Azure functions (C#)

using System.IO;
using Microsoft.AspNetCore.Mvc;
using Microsoft.Azure.WebJobs;
using Microsoft.Azure.WebJobs.Extensions.Http;
using Microsoft.AspNetCore.Http;
using Microsoft.Azure.WebJobs.Host;
using Newtonsoft.Json;
using BayesServer.Inference;
using BayesServer.Inference.RelevanceTree;

namespace BayesServer.Azure.Functions
    /// <summary>
    /// A simple example of using Bayes Server within an HttpTrigger Azure function.
    /// </summary>
    /// <remarks>
    /// If you are using an Azure function Consumption plan, after a period of inactivity Azure functions require 
    /// a cold start.  This can lead to a delay of up to 2 seconds, which is not caused by Bayes Server itself. 
    /// You can use Azure function monitoring to provide timing detail and additional information about usage.
    /// Please see the Azure documentation for further information.
    /// </remarks>
    public static class Waste
        // Cache the network so it can re-used between calls.
        // See the Azure documentation for more advanced caching options.
        private static readonly Network network;
        private static readonly IInferenceFactory factory;

        private static readonly Variable filterState;

        static Waste()
   = new Network();

            // In this example the 'Waste' network, which is included as an example with Bayes Server,
            // is loaded from a project resource.
            // See the Azure documentation on the many options for loading resources/data

            using (var stream = new MemoryStream(Properties.Resources.Waste))

            Waste.factory = new RelevanceTreeInferenceFactory();

            Waste.filterState = network.Variables["Filter state", true];

        /// <summary>
        /// HttpTrigger that calculates the log-likelihood of evidence.
        /// The http(s) request body expects json with an evidence section.  For example:
        /// {
        ///    "evidence": {
        ///       "filterState": "Defect"
        ///     }
        /// }
        /// </summary>
        public static IActionResult Run(
                [HttpTrigger(AuthorizationLevel.Function, "post", Route = null)]
            HttpRequest req,
                TraceWriter log)
            log.Info("C# HTTP trigger function processed a request.");

            var requestBody = new StreamReader(req.Body).ReadToEnd();
            dynamic data = JsonConvert.DeserializeObject(requestBody);
            dynamic evidence = data?.evidence;
            string filterStateValue = evidence?.filterState;

            if (filterStateValue == null)
                return new BadRequestObjectResult("filterState not found in json body (evidence section)");

            // In this simple example, we create a new inference engine per call.
            // Ideally, inference engines are re-used over multiple calls.
            // If required, please refer to the Azure documentation on how best to do this.
            // A simple approach would be to use one of the built in .NET Concurrent data structures.

            var inference = factory.CreateInferenceEngine(network);
            var queryOptions = factory.CreateQueryOptions();
            var queryOutput = factory.CreateQueryOutput();
            queryOptions.LogLikelihood = true;

            inference.Evidence.SetState(filterState.States[filterStateValue, true]);

            inference.Query(queryOptions, queryOutput);

            return (ActionResult)new OkObjectResult($"Log-likelihood = {queryOutput.LogLikelihood.Value}");