public async Task <KeyValuePair <bool, string> > PostString(string sourceHttpUrl, HttpContent?httpContent, bool verbose = true)
{
Uri sourceUri = new Uri(sourceHttpUrl);
if (verbose)
{
_logger.LogInformation("[PostString] HttpClientHelper > " + sourceUri.Host + " ~ " + sourceHttpUrl);
}
// // allow whitelist and https only
if (!_allowedDomains.Contains(sourceUri.Host) || sourceUri.Scheme != "https")
{
return
(new KeyValuePair <bool, string>(false, string.Empty));
}
try
{
using (HttpResponseMessage response = await _httpProvider.PostAsync(sourceHttpUrl, httpContent))
using (Stream streamToReadFrom = await response.Content.ReadAsStreamAsync())
{
var reader = new StreamReader(streamToReadFrom, Encoding.UTF8);
var result = await reader.ReadToEndAsync();
return(new KeyValuePair <bool, string>(response.StatusCode == HttpStatusCode.OK, result));
}
}
catch (HttpRequestException exception)
{
return(new KeyValuePair <bool, string>(false, exception.Message));
}
}
/// <summary>
/// Handles one round of Handling images (i.e. image saving, registering in Database, getting results for each image
/// </summary>
/// <param name="tempFolderPath">Path where to store file</param>
/// <param name="url">Url at which DeepLearningAPI is server</param>
/// <param name="files">Files (images) to be processed</param>
/// <returns></returns>
public async Task <ResponseModel> ProcessImagesAsync(
string tempFolderPath,
string url,
List <IFormFile> files)
{
logger.LogDebug($"{correlationId} - Getting predictions for {files.Count} images");
// orig. file names
List <string> images = new List <string>(files.Count);
List <PredictionRequest> predictionRequests = new List <PredictionRequest>(files.Count);
List <ImageDefinition> imageDefinitions = new List <ImageDefinition>(files.Count);
DateTime started = DateTime.Now;
WebRequest webRequest = dbProvider.RegisterRequest(correlationId);
// Save files and extract their EXIF data
List <string> newFileNames = RegisterImages(
webRequest,
files,
images,
predictionRequests,
tempFolderPath,
imageDefinitions
);
// Send actual request
logger.LogDebug($"{correlationId} - Sending request for predictions to \"{url}\"");
PredictionResponseModel predictionResponse = await httpProvider
.PostAsync <PredictionResponseModel>(url, new PredictionRequestModel(newFileNames));
if (predictionResponse.Data == null)
{
logger.LogError($"{correlationId} - Predictions from DeepLearning API was not returned.");
// TODO: Register metrics
throw new HttpRequestException($"Response from {url} does not contain any data");
}
// TODO: Save results to DB..
// predictionResponse.Data.ForEach();
// collect all results (amount: images x labels)
List <PredictionResult> allPredictionResults = predictionResponse.Data
.Select((imagePredictions, i) =>
imagePredictions.Select(prediction => new PredictionResult
{
Label = prediction.Key,
Score = prediction.Value,
ImageDefinition = imageDefinitions[i],
PredictionRequest = predictionRequests[i]
}).ToList())
.Aggregate((predictions1, predictions2) =>
{
predictions1.AddRange(predictions2);
return(predictions1);
});
// save results to db
dbProvider.RegisterPredictionResults(allPredictionResults);
dbProvider.RegisterMetrics(new Metric
{
WebRequestId = webRequest.WebRequestId,
MetricCode = "OVERALL",
Started = started,
Ended = DateTime.Now
});
// map image orig. names to prediction response
return(new ResponseModel(predictionResponse.Data.ToDictionary(predictions => images[predictionResponse.Data.IndexOf(predictions)])));
}