private void ExecuteLoadItemsCommand()
{
if (IsBusy)
{
return;
}
IsBusy = true;
try {
using (var dict = _db.GetDocument(CoreApp.DocId)) {
var arr = dict.GetArray(CoreApp.ArrKey);
for (int i = 0; i < arr.Count; i++)
{
DictionaryObject d = arr.GetDictionary(i);
_items[i].Name = d.GetString("key");
_items[i].Quantity = d.GetInt("value");
_items[i].ImageByteArray = d.GetBlob("image")?.Content;
}
}
} catch (Exception ex) {
Debug.WriteLine(ex);
} finally {
IsBusy = false;
}
}
public DictionaryObject Predict(DictionaryObject input)
{
var blob = input.GetBlob("photo");
if (blob == null)
{
return(null);
}
var imageData = blob.Content;
// `TensorFlowModel` is a fake implementation
// this would be the implementation of the ml model you have chosen
var modelOutput = TensorFlowModel.PredictImage(imageData);
return(new MutableDictionaryObject(modelOutput)); // <1>
}
protected override DictionaryObject DoPrediction(DictionaryObject input)
{
var blob = input.GetBlob("text");
if (blob == null)
{
return(null);
}
ContentType = blob.ContentType;
var text = Encoding.UTF8.GetString(blob.Content);
var wc = text.Split(' ', StringSplitOptions.RemoveEmptyEntries).Length;
var sc = text.Split('.', StringSplitOptions.RemoveEmptyEntries).Length;
var output = new MutableDictionaryObject();
output.SetInt("wc", wc);
output.SetInt("sc", sc);
return(output);
}