This repository consists of files required for end to end implementation and deployment of Deep Learning Cotton Disease Classification web application created with flask and deployed on the Heroku platform.
If you want to view the deployed model, click on the following link:
https://cottondiseasepredict.herokuapp.com/
A glimpse of the web app:
• If you encounter this webapp as shown in the picture given below, it is occuring just because free dynos for this particular month provided by the Heroku platform have been completely used. You can access the webpage on 1st of the next month.
• Sorry for the inconvenience.
The Cotton Disease Predictor is a flask web application which classifies a cotton plant/leaf image into four categories viz. diseased cotton leaf, diseased cotton plant, fresh cotton leaf, and fresh cotton plant. The code is written in Python 3.6.10 and makes use of Keras and Tensorflow libraries in developing an InceptionV3 based image classification web application.
If you don't have Python installed, you can find it here. If you are using a lower version of Python you can upgrade using the pip package, ensuring you have the latest version of pip. To install the required packages and libraries, run this command in the project directory after cloning the repository:
pip install -r requirements.txtLogin or signup in order to create virtual app. You can either connect your github profile or download ctl to manually to deploy this project.
The next step would be to follow the instruction given in the Heroku Documentation to deploy a web app.
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