feat: publishing infernet-container-starter v0.2.0

projects/prompt-to-nft/container/README.md

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+# 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%).