<?php

namespace App\Http\Controllers;

use App\Models\User;
use App\Models\Atribut;
use App\Models\Probability;
use App\Models\TestingData;
use Illuminate\Support\Str;
use App\Models\NilaiAtribut;
use App\Models\TrainingData;
use Illuminate\Http\Request;
use App\Exports\TestingExport;
use App\Imports\TestingImport;
use App\Models\Classification;
use Illuminate\Support\Facades\Log;

use Illuminate\Support\Facades\Auth;
use Illuminate\Support\Facades\Hash;
use Maatwebsite\Excel\Facades\Excel;
use Illuminate\Database\QueryException;
use Yajra\DataTables\Facades\DataTables;
use Illuminate\Support\Facades\Validator;
use Illuminate\Database\Eloquent\ModelNotFoundException;

class HomeController extends Controller
{
    public function index()
		{
			$atribut = Atribut::all();
			return view('user.index', compact('atribut'));
		}
		public function user_testing()
		{
			$atribut = Atribut::get();
			if (count($atribut) === 0) {
				return to_route('atribut.index')
					->withWarning('Tambahkan Atribut dulu sebelum menginput Dataset');
			}
			$nilai = NilaiAtribut::get();
			$calculated = Probability::count();
			$hasil = ProbabLabel::$label;
			return view(
				'user.dataset.testing',
				compact('atribut', 'nilai', 'calculated', 'hasil')
			);
		}
		public function user_training()
		{
			$atribut = Atribut::get();
			if (count($atribut) === 0) {
				return to_route('atribut.index')
					->withWarning('Tambahkan Atribut dulu sebelum menginput Dataset');
			}
			$nilai = NilaiAtribut::get();
			$hasil = ProbabLabel::$label;
			return view('user.dataset.training', compact('atribut', 'nilai', 'hasil'));
		}

		public function user_probab()
	{
		$atribut = Atribut::get();
		if (count($atribut) === 0) {
			return to_route('atribut.index')
				->withWarning('Atribut dan Nilai Atribut Kosong');
		}
		$nilaiattr = NilaiAtribut::get();
		$data = Probability::get();
		$kelas = Probability::probabKelas();
		$training = TrainingData::get();
		$attribs = ['atribut' => $atribut, 'nilai' => $nilaiattr];
		$hasil = ProbabLabel::$label;
		return view(
			'user.naivebayes.probab',
			compact('attribs', 'data', 'kelas', 'training', 'hasil')
		);
	}
	public function user_class()
	{
		return view('user.naivebayes.classify', ['hasil' => ProbabLabel::$label]);
	}

		public function profile()
		{
			return view('user.profil');
		}

		public function update(Request $request)
    {
        $id = auth()->user()->id;
        $validator = Validator::make($request->all(), [
            'name' => 'required',
            'email' => 'required',
        ]);

        if ($validator->fails()) {
            return redirect()->back()->with('error', 'terdapat data yang belum diisi')->withInput();
        }

        $input = $request->all();
        $findUser = User::find($id);

        if($findUser) {
            // update data
            $findUser->name = $input['name'];
            $findUser->email = $input['email'];
            if(!empty($input['password'])) {
                $findUser->password = bcrypt($input['password']);
            }

            // save data
            $findUser->save();
            return redirect()->back()->with('success', 'Data berhasil disimpan');
        } else {
            return redirect()->back()->with('error', 'Tidak ditemukan data!');
        }
    }

		public function performa()
		{
			if (Classification::count() === 0) {
				return to_route('class.index')
					->withWarning('Lakukan klasifikasi dulu sebelum melihat performa klasifikasi');
			}
			$data['train'] = $this->cm('train');
			$data['test'] = $this->cm('test');
			$performa['train'] = $this->performas($data['train']);
			$performa['test'] = $this->performas($data['test']);
			$stat = ProbabLabel::$label;
			return view('user.performa', compact('data', 'performa', 'stat'));
		}

		private static function cm(string $type)
	{
		$tp = Classification::where('type', $type)->where('predicted', true)
			->where('real', true)->count(); //True Positive
		$fp = Classification::where('type', $type)->where('predicted', true)
			->where('real', false)->count(); //False Positive
		$fn = Classification::where('type', $type)->where('predicted', false)
			->where('real', true)->count(); //False Negative
		$tn = Classification::where('type', $type)->where('predicted', false)
			->where('real', false)->count(); //True Negative
		$total = $tp + $fp + $fn + $tn;
		return [
			'tp' => $tp,
			'fp' => $fp,
			'fn' => $fn,
			'tn' => $tn,
			'total' => $total
		];
	}
	private static function performas(array $data)
	{
		if ($data['total'] === 0) $accu = $prec = $rec = $f1 = 0;
		else {
			$accu = (($data['tp'] + $data['tn']) / $data['total']) * 100;
			$prec = ($data['tp'] / ($data['tp'] + $data['fp'])) * 100;
			$rec = ($data['tp'] / ($data['tp'] + $data['fn'])) * 100;
			$f1 = 2 * ($prec * $rec) / ($prec + $rec);
		}
		return [
			'accuracy' => $accu, 'precision' => $prec, 'recall' => $rec, 'f1' => $f1
		];
	}
}