Marlin Oyster teeML changes

README.md

@@ -136,21 +136,11 @@ We use **Cairo** for two proofs:
 
 We used the LambdaClass CairoVM. Because of the current dependency mismatches between the prover and the runner, the Cairo prover and the Cairo runner had to be compiled separately.
 
-### Using Giza for zkML
+### teeML
 
-ZkML is one of ZK's many use cases. It helps you assert that a prediction's result was obtained with the right model, trained on the right dataset, and fed with the right input.
+The demo used to generate zk-proofs for the facial feature classification predictions using [Cairo](https://starkware.co/cairo/). This was necessary to predict if the person is smiling & prove the same using a zk-proof. Althought the computations required for this were server-side done on the Cairo VM it still resulted in long wait time for the users of the demo.
 
-**Giza** focuses on helping developers create a provable machine-learning model.
-
-Here is the flow we followed:
-
-1. We used a **simple classifier from the [XGBoost library](https://xgboost.readthedocs.io/en/stable/) in Python**, which Giza fully supports. 
-2. We serialized our model in json thanks to the Giza SDK.
-3. We used the Giza API to turn our model into a Cairo program.
-4. We compiled the Cairo ML into Sierra using `scarb`
-5. We executed our model in the Cairo VM we were using.
-
-Deep learning models, especially CNNs, would typically be more appropriate for image recognition, but some primitives used by those are not yet supported. Larger models are also extremely hard to run in a Cairo VM because of their high memory requirements.
+By using [TEE](https://www.marlin.org/ai) for serving classification predictions & their attestations we not only reduced wait time for the users but also give security security for this particular use-case.
 
 ## Thanks
 

vibe-check-frontend/.gitignore

@@ -0,0 +1,30 @@
+target
+corelib
+node_modules
+pkg
+.env
+
+# Logs
+logs
+*.log
+npm-debug.log*
+yarn-debug.log*
+yarn-error.log*
+pnpm-debug.log*
+lerna-debug.log*
+
+node_modules
+dist
+dist-ssr
+*.local
+
+# Editor directories and files
+.vscode/*
+!.vscode/extensions.json
+.idea
+.DS_Store
+*.suo
+*.ntvs*
+*.njsproj
+*.sln
+*.sw?

vibe-check-frontend/README.md

@@ -1,15 +1,26 @@
-# Vibe Check
+# Hyle teeML Vibe Check
 
-Frontend for the EthCC Vibe check demo.
+Forked from Hyle Vibe check frontend [repo](https://github.com/Hyle-org/vibe-check/tree/main/vibe-check-frontend).
+
+# Changes
+## Changed zkML to teeML
+
+The demo used to generate zk-proofs for the facial feature classification predictions using [Cairo](https://starkware.co/cairo/). This was necessary to predict if the person is smiling & prove the same using a zk-proof. Althought the computations required for this were server-side done on the Cairo VM it still resulted in long wait time for the users of the demo.
+
+By using [TEE](https://www.marlin.org/ai) for serving classification predictions & their attestations we not only reduced wait time for the users but also give security security for this particular use-case.
+
+# Todo
+- [ ] Make production ready. Currently runnable in dev only.
 
 ### Development
 
+Create a `.env` file before running the below commands. See `sample.env`.
 ```bash
-bun install
-bun run dev
+npm install
+npm run dev
 ```
 
 ### Setup
 
 -   [VS Code](https://code.visualstudio.com/) + [Vue - Official](https://marketplace.visualstudio.com/items?itemName=Vue.volar)
--   [Prettier](https://marketplace.visualstudio.com/items?itemName=esbenp.prettier-vscode)
+-   [Prettier](https://marketplace.visualstudio.com/items?itemName=esbenp.prettier-vscode)