to generate the dataset, push it to the Hub and test it locally.

 

Improve OCR Results with Sparrow (running on Streamlit/Python and Ngrok)

OCR can often generate results in a different order. But to produce a dataset for data extraction ML model fine-tuning (for example - Donut), fields in all documents must be ordered correctly. Our solution (open-source), Sparrow, for data annotation/labeling includes functionality for OCRed field reordering. In this video, I explain and show how it works.