README.md

Model Deployment Tutorial

In this tutorial, we'll explore how to deploy pretrained models using Streamlit, Django, and Flask. This tutorial is designed for first-year students and aims to provide a basic understanding of networking concepts such as requests, responses, JSON, APIs, and the Fetch API in Python.

Objectives:

• Understand fundamental networking concepts.
• Learn to build APIs and endpoints using Flask and Django.
• Create a website for model deployment using Streamlit.

Setting up the environment

First install virtualenv by running :

pip install virtualenv

Then, create a virtual enviroment:

virtualenv env

After virtual env is created. Then, following command to activate it:

source ./env/bin/activate

Then, install the requirements:

pip install -r requirements.txt

Flask

A basic flask app

# app.py

from flask import Flask

app = Flask(__name__)


@app.route("/", methods=["GET"])
def hello_word():
    return "Hello, World"


if __name__ == "__main__":
    app.run(debug=True)

In order to run the flask app, run the following command

python3 app.py # app.py file name

TODO:

• Materials About, basic concepts of Networking

  • API, HTTP, JSON, HTTP Methods (GET, POST, ..,) here
  • apply these concepts in Flask.
  • Build a simple TODO APP.

• Fetch APIs:

  • Looking for available APIs (ex. Github API)
  • Materials About: using the requests library, fetch website & APIs.

• ML Models:

  • Looking for pre-trained models, to use in this tuto.
  • Work with different data type: text, images, json ,etc.