/Fruit-Freshness-Detection

This repository is an implementation of running python flask app on docker environment. On this project we will detect apples, bananas, and oranges using Yolov5 custom model, and then classify that using Tensorflow custom model. It can be done using image link.

Primary LanguagePython

Python Flask App on Docker

This repository is an implementation of running python flask app on docker environment. On this project we will detect apples, bananas, and oranges using Yolov5 custom model, and then classify that using Tensorflow custom model. It can be done using image link. Here is the label list of classification model :

  • freshapples
  • freshbanana
  • freshoranges
  • rottenapples
  • rottenbanana
  • rottenoranges

Requirements

  • Python 3.10 or later
  • Docker Desktop
  • Postman
  • WSL 1.2.5.0
  • Pip 23.0 or later
  • Free storage more than 10gb

Run Locally

Clone the project

  git clone https://github.com/aldebarankwsuperrr/Docker-Flask.git

Go to the project directory

  cd Docker-Flask

Build docker image

  docker build --tag docker-flas.

Run docker image as a container

  docker run -d -p 5000:5000 docker-flask
  

API Reference

Get Predict Result

  POST http://localhost:5000/predict

Use this json format

{
    "url": "Image Link"
}