junaidulhassan/human-protein-classifier

human-protein-classifier

Table of Content

• Demo
• Overview
• Directory Tree
• Installation
• Run
• To Do
• Bug / Feature RequestLLMOps
• Contribution
• Technologies Used
• Team
• Credits

Demo

Overview

This is a simple multi label image classification application developed using pytorch. The trained model can be downloaded and stored inside models folder with name model_resnet.pth that takes image as an input via file upload and then model predicts protiens present in the image.

Directory Tree

├── models
|    ├── model_resnet.pth
├── static
|    ├── css
|    ├── js
|    ├── images
├── templates
|    ├── base.html
|    ├── result.html
├── uploads
├── Dockerfile
├── Procfile
├── README.md
├── activation.bat
├── app.py
├── requirements.txt

Installation

1. Windows user can double click on activation.bat file to install required package
2. Linux User type following command in commnand line a) First create a virtual environment

python3.7 -m virtualenv venv

b) Move to venv directory and activate environment

cd venv
. bin/activate

c) Clone this project

git clone https://github.com/pandeynandancse/human-protein-classifier.git

d) Move into cloned directory

cd human-protein-classifier

e) Now install all requirements

pip install -r requirements.txt

Run

1. After successfull installation windows user can directly open this link : https://127.0.0.1:5000
2. After successful installation open type

python app.py

and then open link : https://127.0.0.1:5000

To Do

1. More Interactive and stylish
2. Multi Image prediction at same time

Bug / Feature Request

If you find a bug (the website couldn't handle the query and / or gave undesired results), kindly open an issue here by including your search query and the expected result.

If you'd like to request a new function, feel free to do so by opening an issue here. Please include sample queries and their corresponding results.

Contribution

If you'd like to do some contribution, feel free to do so by opening a pull request here. Please include sample queries and their corresponding results.

Technologies Used

Credits

1. Special thanks to Kris Naik sir, front end source code has been taken from one of his project.
2. Special thanks to Akash NS sir,Founder of jovian.ml and Instructor of the course ZERO-to-GANs using pytorch , who hosted related dataset on Kaggle and organized in-class competition..