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https://github.com/filtoor/cnft-spam-filter

An open-source, lightweight, and portable spam classifier for cNFTs on Solana
https://github.com/filtoor/cnft-spam-filter

cnft cnfts solana solana-nft

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An open-source, lightweight, and portable spam classifier for cNFTs on Solana

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# cnft-spam-filter



An open-source, lightweight, and portable spam classifier for cNFTs on Solana with 96% accuracy.



Can run anywhere that webassembly runs: on a server, in a lambda function, and *even running entirely in your browser*:



https://github.com/solarnius/cnft-spam-filter/assets/157436846/3bd2788e-84e1-41b7-8600-a297a99696f3



Also included is the model training code and data, so you can train and bring your own model if the default model is not performing well.



Feature extraction is done with a combination of on-chain data and OCR using the [tesseract.js library](https://github.com/naptha/tesseract.js). Classification is done with naive bayes and a hand-picked set of `spam` and `ham` cNFTs.



## Live Example



You can try a live (heavily rate limited) example of the library running on AWS Lambda here: 



[https://api.filtoor.xyz/classify?address=A1xhLVywcq6SeZnmRG1pUzoSWxVMpS6J5ShEbt3smQJr](https://api.filtoor.xyz/classify?address=A1xhLVywcq6SeZnmRG1pUzoSWxVMpS6J5ShEbt3smQJr)



Try a new cNFT by replacing the `address={...}` parameter. The classifier will either spit out "spam" or "ham" (or "error" if something went wrong).



If you'd like to use this API in your production project, please [DM me](https://twitter.com/solarnius) to get set up!



## Installation



First, install the library:



`npm i cnft-spam-filter`



then import the requisite function:



`const { extractAndClassify } = require("cnft-spam-filter")`



or 



`import { extractAndClassify } from "cnft-spam-filter"`



Finally, call the function wherever you want to classify:



```js

const classification = await extractAndClassify(assetId, rpcUrl);

```



Note that you'll need to bring your own `rpcUrl` that supports the `DAS` api--I recommend Helius for their generous free plan https://www.helius.dev/.



## Examples



You can find a few lightweight examples of how to use the library in different environments in the [/examples folder](https://github.com/solarnius/cnft-spam-filter/tree/main/examples) of the repository.



`cnft-spam-filter` aims to be portable, so you can run it in pretty much any environment that you want.



## Training



You can train your own model and pass it to `classify(tokens, model)`. Code for this is in the [/train folder](https://github.com/solarnius/cnft-spam-filter/tree/main/train).



You'll see `spam_ids.json` and `ham_ids.json` there; these are the cNFTs used to train the model.



## Testing



You can test the accuracy of a model using the code in the [/test folder](https://github.com/solarnius/cnft-spam-filter/tree/main/train). Make sure that your training set and test set do not overlap. It should spit out a confusion matrix as well as all of the mistakes made:



10



## Usage in Production



If you want to use `cnft-spam-filter` in production, I recommend setting up a caching layer so that you don't have to analyze each cNFT multiple times. This should be done at your own app level: you can use redis, a database, localstorage--whatever you want.



## Contributing



Feel free to open pull requests to contribute if you think this is interesting! I will try to get to them as best as I can. There are definitely some tasks that need to be implemented.



## License



All code is released under the [MIT license](https://opensource.org/license/mit/) -- go crazy.



Solana/USDC donations are appreciated but not required by any means: 



`solarnius.sol`