This is the code for "Watch Me Build an Education Startup" by Siraj Raval on Youtube

This is the code for this video on Youtube by Siraj Raval. It's the code for EssayBrain, a tool for teacher that automatically grades and validates essays. In order to validate essays, it uses the copyleaks API to check for plagiarism. It also uses a modified version of GPT-2 to detect the likelihood that the text was real or fake. Then it outputs a validation score using these 2 scores. In order to grade the essay, it uses a neural network model trained on the automatic essay grading dataset on Kaggle found here https://www.kaggle.com/c/asap-aes/data .Take this code and go build a profitable startup with it.

Credits for the base repository go to this academic team. Credits for the other tools go to Google, Stripe, and OpenAI. I will also take ths opportunity to give thanks to all humans who perform selfless acts for others, in big ways and small. Thank you, please keep doing that.

This is an ongoing, open source project. Please contribute, it will help all those that watch the video. Make a PR for any of these bugs

• Finish implementing the CopyLeaks API
• Create a more accurate version of a validation score that uses the CopyLeaks API score + the GPT-2 real/fake score. Display it to the screen when computed.
• Fix the Tensorflow.js integration, such that its using a pretrained model on the Kaggle automatic essay scoring data, instead ot the default model its using right now. Display the score to the screen accordingly.
• Redesign the app so it looks even more professional (UI elements)

• Flask
• GPT-2 model
• D3.js
• Stripe
• Firebase
• Copyleaks API

Install dependencies for Python >3.6 :

pip install -r requirements.txt

run server for gpt-2-small:

python server.py

the demo instance runs now at http://localhost:5001/client/index.html

start the server for BERT:

python server.py --model BERT

the instance runs now at http://localhost:5001/client/index.html?nodemo. HINT: we only provide demo texts for gpt2-small.

usage: server.py [-h] [--model MODEL] [--nodebug NODEBUG] [--address ADDRESS]
                 [--port PORT] [--nocache NOCACHE] [--dir DIR] [--no_cors]

optional arguments:
  -h, --help         show this help message and exit
  --model MODEL		 choose either 'gpt-2-small' (default) or 'BERT' or your own
  --nodebug NODEBUG  server in non-debugging mode
  --port PORT	     port to launch UI and API (default:5001)
  --no_cors          launch API without CORS support (default: False)

The backend defines a number of model api's that can be invoked by the server by starting it with the parameter --model NAME. To add a custom model, you need to write your own api in backend/api.py and add the decorator @register_api(name=NAME).

Each api needs to be a class that inherits from AbstractLanguageChecker, which defines two functions check_probabilities and postprocess. Please follow the documentation within api.py when implementing the class and the functions.

the source code for the front-end is in client/src.

To modify, installing of node dependencies is necessary:

cd client/src; npm install; cd ../..

re-compilation of front-end:

> rm -rf client/dist;cd client/src/; npm run build; cd ../..