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); } }