Metamask insights and security solutions

This repo contains the codebase which proposes a new idea of visulaizing ongoing transanctions from the user wallet, using metamask snap. It also, proposes a machine learning model that analyses the smart contract which the user is interacting with. All of this happens in real-time.

Code structure walkthrough

There are 4 major directories in the code, that comprises entire codebase.

1. Frontend code
2. Snap code
3. Backend code
4. ML model and Flask server

Each of the directories contains a README file, which contains all the necessary information related with the corresponding repository.

Starting the root repo

Clone the repo. Inside each of the workspaces, do a yarn install to make sure all the workspaces are using latest dependencies. Note The ML model, is not a yarn package. To run it locally, please refer to its readme file.

After doing yarn install in all the workspaces, in the root directory of the repo, run the below command.

yarn install && yarn start

This should start the backend and the frontend servers locally.

Starting the ML model

The ML model is currently not hosted on the cloud, as it requires a bit of preprocessing based on GPU to generate the result. To get the ML analysis, you'll need to start the server manually.

1. Go to the model pakcage
2. Make sure you have all the dependencies installed
3. Run python app.py. This will start a flask server on port 1234. If this port is already in use, then please specify a free port in the app.py file.

Data flow visualization