"use client";

import { useEffect, useRef, useState } from "react";
import Webcam from "react-webcam";
import * as faceapi from "face-api.js";
import { Button } from "@/components/ui/button";
import { Camera } from "lucide-react";
import { useToast } from "@/hooks/use-toast";

const MODEL_URL = "https://cdn.jsdelivr.net/npm/@vladmandic/face-api/model";

const RealtimeFaceDetection = () => {
  const webcamRef = useRef<Webcam>(null);
  const canvasRef = useRef<HTMLCanvasElement>(null);
  const [isModelLoaded, setIsModelLoaded] = useState(false);
  const [isDetecting, setIsDetecting] = useState(false);
  const { toast } = useToast();

  useEffect(() => {
    const loadModels = async () => {
      try {
        await faceapi.nets.tinyFaceDetector.loadFromUri(MODEL_URL);
        await faceapi.nets.faceLandmark68Net.loadFromUri(MODEL_URL);
        await faceapi.nets.faceRecognitionNet.loadFromUri(MODEL_URL);
        setIsModelLoaded(true);
      } catch (error) {
        console.error("Error loading models:", error);
        toast({
          title: "Error",
          description: "Failed to load face detection models.",
          variant: "destructive",
        });
      }
    };
    loadModels();
  }, [toast]);

  const detectFace = async () => {
    if (!webcamRef.current || !webcamRef.current.video || !canvasRef.current)
      return;

    const video = webcamRef.current.video;
    const canvas = canvasRef.current;
    const context = canvas.getContext("2d");

    if (!context) return;

    // Set canvas size to match video
    canvas.width = video.videoWidth;
    canvas.height = video.videoHeight;
    context.clearRect(0, 0, canvas.width, canvas.height); // Clear previous drawings

    // Mirror the canvas context to match the mirrored video
    context.translate(canvas.width, 0); // Move the origin to the right side of the canvas
    context.scale(-1, 1); // Flip the context horizontally

    // Detect face
    const detections = await faceapi
      .detectSingleFace(video, new faceapi.TinyFaceDetectorOptions())
      .withFaceLandmarks()
      .withFaceDescriptor();

    if (detections) {
      // Draw bounding box
      const { x, y, width, height } = detections.detection.box;
      context.strokeStyle = "red"; // Box color
      context.lineWidth = 3;
      context.strokeRect(x, y, width, height);

      // Capture the face as an image
      const imageCanvas = document.createElement("canvas");
      const imageContext = imageCanvas.getContext("2d");

      if (imageContext) {
        imageCanvas.width = video.videoWidth;
        imageCanvas.height = video.videoHeight;

        // Mirror the image context as well
        imageContext.translate(imageCanvas.width, 0);
        imageContext.scale(-1, 1);

        imageContext.drawImage(
          video,
          0,
          0,
          imageCanvas.width,
          imageCanvas.height
        );

        // Convert to Blob and send
        imageCanvas.toBlob((blob) => {
          if (blob) {
            sendFaceDataToAPI(blob);
          }
        }, "image/jpeg");
      }
    }
  };

  const sendFaceDataToAPI = async (imageBlob: Blob) => {
    try {
      const formData = new FormData();
      formData.append("image", imageBlob, "face.jpg");

      const response = await fetch(
        `${process.env.NEXT_PUBLIC_BASE_URL}/search`,
        {
          method: "POST",
          body: formData, // Send multipart/form-data
        }
      );

      const data = await response.json();
      toast({ title: data?.name, description: data.message });
    } catch (error) {
      console.error("Error sending face data:", error);
      toast({
        title: "Error",
        description: "Failed to send face data.",
        variant: "destructive",
      });
    }
  };

  const startDetection = () => {
    if (!isModelLoaded) return;
    setIsDetecting(true);
    const interval = setInterval(detectFace, 1000);
    setTimeout(() => {
      clearInterval(interval);
      setIsDetecting(false);
    }, 100000); // Stops detection after 10 seconds
  };

  return (
    <div className="max-w-3xl mx-auto">
      <div className="relative">
        <Webcam ref={webcamRef} mirrored className="w-full rounded-lg" />
        <canvas
          ref={canvasRef}
          className="absolute top-0 left-0 w-full h-full"
        />
      </div>
      <div className="mt-6 flex justify-center">
        <Button
          onClick={startDetection}
          disabled={!isModelLoaded || isDetecting}
        >
          <Camera className="mr-2 h-4 w-4" />
          {isDetecting ? "Detecting..." : "Start Realtime Detection"}
        </Button>
      </div>
    </div>
  );
};

export default RealtimeFaceDetection;