Setting up this project on your local machine is really easy.
- Download and install python 3.6 and git
- Clone using
$ git clone https://github.com/Sanji515/Abhikalpan-Hackathon.git && cd Abhikalpan-Hackathon
-
Install virtualenv
- on Ubuntu:
$ sudo apt install python-virtualenv
- on Windows:
$ pip install virtualenv
- on Ubuntu:
-
Create a virtual environment
- on Ubuntu:
$ virtualenv venv -p python3.6
- on Windows:
$ virtualenv venv
- on Ubuntu:
-
Activate the environment:
- on Ubuntu:
$ source venv/bin/activate
- on Windows:
$ ./venv/Scripts/activate
- on Ubuntu:
-
Install the requirements:
$ pip install -r requirements.txt
-
Make migrations
$ python manage.py makemigrations
-
Migrate the changes to the database
$ python manage.py migrate
-
Run the server
$ python manage.py runserver
That's it. Open web browser and hit the url http://127.0.0.1:8000
- IBM Cloud account: If you do not have an IBM Cloud account, you can create an account Click Here .
- Watson Knowledge Studio account: User must have a WKS account. If you do not have an account, you can create a free account click Here. Make a note of the login URL since it is unique to every login id
- Basic knowledge of building models in WKS: The user must possess basic knowledge of building model in WKS in order to build a custom model. Check getting started documentation Click Here
- Step1: Click here to create NLU service and enter the service name
- Step2: Once you click on Create NLU service instance should get created.
- Click Create Workspace in Watson Knowledge Studio
- In the Create Workspace pop up window, enter the name of the new project. Click Create
- Click on the workspace you created and on Entity Type Click on Upload and add json file Abhikalpan-Hackathon/Watson_knowlege_Studio/Entities/types-de2a5c20-3cc6-11e9-9a38-235c4e7dcc32.json
- On Documents Click Upload and select Abhikalpan-Hackathon/Watson_knowlege_Studio/dataset/corpus-de2a5c20-3cc6-11e9-9a38-235c4e7dcc32.zip
- Click on Annotation Task under Machine Learning model and click on task available
- Click on Task which is in Not Complete and make each task Complete
- Click on Versions under Machine learning model and create new version and deploy it to Natural Language Understanding services
- Click on Performance under Machine learning model and click on Train and Evaluate button which will train the model against the annotations