Files

130 lines
4.2 KiB
C#

using System;
using System.IO;
using System.Net.Http;
using System.Threading.Tasks;
namespace MarketData.CNNProcessing
{
public class CNNClient
{
public enum Model{resnet50,resnet50B,resnet50_20241024_270,inception,vgg16,lenet5,convnext,ping};
private static readonly string Alive="Alive";
private readonly HttpClient client = new HttpClient();
private string baseUrl;
private static int REQUEST_TIMEOUT=15000;
public CNNClient(String baseUrl)
{
client.Timeout=new TimeSpan(0,0,0,0,REQUEST_TIMEOUT);
if(!baseUrl.StartsWith("http://") && !baseUrl.StartsWith("HTTP://"))baseUrl="http://"+baseUrl;
this.baseUrl=baseUrl;
}
private String GetModelUrl(CNNClient.Model model)
{
return baseUrl+"/predict_"+model.ToString();
}
public String Predict(CNNClient.Model model,Stream stream)
{
try
{
string responsePayload = Upload(GetModelUrl(model),stream).GetAwaiter().GetResult();
return responsePayload;
}
catch(Exception exception)
{
return exception.ToString();
}
}
// This method is used to process an image through PIL. All images that are being used in training should ultimately be processed through
// PIL (Python Image Library) so that images can be normalized by python prior to training. We do this because we use PIL during the
// prediction process and we want all images used in training/validation and all images used for prediction to be touched by and processed
// by PIL
public Stream ProcessImage(Stream stream)
{
return UploadImage(baseUrl+"/process_image", stream);
}
public bool Ping()
{
try
{
string responsePayload = Upload(baseUrl+"/ping").GetAwaiter().GetResult();
if(null==responsePayload)return false;
return responsePayload.Equals(Alive);
}
catch(Exception exception)
{
MDTrace.WriteLine(LogLevel.DEBUG,String.Format("Exception encountered: {0}",exception.ToString()));
return false;
}
}
private async Task<String> Upload(String url,Stream stream)
{
try
{
using (HttpRequestMessage request = new HttpRequestMessage(HttpMethod.Post, url))
{
int streamEnd = Convert.ToInt32(stream.Length);
byte[] byteArray = new byte[streamEnd];
stream.Read(byteArray, 0, streamEnd);
request.Content=new ByteArrayContent(byteArray);
using (HttpResponseMessage response = await client.SendAsync(request))
{
return await response.Content.ReadAsStringAsync();
}
}
}
catch(Exception exception)
{
MDTrace.WriteLine(LogLevel.DEBUG,String.Format("Exception encountered: {0}",exception.ToString()));
return null;
}
}
/// <summary>
/// This method uploads an image stream to the specified url and receives the processed image back.
/// The processed image simply processes the image through PIL (Python Image Library)
/// </summary>
private Stream UploadImage(String url,Stream stream)
{
try
{
using (HttpRequestMessage request = new HttpRequestMessage(HttpMethod.Post, url))
{
int streamEnd = Convert.ToInt32(stream.Length);
byte[] byteArray = new byte[streamEnd];
stream.Read(byteArray, 0, streamEnd);
request.Content=new ByteArrayContent(byteArray);
HttpResponseMessage response = client.SendAsync(request).GetAwaiter().GetResult();
return response.Content.ReadAsStreamAsync().GetAwaiter().GetResult();
}
}
catch(Exception exception)
{
MDTrace.WriteLine(LogLevel.DEBUG,String.Format("Exception encountered: {0}",exception.ToString()));
return null;
}
}
private async Task<String> Upload(String url)
{
try
{
using (HttpRequestMessage request = new HttpRequestMessage(HttpMethod.Get, url))
{
HttpResponseMessage response = await client.SendAsync(request);
return await response.Content.ReadAsStringAsync();
}
}
catch(Exception exception)
{
MDTrace.WriteLine(LogLevel.DEBUG,String.Format("Exception encountered: {0}",exception.ToString()));
return null;
}
}
}
}