using Microsoft.AspNetCore.Hosting;
using Microsoft.Extensions.Configuration;
using Microsoft.Extensions.Hosting;
using Microsoft.Extensions.Logging;
using System;
using System.Collections.Generic;
using System.Linq;
using System.Threading.Tasks;
using Microsoft.ML;
using Microsoft.ML.Data;
using InfluxData.Net.Common.Enums;
using InfluxData.Net.InfluxDb;
using System.CodeDom.Compiler;
using AdysTech.InfluxDB.Client.Net;
using System.Drawing;
using InfluxData.Net.InfluxDb.Models;

namespace Ropin.IOT.MLService
{
    public class Program
    {
        //public static void Main(string[] args)
        //{
        //    CreateHostBuilder(args).Build().Run();

        //}
        // 定义文件路径
        private static string _dataPath = "./accident_data.csv";
        private static string _modelPath = "./AccidentPredictionModel.zip";
        public static void Main(string[] args)
        {
            //try
            //{
            //    GetData(args);
            //}
            //catch (Exception ex) { Console.WriteLine(ex.InnerException.Message); }

            



        Console.OutputEncoding = System.Text.Encoding.UTF8;
            Console.WriteLine("工厂事故预测系统启动...");

            // 创建ML.NET上下文  
            MLContext mlContext = new MLContext(seed: 0);

            // 加载数据  
            Console.WriteLine("正在加载数据...");
            IDataView dataView = mlContext.Data.LoadFromTextFile<AccidentData>(
                path: _dataPath,
                hasHeader: true,
                separatorChar: ',',
                allowQuoting: true
            );

            // 分割训练集和测试集  
            var dataSplit = mlContext.Data.TrainTestSplit(dataView, testFraction: 0.2);
            var trainingData = dataSplit.TrainSet;
            var testData = dataSplit.TestSet;

            // 数据处理和特征工程  
            Console.WriteLine("正在处理数据和提取特征...");
            var dataProcessPipeline = mlContext.Transforms.Conversion.MapValueToKey(
                    outputColumnName: "Label",  // 这将创建一个新列作为Key类型标签  
                    inputColumnName: "AccidentLevel"  // 原始字符串标签  
                )
                .Append(mlContext.Transforms.Categorical.OneHotEncoding(
                    new[] {
                new InputOutputColumnPair("CountryEncoded", "Country"),
                new InputOutputColumnPair("IndustrySectorEncoded", "IndustrySector"),
                new InputOutputColumnPair("CriticalRiskEncoded", "CriticalRisk"),
                new InputOutputColumnPair("GenreEncoded", "Genre"),
                new InputOutputColumnPair("EmployeeTypeEncoded", "EmployeeOrThirdParty")
                    }))
                // 文本特征提取（从描述中提取特征）  
                .Append(mlContext.Transforms.Text.FeaturizeText("DescriptionFeaturized", "Description"))
                // 合并所有特征到一个向量  
                .Append(mlContext.Transforms.Concatenate("Features",
                    "CountryEncoded", "IndustrySectorEncoded", "CriticalRiskEncoded",
                    "GenreEncoded", "EmployeeTypeEncoded", "DescriptionFeaturized"));

            // 选择训练算法 - 使用多类分类器  
            Console.WriteLine("构建和训练模型...");
            var trainer = mlContext.MulticlassClassification.Trainers.SdcaMaximumEntropy();

            // 构建完整训练管道  
            var trainingPipeline = dataProcessPipeline.Append(trainer)
                // 将预测结果映射回原始标签值  
                .Append(mlContext.Transforms.Conversion.MapKeyToValue("PredictedLabel"));

            // 训练模型  
            var trainedModel = trainingPipeline.Fit(trainingData);
            Console.WriteLine("模型训练完成！");

            // 保存模型  
            mlContext.Model.Save(trainedModel, trainingData.Schema, _modelPath);
            Console.WriteLine($"模型已保存到: {_modelPath}");

            // 评估模型  
            Console.WriteLine("评估模型性能...");
            var predictions = trainedModel.Transform(testData);
            var metrics = mlContext.MulticlassClassification.Evaluate(predictions);

            // 输出评估指标  
            Console.WriteLine($"宏平均精确度: {metrics.MacroAccuracy:F2}");
            Console.WriteLine($"微平均精确度: {metrics.MicroAccuracy:F2}");
            Console.WriteLine($"对数损失: {metrics.LogLoss:F2}");
            Console.WriteLine($"混淆矩阵: \n{metrics.ConfusionMatrix.GetFormattedConfusionTable()}");

            // 使用模型进行预测示例  
            PredictSample(mlContext, trainedModel);
            CreateHostBuilder(args).Build().Run();
        }

        public static IHostBuilder CreateHostBuilder(string[] args) =>
            Host.CreateDefaultBuilder(args)
                .ConfigureWebHostDefaults(webBuilder =>
                {
                    webBuilder.UseStartup<Startup>();
                });

        #region 从Influxdb读取数据创建模型预测
        private static readonly IInfluxDBClient client = new InfluxDBClient("http://60.204.212.71:8085/", "admin", "123456");
        public static async Task GetData(string[] args)
        {

            //// 查询最近 10 条 CPU 使用率数据
            //    var query1 = "SELECT * FROM fanyidev ORDER BY time DESC LIMIT 10";
            //    var results = await client.QueryMultiSeriesAsync("fanyidb", query1);

            //    if (results != null && results.Count > 0)
            //    {

            //    }
            //     if (results != null && results.Count > 0)
            //    {
            //        // 处理查询结果
            //        foreach (var series in results)
            //        {
            //            Console.WriteLine($"查询结果集: {series.Name}");
            //            foreach (var point in series.Entries)
            //            {
            //                // 根据字段名获取值
            //                var time = point.GetTimeAsDateTime();
            //        var host = point.GetTagAsString("host");
            //        var value = point.GetFieldAsDouble("value");
            //        Console.WriteLine($"时间: {time}, 主机: {host}, CPU 使用率: {value}");
            //            }
            //}
            //    }





            //传入查询命令，支持多条
            var queries = new[]
            {

                " SELECT * FROM fanyidev WHERE time> now() -  1h "
            };

            var dbName = "fanyidb";

            InfluxDbClient influxDbClient = new InfluxDbClient("http://60.204.212.71:8085/", "admin", "123456", InfluxDbVersion.Latest);
            //从指定库中查询数据

            var response = await influxDbClient.Client.QueryAsync(queries, dbName);

            if (response.Any())
            {
                var series = response.ToList();
                foreach (var value in series[0].Values)
                {
                    //var TimeStamp = DateTime.Parse((string)value[0]);
                    var Pressure = Convert.ToDouble(value[2]);
                }
                var dataPoints = series[0].Values.Select(value =>
                    new DeviceStatusDataPoint
                    {
                        //TimeStamp = DateTime.Parse((string)value[0]),
                        //Temperature = Convert.ToDouble(value[1]),
                        Pressure = Convert.ToDouble(value[2])//,
                        //IsFaulty = (bool)value[3]
                    }).ToList();

                dataPoints = new List<DeviceStatusDataPoint>();
                var v1 = new DeviceStatusDataPoint
                {
                    TimeStamp = DateTime.Now.AddMilliseconds(-1),
                    Temperature = 70,
                    Pressure = 150,
                    IsFaulty = true
                };
                var v2 = new DeviceStatusDataPoint
                {
                    TimeStamp = DateTime.Now,
                    Temperature = 60,
                    Pressure = 140,
                    IsFaulty = false
                };
                dataPoints.Add(v1);
                dataPoints.Add(v2);

                // 使用 ML.NET 训练模型
                var mlContext = new MLContext();

                // 将数据加载到 IDataView
                var dataView = mlContext.Data.LoadFromEnumerable(dataPoints);

                // 分割数据集为训练集和测试集
                var trainTestSplit = mlContext.Data.TrainTestSplit(dataView, testFraction: 0.2);
                var trainingData = trainTestSplit.TrainSet;
                var testData = trainTestSplit.TestSet;

                // 定义管道
                var pipeline = mlContext.Transforms.Concatenate("Features", nameof(DeviceStatusDataPoint.Temperature), nameof(DeviceStatusDataPoint.Pressure))
                    .Append(mlContext.BinaryClassification.Trainers.SdcaLogisticRegression(labelColumnName: nameof(DeviceStatusDataPoint.IsFaulty)));

                // 训练模型
                var model = pipeline.Fit(trainingData);

                // 创建预测引擎
                var predictionEngine = mlContext.Model.CreatePredictionEngine<DeviceStatusDataPoint, FaultPrediction>(model);

                // 预测新样本
                var sampleData = new DeviceStatusDataPoint { Temperature = 75.0, Pressure = 150.0 };
                var prediction = predictionEngine.Predict(sampleData);
                Console.WriteLine($"Predicted IsFaulty: {prediction.PredictedLabel}, Probability: {prediction.Probability}");

                // 评估模型
                var predictions = model.Transform(testData);
                var metrics = mlContext.BinaryClassification.Evaluate(predictions);
                Console.WriteLine($"Accuracy: {metrics.Accuracy}");
            }
            else
            {
                Console.WriteLine("Failed to retrieve data from InfluxDB.");
            }


            //var response = await influxDbClient.Client.QueryAsync(query);

            ////得到Serie集合对象（返回执行多个查询的结果）

            //var series = response.ToList();

            ////取出第一条命令的查询结果，是一个集合

            //var list = series[0].Values;

            ////从集合中取出第一条数据

            //var info_model = list.FirstOrDefault();

        }


        #endregion


        private static void PredictSample(MLContext mlContext, ITransformer model)
        {
            // 创建预测引擎  
            var predictionEngine = mlContext.Model.CreatePredictionEngine<AccidentData, AccidentPrediction>(model);

            // 创建测试样本  
            var sampleAccident = new AccidentData
            {
                Country = "Country_01",
                IndustrySector = "Mining",
                Genre = "Male",
                EmployeeOrThirdParty = "Third Party",
                CriticalRisk = "Pressed",
                Description = "Worker operating drilling equipment without proper safety procedures, potential for hand injury."
            };

            // 进行预测  
            var prediction = predictionEngine.Predict(sampleAccident);

            Console.WriteLine("\n预测示例：");
            Console.WriteLine($"行业: {sampleAccident.IndustrySector}");
            Console.WriteLine($"风险类型: {sampleAccident.CriticalRisk}");
            Console.WriteLine($"事故描述: {sampleAccident.Description}");
            Console.WriteLine($"预测事故等级: {prediction.PredictedAccidentLevel}");

            // 输出各类别的概率  
            Console.WriteLine("各等级的概率分布:");
            var labels = new[] { "I", "II", "III", "IV" };
            for (int i = 0; i < prediction.Score.Length; i++)
            {
                if (i < prediction.Score.Length)
                    Console.WriteLine($"等级 {i}: {prediction.Score[i]:P2}");
            }

            Console.ReadKey();
        }
    }
    public class AccidentData
    {
        [LoadColumn(0)]
        public float SeqNo { get; set; }

        [LoadColumn(1)]
        public string Date { get; set; }

        [LoadColumn(2)]
        public string Country { get; set; }

        [LoadColumn(3)]
        public string Local { get; set; }

        [LoadColumn(4)]
        public string IndustrySector { get; set; }

        [LoadColumn(5)]
        public string AccidentLevel { get; set; }  // 原始字符串标签  

        [LoadColumn(6)]
        public string PotentialAccidentLevel { get; set; }

        [LoadColumn(7)]
        public string Genre { get; set; }

        [LoadColumn(8)]
        public string EmployeeOrThirdParty { get; set; }

        [LoadColumn(9)]
        public string CriticalRisk { get; set; }

        [LoadColumn(10)]
        public string Description { get; set; }
    }

    public class AccidentPrediction
    {
        [ColumnName("PredictedLabel")]
        public string PredictedAccidentLevel { get; set; }

        public float[] Score { get; set; }
    }

  

    public class DeviceStatusDataPoint
    {
        [LoadColumn(0)]
        public DateTime TimeStamp { get; set; }

        [LoadColumn(1)]
        public double Temperature { get; set; }

        [LoadColumn(2)]
        public double Pressure { get; set; }

        [LoadColumn(3)]
        public bool IsFaulty { get; set; }
    }

    public class FaultPrediction
    {
        [ColumnName("PredictedLabel")]
        public bool PredictedLabel { get; set; }

        [ColumnName("Probability")]
        public float Probability { get; set; }
    }
}