Create an image classification client application

from msrest.authentication import ApiKeyCredentials
from azure.cognitiveservices.vision.customvision.prediction import CustomVisionPredictionClient


 # Authenticate a client for the prediction API
credentials = ApiKeyCredentials(in_headers={"Prediction-key": "<YOUR_PREDICTION_RESOURCE_KEY>"})
prediction_client = CustomVisionPredictionClient(endpoint="<YOUR_PREDICTION_RESOURCE_ENDPOINT>",
                                                 credentials=credentials)

# Get classification predictions for an image
image_data = open("<PATH_TO_IMAGE_FILE>"), "rb").read()
results = prediction_client.classify_image("<YOUR_PROJECT_ID>",
                                           "<YOUR_PUBLISHED_MODEL_NAME>",
                                           image_data)

# Process predictions
for prediction in results.predictions:
    if prediction.probability > 0.5:
        print(image, ': {} ({:.0%})'.format(prediction.tag_name, prediction.probability))

using System;
using System.IO;
using Microsoft.Azure.CognitiveServices.Vision.CustomVision.Prediction;

// Authenticate a client for the prediction API
CustomVisionPredictionClient prediction_client = new CustomVisionPredictionClient(new ApiKeyServiceClientCredentials("<YOUR_PREDICTION_RESOURCE_KEY>"))
{
    Endpoint = "<YOUR_PREDICTION_RESOURCE_ENDPOINT>"
};

// Get classification predictions for an image
MemoryStream image_data = new MemoryStream(File.ReadAllBytes("<PATH_TO_IMAGE_FILE>"));
var result = prediction_client.ClassifyImage("<YOUR_PROJECT_ID>",
                                             "<YOUR_PUBLISHED_MODEL_NAME>",
                                             image_data);

// Process predictions
foreach (var prediction in result.Predictions)
{
    if (prediction.Probability > 0.5)
    {
        Console.WriteLine($"{prediction.TagName} ({prediction.Probability})");
    }
}