# Prompt-to-NFT Container ## Overview ## Building & Running the Container in Isolation Note that this container is meant to be started by the infernet-node. For development & Testing purposes, you can run the container in isolation using the following commands. ### Building the Container Simply run the following command to build the container. ```bash make build ``` Consult the [Makefile](./Makefile) for the build command. ### Adding Arweave File Add your arweave wallet file ### Running the Container To run the container, you can use the following command: ```bash make run ``` ## Testing the Container Run the following command to run an inference: ```bash curl -X POST http://127.0.0.1:3000/service_output \ -H "Content-Type: application/json" \ -d '{"source":1, "data": {"prompt": "a golden retriever skiing"}}' ``` #### Note Regarding the Input The inputs provided above correspond to an iris flower with the following characteristics. Refer to the 1. Sepal Length: `5.5cm` 2. Sepal Width: `2.4cm` 3. Petal Length: `3.8cm` 4. Petal Width: `1.1cm` Putting this input into a vector and scaling it, we get the following scaled input: ```python [1.0380048, 0.5586108, 1.1037828, 1.712096] ``` Refer to [this function in the model's repository](https://github.com/ritual-net/simple-ml-models/blob/03ebc6fb15d33efe20b7782505b1a65ce3975222/iris_classification/iris_inference_pytorch.py#L13) for more information on how the input is scaled. For more context on the Iris dataset, refer to the [UCI Machine Learning Repository](https://archive.ics.uci.edu/ml/datasets/iris). ### Output By running the above command, you should get a response similar to the following: ```json [ [ [ 0.0010151526657864451, 0.014391022734344006, 0.9845937490463257 ] ] ] ``` The response corresponds to the model's prediction for each of the classes: ```python ['setosa', 'versicolor', 'virginica'] ``` In this case, the model predicts that the input corresponds to the class `virginica`with a probability of `0.9845937490463257`(~98.5%).