Building a Simple Web App with Hugging Face and Streamlit
Web App with Hugging Face and Streamlit
Introduction
Embark on a journey to create a straightforward web application leveraging the power of Hugging Face and Streamlit. Dive into the realm of natural language processing with ease. Join us in crafting a user-friendly interface that harnesses the capabilities of Hugging Faceβs transformative models and Streamlitβs seamless interactivity. Letβs explore the magic of combining these technologies to build an engaging and intuitive web experience.
Letβs kick things off! π₯
1. Installation and Setup
To start, install the Streamlit package via pip in your terminal:
pip install streamlit
Next, ensure you have Git installed. Follow the tutorial provided [here](https://github.com/git-guides/install-git) to set up Git through the command line.
2. Web App Code
Weβre diving into the creation of a web app that predicts the variety of an iris flower. For this tutorial, our focus is on deploying the app using Hugging Face, assuming the model training has been completed beforehand. Weβve trained a Logistic Regression model and saved itβ¦
