- Creating python project
- Machine Learning model building
- Export trained model
- Export Tfidf object used to create training dataset
- Set-up Flask environment
- creating app.py which will have routes of index and other html files.
- creating or using templates for attractive user interface.
- create two directories namely templates and static in root directory of the project.
- templates directory will have html files.
- static directory will have css and javascript files.
- Finally, create nltk.txt, requirements.txt and Procfile(shell script file.) in root directory.
- First of all, we need to create git repository for our project.
- Upload all the project files inside github repository.
- Go to Heroku platform and login or create your account. It's free platform where we can host our ML/DL project web application.
- Create your free Heroku project
- Integrate your github profile
- Search for the github repository within provided search bar.
- Finally, deploy your machine learning Flask web application.