private MLRequestModel CreatePredictionRequestBody(PlayerStatistics aggregatedStats)
{
var inputs = new MLRequestInputModel[]
{
new MLRequestInputModel
{
MIN = aggregatedStats.MIN.ToString(),
FGA = aggregatedStats.FGA.ToString(),
FG3A = aggregatedStats.FG3A.ToString(),
FTA = aggregatedStats.FTA.ToString(),
OREB = aggregatedStats.OREB.ToString(),
DREB = aggregatedStats.DREB.ToString(),
AST = aggregatedStats.AST.ToString(),
TOV = aggregatedStats.TOV.ToString(),
STL = aggregatedStats.STL.ToString(),
BLK = aggregatedStats.BLK.ToString(),
PF = aggregatedStats.PF.ToString(),
PTS = aggregatedStats.PTS.ToString()
}
};
var input1 = new MLRequestInputListModel {
input1 = inputs
};
var request = new MLRequestModel {
Inputs = input1, GlobalParameters = new { }
};
return(request);
}
private async Task <MLResponseModel> FetchPrediction(MLRequestModel data)
{
using (var client = new HttpClient())
{
client.DefaultRequestHeaders.Authorization = new AuthenticationHeaderValue("Bearer", ApiKey);
client.BaseAddress = new Uri(Url);
var dataString = JsonConvert.SerializeObject(data);
var content = new StringContent(dataString, System.Text.Encoding.UTF8, "application/json");
var responseMessage = await client.PostAsync("", content);
var response = await responseMessage.Content.ReadAsStringAsync();
var responseObject = JsonConvert.DeserializeObject <MLResponseModel>(response);
return(responseObject);
}
}
