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
{
"input_data": [
[
1.0380048,
0.5586108,
1.1037828,
1.712096
]
],
"input_shapes": [
[
4
]
],
"output_data": [
[
0.0016699483385309577,
0.021144982427358627,
0.977185070514679
]
]
}
```
The `output_data` 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`