face detection integrated in rtsp stream

app/globals.css

@@ -1,3 +1,66 @@
 @tailwind base;
 @tailwind components;
 @tailwind utilities;
+@layer base {
+  :root {
+    --background: 0 0% 100%;
+    --foreground: 0 0% 3.9%;
+    --card: 0 0% 100%;
+    --card-foreground: 0 0% 3.9%;
+    --popover: 0 0% 100%;
+    --popover-foreground: 0 0% 3.9%;
+    --primary: 0 0% 9%;
+    --primary-foreground: 0 0% 98%;
+    --secondary: 0 0% 96.1%;
+    --secondary-foreground: 0 0% 9%;
+    --muted: 0 0% 96.1%;
+    --muted-foreground: 0 0% 45.1%;
+    --accent: 0 0% 96.1%;
+    --accent-foreground: 0 0% 9%;
+    --destructive: 0 84.2% 60.2%;
+    --destructive-foreground: 0 0% 98%;
+    --border: 0 0% 89.8%;
+    --input: 0 0% 89.8%;
+    --ring: 0 0% 3.9%;
+    --chart-1: 12 76% 61%;
+    --chart-2: 173 58% 39%;
+    --chart-3: 197 37% 24%;
+    --chart-4: 43 74% 66%;
+    --chart-5: 27 87% 67%;
+    --radius: 0.5rem
+  }
+  .dark {
+    --background: 0 0% 3.9%;
+    --foreground: 0 0% 98%;
+    --card: 0 0% 3.9%;
+    --card-foreground: 0 0% 98%;
+    --popover: 0 0% 3.9%;
+    --popover-foreground: 0 0% 98%;
+    --primary: 0 0% 98%;
+    --primary-foreground: 0 0% 9%;
+    --secondary: 0 0% 14.9%;
+    --secondary-foreground: 0 0% 98%;
+    --muted: 0 0% 14.9%;
+    --muted-foreground: 0 0% 63.9%;
+    --accent: 0 0% 14.9%;
+    --accent-foreground: 0 0% 98%;
+    --destructive: 0 62.8% 30.6%;
+    --destructive-foreground: 0 0% 98%;
+    --border: 0 0% 14.9%;
+    --input: 0 0% 14.9%;
+    --ring: 0 0% 83.1%;
+    --chart-1: 220 70% 50%;
+    --chart-2: 160 60% 45%;
+    --chart-3: 30 80% 55%;
+    --chart-4: 280 65% 60%;
+    --chart-5: 340 75% 55%
+  }
+}
+@layer base {
+  * {
+    @apply border-border;
+  }
+  body {
+    @apply bg-background text-foreground;
+  }
+}

app/layout.tsx

@@ -1,6 +1,7 @@
 import type { Metadata } from "next";
 import { Geist, Geist_Mono } from "next/font/google";
 import "./globals.css";
+import { Toaster } from "@/components/ui/toaster";
 
 const geistSans = Geist({
   variable: "--font-geist-sans",
@@ -28,6 +29,7 @@ export default function RootLayout({
         className={`${geistSans.variable} ${geistMono.variable} antialiased`}
       >
         {children}
+        <Toaster />
       </body>
     </html>
   );

components.json

@@ -0,0 +1,21 @@
+{
+  "$schema": "https://ui.shadcn.com/schema.json",
+  "style": "default",
+  "rsc": true,
+  "tsx": true,
+  "tailwind": {
+    "config": "tailwind.config.ts",
+    "css": "app/globals.css",
+    "baseColor": "neutral",
+    "cssVariables": true,
+    "prefix": ""
+  },
+  "aliases": {
+    "components": "@/components",
+    "utils": "@/lib/utils",
+    "ui": "@/components/ui",
+    "lib": "@/lib",
+    "hooks": "@/hooks"
+  },
+  "iconLibrary": "lucide"
+}

components/MainForm.tsx

@@ -2,10 +2,21 @@
 import React, { useState } from "react";
 import Register from "./register/Register";
 import Search from "./search/Search";
-import "./MainForm.css"; // Import CSS for styling
+import "./MainForm.css";
+import FaceLiveness from "./faceLivelinessCheck/FaceLivelinessCheck";
+import FaceMovementDetection from "./faceMovementDetection/FaceMovementDetection";
+import RealtimeCount from "./realtimeCount/RealtimeCount";
+import RealtimeDetection from "./realtimeDetection/RealtimeDetection";
 
 const MainForm: React.FC = () => {
-  const [activeTab, setActiveTab] = useState<"register" | "search">("register");
+  const [activeTab, setActiveTab] = useState<
+    | "register"
+    | "search"
+    | "realtime"
+    | "liveliness"
+    | "realtime-count"
+    | "facemovement"
+  >("register");
 
   return (
     <div className="main-container">
@@ -22,10 +33,42 @@ const MainForm: React.FC = () => {
         >
           Search
         </button>
+        <button
+          className={`tab-button ${activeTab === "realtime" ? "active" : ""}`}
+          onClick={() => setActiveTab("realtime")}
+        >
+          Realtime Detection
+        </button>
+        {/* <button
+          className={`tab-button ${activeTab === "liveliness" ? "active" : ""}`}
+          onClick={() => setActiveTab("liveliness")}
+        >
+          Liveliness Detection
+        </button> */}
+        <button
+          className={`tab-button ${
+            activeTab === "realtime-count" ? "active" : ""
+          }`}
+          onClick={() => setActiveTab("realtime-count")}
+        >
+          Realtime Count
+        </button>
+        <button
+          className={`tab-button ${
+            activeTab === "facemovement" ? "active" : ""
+          }`}
+          onClick={() => setActiveTab("facemovement")}
+        >
+          Face Movement Detection
+        </button>
       </div>
       <div className="tab-content">
         {activeTab === "register" && <Register />}
         {activeTab === "search" && <Search />}
+        {activeTab === "realtime" && <RealtimeDetection />}
+        {activeTab === "liveliness" && <FaceLiveness />}
+        {activeTab === "realtime-count" && <RealtimeCount />}
+        {activeTab === "facemovement" && <FaceMovementDetection />}
       </div>
     </div>
   );

components/faceLivelinessCheck/FaceLivelinessCheck.tsx

@@ -0,0 +1,192 @@
+/* eslint-disable @typescript-eslint/no-explicit-any */
+"use client";
+
+import { useState, useRef, useEffect } from "react";
+import Webcam from "react-webcam";
+import { Card, CardContent } from "@/components/ui/card";
+import { Loader2 } from "lucide-react";
+import { useToast } from "@/hooks/use-toast";
+import * as faceapi from "face-api.js";
+
+export default function FaceLiveness() {
+  const webcamRef = useRef<Webcam>(null);
+  const [isModelLoading, setIsModelLoading] = useState(true);
+  const [isProcessing, setIsProcessing] = useState(false);
+  const [previousExpressions, setPreviousExpressions] = useState<any>(null);
+  const processingTimeoutRef = useRef<NodeJS.Timeout | null>(null);
+  const { toast } = useToast();
+
+  useEffect(() => {
+    const loadModels = async () => {
+      try {
+        const MODEL_URL =
+          "https://justadudewhohacks.github.io/face-api.js/models";
+        await Promise.all([
+          faceapi.nets.tinyFaceDetector.loadFromUri(MODEL_URL),
+          faceapi.nets.faceLandmark68Net.loadFromUri(MODEL_URL),
+          faceapi.nets.faceExpressionNet.loadFromUri(MODEL_URL),
+        ]);
+        setIsModelLoading(false);
+      } catch (error) {
+        console.error("Error loading models:", error);
+        toast({
+          title: "Error",
+          description:
+            "Failed to load face detection models. Please refresh the page.",
+          variant: "destructive",
+        });
+      }
+    };
+    loadModels();
+  }, [toast]);
+
+  const checkLiveness = (expressions: any, landmarks: any) => {
+    if (!previousExpressions) {
+      setPreviousExpressions(expressions);
+      return false;
+    }
+
+    // Check for expression changes
+    const expressionThreshold = 0.1;
+    let hasExpressionChange = false;
+    for (const expression in expressions) {
+      const diff = Math.abs(
+        expressions[expression] - previousExpressions[expression]
+      );
+      if (diff > expressionThreshold) {
+        hasExpressionChange = true;
+        break;
+      }
+    }
+
+    // Check for natural facial movement using landmarks
+    const eyeBlinkDetected = detectEyeBlink(landmarks);
+
+    setPreviousExpressions(expressions);
+    return hasExpressionChange || eyeBlinkDetected;
+  };
+
+  const detectEyeBlink = (landmarks: any) => {
+    const leftEye = landmarks.getLeftEye();
+    const rightEye = landmarks.getRightEye();
+
+    // Calculate eye aspect ratio
+    const leftEAR = getEyeAspectRatio(leftEye);
+    const rightEAR = getEyeAspectRatio(rightEye);
+
+    // If either eye is closed (low aspect ratio), consider it a blink
+    const blinkThreshold = 0.2;
+    return leftEAR < blinkThreshold || rightEAR < blinkThreshold;
+  };
+
+  const getEyeAspectRatio = (eye: any) => {
+    // Calculate the eye aspect ratio using the landmark points
+    const height1 = distance(eye[1], eye[5]);
+    const height2 = distance(eye[2], eye[4]);
+    const width = distance(eye[0], eye[3]);
+    return (height1 + height2) / (2.0 * width);
+  };
+
+  const distance = (point1: any, point2: any) => {
+    return Math.sqrt(
+      Math.pow(point1.x - point2.x, 2) + Math.pow(point1.y - point2.y, 2)
+    );
+  };
+
+  useEffect(() => {
+    const processFrame = async () => {
+      if (!webcamRef.current || isProcessing || isModelLoading) return;
+
+      setIsProcessing(true);
+      try {
+        const imageSrc = webcamRef.current.getScreenshot();
+        if (!imageSrc) return;
+
+        const img = new Image();
+        img.src = imageSrc;
+        await new Promise((resolve) => (img.onload = resolve));
+
+        const detections = await faceapi
+          .detectAllFaces(img, new faceapi.TinyFaceDetectorOptions())
+          .withFaceLandmarks()
+          .withFaceExpressions();
+
+        if (detections.length > 0) {
+          // Process each detected face with high confidence
+          detections
+            .filter((detection) => detection.detection.score > 0.7)
+            .forEach((detection) => {
+              const isLive = checkLiveness(
+                detection.expressions,
+                detection.landmarks
+              );
+              if (isLive) {
+                toast({
+                  title: "Liveness Detected",
+                  description: "Real face detected with natural movements",
+                });
+              } else {
+                toast({
+                  title: "Liveness Check",
+                  description: "Please move or blink naturally",
+                  variant: "destructive",
+                });
+              }
+            });
+        }
+      } catch (error) {
+        console.error("Processing error:", error);
+      } finally {
+        setIsProcessing(false);
+        // Schedule next frame processing
+        processingTimeoutRef.current = setTimeout(processFrame, 1000); // Process every second
+      }
+    };
+
+    if (!isModelLoading) {
+      processFrame();
+    }
+
+    return () => {
+      if (processingTimeoutRef.current) {
+        clearTimeout(processingTimeoutRef.current);
+      }
+    };
+    // eslint-disable-next-line react-hooks/exhaustive-deps
+  }, [isModelLoading, isProcessing, toast]);
+
+  if (isModelLoading) {
+    return (
+      <Card>
+        <CardContent className="p-6 text-center">
+          <Loader2 className="h-8 w-8 animate-spin mx-auto" />
+          <p className="mt-2">Loading face detection models...</p>
+        </CardContent>
+      </Card>
+    );
+  }
+
+  return (
+    <Card>
+      <CardContent className="p-6">
+        <div className="space-y-6">
+          <div className="relative rounded-lg overflow-hidden bg-black">
+            <Webcam
+              ref={webcamRef}
+              screenshotFormat="image/jpeg"
+              className="w-full"
+              videoConstraints={{
+                width: 640,
+                height: 480,
+                facingMode: "user",
+              }}
+            />
+          </div>
+          <p className="text-center text-sm text-muted-foreground">
+            Move your face naturally or blink to verify liveness
+          </p>
+        </div>
+      </CardContent>
+    </Card>
+  );
+}

components/faceMovementDetection/FaceMovementDetection.css

@@ -0,0 +1,28 @@
+.video-container {
+  position: relative;
+  width: 100%;
+  max-width: 768px;
+  margin: 0 auto;
+}
+
+.webcam {
+  width: 100%;
+  border-radius: 8px;
+}
+
+.alert-warning {
+  display: flex;
+  align-items: center;
+  background-color: #facc15; /* Yellow */
+  color: #000;
+  padding: 12px;
+  border-radius: 6px;
+  margin-top: 16px;
+  font-weight: bold;
+}
+
+.button-container {
+  margin-top: 24px;
+  display: flex;
+  justify-content: center;
+}

components/faceMovementDetection/FaceMovementDetection.tsx

@@ -0,0 +1,112 @@
+"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 { AlertTriangle, Camera } from "lucide-react";
+import { useToast } from "@/hooks/use-toast";
+
+const MODEL_URL = "https://cdn.jsdelivr.net/npm/@vladmandic/face-api/model";
+const CHECK_INTERVAL = 500;
+
+const FaceMovementDetection = () => {
+  const webcamRef = useRef<Webcam>(null);
+  const [isModelLoaded, setIsModelLoaded] = useState(false);
+  const [isDetecting, setIsDetecting] = useState(false);
+  const prevBoxSizeRef = useRef<number | null>(null);
+  const [movingForward, setMovingForward] = useState(false);
+  const { toast } = useToast();
+
+  useEffect(() => {
+    const loadModels = async () => {
+      try {
+        await Promise.all([
+          faceapi.nets.tinyFaceDetector.loadFromUri(MODEL_URL),
+          faceapi.nets.faceLandmark68Net.loadFromUri(MODEL_URL),
+        ]);
+        setIsModelLoaded(true);
+        toast({
+          title: "Models Loaded",
+          description: "Face detection models ready.",
+        });
+      } catch (error) {
+        console.error("Error loading models:", error);
+        toast({
+          title: "Error",
+          description: "Failed to load models.",
+          variant: "destructive",
+        });
+      }
+    };
+
+    loadModels();
+  }, [toast]);
+
+  const detectMovement = async () => {
+    if (!webcamRef.current?.video) return;
+
+    const video = webcamRef.current.video;
+    const detections = await faceapi.detectSingleFace(
+      video,
+      new faceapi.TinyFaceDetectorOptions()
+    );
+
+    console.log("prevBoxSizeRef:", prevBoxSizeRef.current);
+
+    if (detections) {
+      const { width, height } = detections.box;
+      const currentBoxSize = width * height;
+      console.log("currentBoxSize:", currentBoxSize);
+
+      if (prevBoxSizeRef.current !== null) {
+        const sizeIncrease = currentBoxSize - prevBoxSizeRef.current;
+        console.log("Size Increase:", sizeIncrease);
+
+        if (sizeIncrease > 3000) {
+          setMovingForward(true);
+          //   toast({
+          //     title: "Movement Detected",
+          //     description: "The person is moving closer!",
+          //     variant: "destructive",
+          //   });
+        } else {
+          setMovingForward(false);
+        }
+      }
+
+      prevBoxSizeRef.current = currentBoxSize; // Update ref directly
+    }
+  };
+
+  const startDetection = () => {
+    if (!isModelLoaded) return;
+    setIsDetecting(true);
+    setInterval(detectMovement, CHECK_INTERVAL);
+  };
+
+  return (
+    <div className="max-w-3xl mx-auto">
+      <div className="relative">
+        <Webcam ref={webcamRef} mirrored className="w-full rounded-lg" />
+      </div>
+      {movingForward && (
+        <div className="mt-4 p-3 bg-yellow-300 text-black rounded-md flex items-center">
+          <AlertTriangle className="mr-2 h-5 w-5" />
+          Person is moving forward!
+        </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 Movement Detection"}
+        </Button>
+      </div>
+    </div>
+  );
+};
+
+export default FaceMovementDetection;

components/realtimeCount/RealtimeCount.tsx

@@ -0,0 +1,39 @@
+"use client";
+import React, { useState } from "react";
+import RegisterFaceCount from "./registerFaceCount/RegisterFaceCount";
+import HeadCount from "./headCount/HeadCount";
+
+const RealtimeCount: React.FC = () => {
+  const [activeTab, setActiveTab] = useState<
+    "register-face-count" | "get-face-count"
+  >("register-face-count");
+
+  return (
+    <div className="main-container">
+      <div className="tabs">
+        <button
+          className={`tab-button ${
+            activeTab === "register-face-count" ? "active" : ""
+          }`}
+          onClick={() => setActiveTab("register-face-count")}
+        >
+          Register Face Count
+        </button>
+        <button
+          className={`tab-button ${
+            activeTab === "get-face-count" ? "active" : ""
+          }`}
+          onClick={() => setActiveTab("get-face-count")}
+        >
+          Get Face Count
+        </button>
+      </div>
+      <div className="tab-content">
+        {activeTab === "register-face-count" && <RegisterFaceCount />}
+        {activeTab === "get-face-count" && <HeadCount />}
+      </div>
+    </div>
+  );
+};
+
+export default RealtimeCount;

components/realtimeCount/headCount/HeadCount.css

@@ -0,0 +1,120 @@
+.headcount-container {
+  max-width: 500px;
+  margin: 0 auto;
+  padding: 20px;
+  font-family: Arial, sans-serif;
+  background-color: #f9f9f9;
+  border-radius: 8px;
+  box-shadow: 0 4px 8px rgba(0, 0, 0, 0.1);
+}
+
+.heading {
+  text-align: center;
+  color: #333;
+  margin-bottom: 20px;
+}
+
+.form {
+  display: flex;
+  flex-direction: column;
+  gap: 15px;
+}
+
+.input-group {
+  display: flex;
+  flex-direction: column;
+  gap: 5px;
+}
+
+.label {
+  font-size: 14px;
+  color: #555;
+}
+
+.input {
+  padding: 10px;
+  font-size: 16px;
+  border-radius: 4px;
+  border: 1px solid #ccc;
+  outline: none;
+}
+
+.button {
+  padding: 10px;
+  font-size: 16px;
+  background-color: #007bff;
+  color: #fff;
+  border: none;
+  border-radius: 4px;
+  cursor: pointer;
+  transition: background-color 0.3s ease;
+}
+
+.button:disabled {
+  background-color: #ccc;
+  cursor: not-allowed;
+}
+
+.error {
+  color: red;
+  text-align: center;
+  margin-top: 10px;
+}
+
+.names-container {
+  margin-top: 20px;
+}
+
+.sub-heading {
+  color: #333;
+  margin-bottom: 10px;
+}
+
+.list {
+  list-style: none;
+  padding: 0;
+}
+
+.list-item {
+  padding: 10px;
+  background-color: #fff;
+  border: 1px solid #ddd;
+  border-radius: 4px;
+  margin-bottom: 5px;
+  font-size: 14px;
+}
+.daily-counts-container {
+  margin-top: 20px;
+  padding: 15px;
+  border-radius: 8px;
+  background: #f8f9fa;
+  box-shadow: 0px 4px 6px rgba(0, 0, 0, 0.1);
+}
+
+.sub-heading {
+  font-size: 1.5rem;
+  margin-bottom: 10px;
+  color: #333;
+}
+
+.list {
+  list-style-type: none;
+  padding: 0;
+}
+
+.list-item {
+  font-size: 1.1rem;
+  padding: 8px 0;
+  border-bottom: 1px solid #ddd;
+  display: flex;
+  justify-content: space-between;
+}
+
+.date {
+  font-weight: bold;
+  color: #007bff;
+}
+
+.count {
+  color: #555;
+}

components/realtimeCount/headCount/HeadCount.tsx

@@ -0,0 +1,183 @@
+import React, { useState } from "react";
+import "./HeadCount.css";
+
+interface ApiResponse {
+  total_unique_faces: number;
+  daily_counts: { date: string; unique_faces: number }[];
+}
+
+interface DateTimeRange {
+  date: string;
+  time: string;
+}
+
+const HeadCount: React.FC = () => {
+  const [from, setFrom] = useState<DateTimeRange>({ date: "", time: "" });
+  const [to, setTo] = useState<DateTimeRange>({ date: "", time: "" });
+  const [count, setCount] = useState<number | null>(null);
+  const [loading, setLoading] = useState<boolean>(false);
+  const [error, setError] = useState<string | null>(null);
+  const [dailyCounts, setDailyCounts] = useState<
+    { date: string; count: number }[]
+  >([]);
+
+  const handleInputChange = (
+    e: React.ChangeEvent<HTMLInputElement>,
+    field: "from" | "to"
+  ) => {
+    const { name, value } = e.target;
+    if (field === "from") {
+      setFrom((prev) => ({ ...prev, [name]: value }));
+    } else {
+      setTo((prev) => ({ ...prev, [name]: value }));
+    }
+  };
+
+  const formatDateTime = (date: string, time: string) => {
+    return `${date}T${time}:00+00:00`;
+  };
+
+  const handleSubmit = async (e: React.FormEvent) => {
+    e.preventDefault();
+
+    if (!from.date || !from.time || !to.date || !to.time) {
+      setError("Please fill in all date and time fields.");
+      return;
+    }
+
+    setLoading(true);
+    setError(null);
+
+    const start = formatDateTime(from.date, from.time);
+    const end = formatDateTime(to.date, to.time);
+    console.log(start, end);
+    try {
+      const response = await fetch(
+        `${
+          process.env.NEXT_PUBLIC_BASE_URL
+        }/face/headcount?start_time=${encodeURIComponent(
+          start
+        )}&end_time=${encodeURIComponent(end)}`,
+        {
+          method: "GET",
+          headers: {
+            "Content-Type": "application/json",
+          },
+        }
+      );
+
+      if (!response.ok) {
+        throw new Error("Failed to fetch data");
+      }
+
+      const data: ApiResponse = await response.json();
+      setCount(data.total_unique_faces);
+      if (data?.daily_counts) {
+        setDailyCounts(
+          data.daily_counts.map((d) => ({
+            date: d.date,
+            count: d.unique_faces,
+          }))
+        );
+      }
+    } catch (err) {
+      setError("An error occurred while fetching data.");
+      console.error(err);
+    } finally {
+      setLoading(false);
+    }
+  };
+
+  return (
+    <div className="headcount-container">
+      <h1 className="heading">Head Count</h1>
+      <form onSubmit={handleSubmit} className="form">
+        <div className="input-group">
+          <label htmlFor="from-date" className="label">
+            From Date:
+          </label>
+          <input
+            type="date"
+            id="from-date"
+            name="date"
+            value={from.date}
+            onChange={(e) => handleInputChange(e, "from")}
+            required
+            className="input"
+          />
+        </div>
+        <div className="input-group">
+          <label htmlFor="from-time" className="label">
+            From Time:
+          </label>
+          <input
+            type="time"
+            id="from-time"
+            name="time"
+            value={from.time}
+            onChange={(e) => handleInputChange(e, "from")}
+            required
+            className="input"
+          />
+        </div>
+
+        <div className="input-group">
+          <label htmlFor="to-date" className="label">
+            To Date:
+          </label>
+          <input
+            type="date"
+            id="to-date"
+            name="date"
+            value={to.date}
+            onChange={(e) => handleInputChange(e, "to")}
+            required
+            className="input"
+          />
+        </div>
+        <div className="input-group">
+          <label htmlFor="to-time" className="label">
+            To Time:
+          </label>
+          <input
+            type="time"
+            id="to-time"
+            name="time"
+            value={to.time}
+            onChange={(e) => handleInputChange(e, "to")}
+            required
+            className="input"
+          />
+        </div>
+
+        <button type="submit" disabled={loading} className="button">
+          {loading ? "Submitting..." : "Submit"}
+        </button>
+      </form>
+
+      {error && <p className="error">{error}</p>}
+
+      {count && (
+        <div className="names-container">
+          <h2 className="sub-heading">Total Unique Face Count:</h2>
+          <ul className="list">{count}</ul>
+        </div>
+      )}
+      {dailyCounts?.length > 0 && (
+        <div className="daily-counts-container">
+          <h2 className="sub-heading">Daily Counts:</h2>
+          <ul className="list">
+            {dailyCounts.map((item, index) => (
+              <li key={index} className="list-item">
+                <span className="date">{item.date}:</span>
+                <span className="count"> {item.count}</span>
+              </li>
+            ))}
+          </ul>
+        </div>
+      )}
+    </div>
+  );
+};
+
+export default HeadCount;

components/realtimeCount/registerFaceCount/RegisterFaceCount.tsx

@@ -0,0 +1,166 @@
+import React from "react";
+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 PADDING = 60;
+
+const RegisterFaceCount = () => {
+  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 extractFaceWithPadding = (
+    video: HTMLVideoElement,
+    box: faceapi.Box
+  ): HTMLCanvasElement => {
+    const canvas = document.createElement("canvas");
+    const context = canvas.getContext("2d");
+
+    // Calculate padded dimensions
+    const x = Math.max(0, box.x - PADDING);
+    const y = Math.max(0, box.y - PADDING);
+    const width = Math.min(video.videoWidth - x, box.width + 2 * PADDING);
+    const height = Math.min(video.videoHeight - y, box.height + 2 * PADDING);
+
+    canvas.width = width;
+    canvas.height = height;
+
+    if (context) {
+      context.drawImage(video, x, y, width, height, 0, 0, width, height);
+    }
+
+    return canvas;
+  };
+
+  const detectFace = async () => {
+    if (!webcamRef.current?.video || !canvasRef.current) return;
+
+    const video = webcamRef.current.video;
+    const canvas = canvasRef.current;
+    const context = canvas.getContext("2d");
+
+    if (!context) return;
+
+    canvas.width = video.videoWidth;
+    canvas.height = video.videoHeight;
+    context.clearRect(0, 0, canvas.width, canvas.height);
+    context.translate(canvas.width, 0);
+    context.scale(-1, 1);
+
+    const detections = await faceapi
+      .detectAllFaces(video, new faceapi.TinyFaceDetectorOptions())
+      .withFaceLandmarks()
+      .withFaceDescriptors();
+
+    if (detections.length > 0) {
+      const highConfidenceDetections = detections.filter(
+        (detection) => detection.detection.score > 0.5
+      );
+
+      for (const detection of highConfidenceDetections) {
+        const { box } = detection.detection;
+        context.strokeStyle = "#00FF00";
+        context.lineWidth = 2;
+        context.strokeRect(box.x, box.y, box.width, box.height);
+        context.save();
+        context.scale(-1, 1);
+        context.fillStyle = "#00FF00";
+        context.font = "16px Arial";
+        context.fillText(
+          `Confidence: ${Math.round(detection.detection.score * 100)}%`,
+          -box.x - box.width,
+          box.y - 5
+        );
+        context.restore();
+
+        const faceCanvas = extractFaceWithPadding(video, box);
+        faceCanvas.toBlob(
+          (blob) => {
+            if (blob) sendFaceDataToAPI(blob);
+          },
+          "image/jpeg",
+          0.95
+        );
+      }
+    }
+  };
+
+  const sendFaceDataToAPI = async (imageBlob: Blob) => {
+    try {
+      const formData = new FormData();
+      formData.append("image", imageBlob, "face.jpg");
+
+      const response = await fetch(
+        `${process.env.NEXT_PUBLIC_BASE_URL}/face/search`,
+        {
+          method: "POST",
+          body: formData,
+        }
+      );
+
+      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);
+    setInterval(detectFace, 1000);
+  };
+  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 RegisterFaceCount;

components/realtimeDetection/RealtimeDetection.tsx

@@ -0,0 +1,33 @@
+"use client";
+import React, { useState } from "react";
+import WebcamDetection from "./webcam/Webcam";
+import RtspStream from "./rtspStream/RtspStream";
+
+const RealtimeDetection: React.FC = () => {
+  const [activeTab, setActiveTab] = useState<"webcam" | "rtsp">("webcam");
+
+  return (
+    <div className="main-container">
+      <div className="tabs">
+        <button
+          className={`tab-button ${activeTab === "webcam" ? "active" : ""}`}
+          onClick={() => setActiveTab("webcam")}
+        >
+          Webcam
+        </button>
+        <button
+          className={`tab-button ${activeTab === "rtsp" ? "active" : ""}`}
+          onClick={() => setActiveTab("rtsp")}
+        >
+          RTSP
+        </button>
+      </div>
+      <div className="tab-content">
+        {activeTab === "webcam" && <WebcamDetection />}
+        {activeTab === "rtsp" && <RtspStream />}
+      </div>
+    </div>
+  );
+};
+
+export default RealtimeDetection;

components/realtimeDetection/rtspStream/RtspStream.tsx

@@ -0,0 +1,270 @@
+import React, { useState, useEffect, useRef } from "react";
+import Hls from "hls.js";
+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 PADDING = 60;
+const API_URL = "http://localhost:8081/start";
+
+const RtspStream: React.FC = () => {
+  const [rtspUrl, setRtspUrl] = useState<string>("");
+  const [cameraName, setCameraName] = useState<string>("");
+  const [m3u8Url, setM3u8Url] = useState<string | null>(null);
+  const [loading, setLoading] = useState<boolean>(false);
+  const [isModelLoaded, setIsModelLoaded] = useState(false);
+  const [isDetecting, setIsDetecting] = useState(false);
+  const videoRef = useRef<HTMLVideoElement | null>(null);
+  const canvasRef = useRef<HTMLCanvasElement>(null);
+  const detectionIntervalRef = useRef<ReturnType<typeof setInterval> | null>(
+    null
+  );
+  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]);
+
+  useEffect(() => {
+    if (m3u8Url && videoRef.current) {
+      if (Hls.isSupported()) {
+        const hls = new Hls();
+        hls.loadSource(m3u8Url);
+        hls.attachMedia(videoRef.current);
+      } else if (
+        videoRef.current.canPlayType("application/vnd.apple.mpegurl")
+      ) {
+        videoRef.current.src = m3u8Url;
+      }
+    }
+  }, [m3u8Url]);
+
+  const extractFaceWithPadding = (
+    video: HTMLVideoElement,
+    box: faceapi.Box
+  ): HTMLCanvasElement => {
+    const canvas = document.createElement("canvas");
+    const context = canvas.getContext("2d");
+
+    const x = Math.max(0, box.x - PADDING);
+    const y = Math.max(0, box.y - PADDING);
+    const width = Math.min(video.videoWidth - x, box.width + 2 * PADDING);
+    const height = Math.min(video.videoHeight - y, box.height + 2 * PADDING);
+
+    canvas.width = width;
+    canvas.height = height;
+
+    if (context) {
+      context.drawImage(video, x, y, width, height, 0, 0, width, height);
+    }
+
+    return canvas;
+  };
+
+  const detectFace = async () => {
+    if (!videoRef.current || !canvasRef.current || !videoRef.current.videoWidth)
+      return;
+
+    const video = videoRef.current;
+    const canvas = canvasRef.current;
+    const context = canvas.getContext("2d");
+
+    if (!context) return;
+
+    canvas.width = video.videoWidth;
+    canvas.height = video.videoHeight;
+    context.clearRect(0, 0, canvas.width, canvas.height);
+
+    const detections = await faceapi
+      .detectAllFaces(video, new faceapi.TinyFaceDetectorOptions())
+      .withFaceLandmarks()
+      .withFaceDescriptors();
+
+    if (detections.length > 0) {
+      const highConfidenceDetections = detections.filter(
+        (detection) => detection.detection.score > 0.5
+      );
+
+      for (const detection of highConfidenceDetections) {
+        const { box } = detection.detection;
+        context.strokeStyle = "#00FF00";
+        context.lineWidth = 2;
+        context.strokeRect(box.x, box.y, box.width, box.height);
+        context.fillStyle = "#00FF00";
+        context.font = "16px Arial";
+        context.fillText(
+          `Confidence: ${Math.round(detection.detection.score * 100)}%`,
+          box.x,
+          box.y - 5
+        );
+
+        const faceCanvas = extractFaceWithPadding(video, box);
+        faceCanvas.toBlob(
+          (blob) => {
+            if (blob) sendFaceDataToAPI(blob);
+          },
+          "image/jpeg",
+          0.95
+        );
+      }
+    }
+  };
+
+  const sendFaceDataToAPI = async (imageBlob: Blob) => {
+    try {
+      const formData = new FormData();
+      formData.append("image", imageBlob, "face.jpg");
+
+      const response = await fetch(
+        `${process.env.NEXT_PUBLIC_BASE_URL}/search`,
+        {
+          method: "POST",
+          body: formData,
+        }
+      );
+
+      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 || !videoRef.current) return;
+    console.log("Starting detection...");
+    setIsDetecting(true);
+    detectionIntervalRef.current = setInterval(detectFace, 1000);
+  };
+
+  const stopDetection = () => {
+    if (detectionIntervalRef.current) {
+      clearInterval(detectionIntervalRef.current);
+    }
+    setIsDetecting(false);
+    if (canvasRef.current) {
+      const context = canvasRef.current.getContext("2d");
+      if (context) {
+        context.clearRect(
+          0,
+          0,
+          canvasRef.current.width,
+          canvasRef.current.height
+        );
+      }
+    }
+  };
+
+  const handleSubmit = async (e: React.FormEvent) => {
+    e.preventDefault();
+    setLoading(true);
+    stopDetection(); // Stop any ongoing detection
+
+    try {
+      const response = await fetch(API_URL, {
+        method: "POST",
+        headers: { "Content-Type": "application/json" },
+        body: JSON.stringify({
+          uri: rtspUrl,
+          alias: cameraName,
+        }),
+      });
+
+      if (!response.ok) {
+        throw new Error("Failed to fetch stream URL");
+      }
+
+      const data = await response.json();
+      setM3u8Url(`http://localhost:8081${data?.uri}`);
+      console.log("isModelLoaded", isModelLoaded);
+      console.log("m3u8Url", m3u8Url);
+    } catch (error) {
+      console.error("Error fetching stream:", error);
+      toast({
+        title: "Error",
+        description: "Failed to load stream.",
+        variant: "destructive",
+      });
+    } finally {
+      setLoading(false);
+    }
+  };
+
+  return (
+    <div className="max-w-3xl mx-auto p-4">
+      <h2 className="text-2xl font-bold mb-4">
+        RTSP Stream with Face Detection
+      </h2>
+      <form onSubmit={handleSubmit} className="space-y-4 mb-6">
+        <input
+          type="text"
+          value={rtspUrl}
+          onChange={(e) => setRtspUrl(e.target.value)}
+          placeholder="Enter RTSP URL"
+          className="w-full p-2 border rounded"
+          required
+        />
+        <input
+          type="text"
+          value={cameraName}
+          onChange={(e) => setCameraName(e.target.value)}
+          placeholder="Enter Camera Name"
+          className="w-full p-2 border rounded"
+          required
+        />
+        <Button type="submit" disabled={loading} className="w-full">
+          {loading ? "Starting stream..." : "Start Stream"}
+        </Button>
+      </form>
+
+      {m3u8Url && !loading && (
+        <div className="relative">
+          <video
+            ref={videoRef}
+            controls
+            autoPlay
+            className="w-full rounded-lg"
+          />
+          <canvas
+            ref={canvasRef}
+            className="absolute top-0 left-0 w-full h-full z-0 pointer-events-none"
+          />
+
+          <div className="mt-4 flex justify-center">
+            <Button
+              onClick={isDetecting ? stopDetection : startDetection}
+              disabled={!isModelLoaded || !m3u8Url}
+            >
+              <Camera className="mr-2 h-4 w-4" />
+              {isDetecting ? "Stop Detection" : "Start Detection"}
+            </Button>
+          </div>
+        </div>
+      )}
+    </div>
+  );
+};
+
+export default RtspStream;

components/realtimeDetection/webcam/Webcam.tsx

@@ -0,0 +1,167 @@
+"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 PADDING = 60;
+
+const WebcamDetection = () => {
+  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 extractFaceWithPadding = (
+    video: HTMLVideoElement,
+    box: faceapi.Box
+  ): HTMLCanvasElement => {
+    const canvas = document.createElement("canvas");
+    const context = canvas.getContext("2d");
+
+    // Calculate padded dimensions
+    const x = Math.max(0, box.x - PADDING);
+    const y = Math.max(0, box.y - PADDING);
+    const width = Math.min(video.videoWidth - x, box.width + 2 * PADDING);
+    const height = Math.min(video.videoHeight - y, box.height + 2 * PADDING);
+
+    canvas.width = width;
+    canvas.height = height;
+
+    if (context) {
+      context.drawImage(video, x, y, width, height, 0, 0, width, height);
+    }
+
+    return canvas;
+  };
+
+  const detectFace = async () => {
+    if (!webcamRef.current?.video || !canvasRef.current) return;
+
+    const video = webcamRef.current.video;
+    const canvas = canvasRef.current;
+    const context = canvas.getContext("2d");
+
+    if (!context) return;
+
+    canvas.width = video.videoWidth;
+    canvas.height = video.videoHeight;
+    context.clearRect(0, 0, canvas.width, canvas.height);
+    context.translate(canvas.width, 0);
+    context.scale(-1, 1);
+
+    const detections = await faceapi
+      .detectAllFaces(video, new faceapi.TinyFaceDetectorOptions())
+      .withFaceLandmarks()
+      .withFaceDescriptors();
+
+    if (detections.length > 0) {
+      const highConfidenceDetections = detections.filter(
+        (detection) => detection.detection.score > 0.5
+      );
+
+      for (const detection of highConfidenceDetections) {
+        const { box } = detection.detection;
+        context.strokeStyle = "#00FF00";
+        context.lineWidth = 2;
+        context.strokeRect(box.x, box.y, box.width, box.height);
+        context.save();
+        context.scale(-1, 1);
+        context.fillStyle = "#00FF00";
+        context.font = "16px Arial";
+        context.fillText(
+          `Confidence: ${Math.round(detection.detection.score * 100)}%`,
+          -box.x - box.width,
+          box.y - 5
+        );
+        context.restore();
+
+        const faceCanvas = extractFaceWithPadding(video, box);
+        faceCanvas.toBlob(
+          (blob) => {
+            if (blob) sendFaceDataToAPI(blob);
+          },
+          "image/jpeg",
+          0.95
+        );
+      }
+    }
+  };
+
+  const sendFaceDataToAPI = async (imageBlob: Blob) => {
+    try {
+      const formData = new FormData();
+      formData.append("image", imageBlob, "face.jpg");
+
+      const response = await fetch(
+        `${process.env.NEXT_PUBLIC_BASE_URL}/search`,
+        {
+          method: "POST",
+          body: formData,
+        }
+      );
+
+      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);
+    setInterval(detectFace, 1000);
+  };
+  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 WebcamDetection;

components/realtimeFaceDetection/RealtimeFaceDetection.tsx

@@ -0,0 +1,167 @@
+"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 PADDING = 60;
+
+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 extractFaceWithPadding = (
+    video: HTMLVideoElement,
+    box: faceapi.Box
+  ): HTMLCanvasElement => {
+    const canvas = document.createElement("canvas");
+    const context = canvas.getContext("2d");
+
+    // Calculate padded dimensions
+    const x = Math.max(0, box.x - PADDING);
+    const y = Math.max(0, box.y - PADDING);
+    const width = Math.min(video.videoWidth - x, box.width + 2 * PADDING);
+    const height = Math.min(video.videoHeight - y, box.height + 2 * PADDING);
+
+    canvas.width = width;
+    canvas.height = height;
+
+    if (context) {
+      context.drawImage(video, x, y, width, height, 0, 0, width, height);
+    }
+
+    return canvas;
+  };
+
+  const detectFace = async () => {
+    if (!webcamRef.current?.video || !canvasRef.current) return;
+
+    const video = webcamRef.current.video;
+    const canvas = canvasRef.current;
+    const context = canvas.getContext("2d");
+
+    if (!context) return;
+
+    canvas.width = video.videoWidth;
+    canvas.height = video.videoHeight;
+    context.clearRect(0, 0, canvas.width, canvas.height);
+    context.translate(canvas.width, 0);
+    context.scale(-1, 1);
+
+    const detections = await faceapi
+      .detectAllFaces(video, new faceapi.TinyFaceDetectorOptions())
+      .withFaceLandmarks()
+      .withFaceDescriptors();
+
+    if (detections.length > 0) {
+      const highConfidenceDetections = detections.filter(
+        (detection) => detection.detection.score > 0.5
+      );
+
+      for (const detection of highConfidenceDetections) {
+        const { box } = detection.detection;
+        context.strokeStyle = "#00FF00";
+        context.lineWidth = 2;
+        context.strokeRect(box.x, box.y, box.width, box.height);
+        context.save();
+        context.scale(-1, 1);
+        context.fillStyle = "#00FF00";
+        context.font = "16px Arial";
+        context.fillText(
+          `Confidence: ${Math.round(detection.detection.score * 100)}%`,
+          -box.x - box.width,
+          box.y - 5
+        );
+        context.restore();
+
+        const faceCanvas = extractFaceWithPadding(video, box);
+        faceCanvas.toBlob(
+          (blob) => {
+            if (blob) sendFaceDataToAPI(blob);
+          },
+          "image/jpeg",
+          0.95
+        );
+      }
+    }
+  };
+
+  const sendFaceDataToAPI = async (imageBlob: Blob) => {
+    try {
+      const formData = new FormData();
+      formData.append("image", imageBlob, "face.jpg");
+
+      const response = await fetch(
+        `${process.env.NEXT_PUBLIC_BASE_URL}/search`,
+        {
+          method: "POST",
+          body: formData,
+        }
+      );
+
+      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);
+    setInterval(detectFace, 1000);
+  };
+  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;

components/register/Register.tsx

@@ -1,144 +1,8 @@
-// "use client";
-// import React, { useRef, useState, useEffect } from "react";
-// import "./Register.css"; // Import CSS for styling
-
-// const Register: React.FC = () => {
-//   const videoRef = useRef<HTMLVideoElement>(null);
-//   const canvasRef = useRef<HTMLCanvasElement>(null);
-//   const [name, setName] = useState<string>("");
-//   const [error, setError] = useState<string>(""); // State for error message
-
-//   // Automatically open the camera when the component mounts
-//   useEffect(() => {
-//     const openCamera = async () => {
-//       try {
-//         const stream = await navigator.mediaDevices.getUserMedia({
-//           video: true,
-//         });
-//         if (videoRef.current) {
-//           videoRef.current.srcObject = stream;
-//         }
-//       } catch (err) {
-//         console.error("Error accessing the camera", err);
-//       }
-//     };
-
-//     openCamera();
-//   }, []);
-
-//   // Capture image from video stream
-//   const captureImage = () => {
-//     if (!name.trim()) {
-//       setError("Name is required"); // Set error message if name is empty
-//       return;
-//     }
-
-//     const video = videoRef.current;
-//     const canvas = canvasRef.current;
-
-//     if (video && canvas) {
-//       const context = canvas.getContext("2d");
-//       if (context) {
-//         // Draw the current frame from the video on the canvas
-//         context.drawImage(video, 0, 0, canvas.width, canvas.height);
-//         canvas.toBlob((blob) => {
-//           if (blob) {
-//             const file = new File([blob], "captured-image.png", {
-//               type: "image/png",
-//             });
-//             callApi(name, file);
-//           }
-//         }, "image/png");
-//       }
-//     }
-//   };
-
-//   // Call API with name and image file
-//   const callApi = async (name: string, file: File) => {
-//     const formData = new FormData();
-//     formData.append("name", name);
-//     formData.append("image", file);
-
-//     try {
-//       const startTime = performance.now();
-//       const response = await fetch(
-//         `${process.env.NEXT_PUBLIC_BASE_URL}/register`,
-//         {
-//           method: "POST",
-//           body: formData,
-//         }
-//       );
-
-//       // End measuring time
-//       const endTime = performance.now();
-//       const totalTime = (endTime - startTime) / 1000; // Convert milliseconds to seconds
-
-//       if (response.ok) {
-//         const data = await response.json();
-//         console.log("API call successful");
-//         alert(
-//           `Message: ${data.message}\nName: ${
-//             data?.name
-//           }\nTime taken: ${totalTime.toFixed(2)} seconds`
-//         ); // Show success message with time taken
-//         setError(""); // Clear error message on success
-//       } else {
-//         console.error("API call failed");
-//         setError("Failed to register. Please try again."); // Set error message on failure
-//       }
-//     } catch (error) {
-//       console.error("Error calling API", error);
-//       setError("An error occurred. Please try again."); // Set error message on exception
-//     }
-//   };
-
-//   return (
-//     <div className="container">
-//       <h1 className="title">Face Capture Form</h1>
-//       <form className="form">
-//         <div className="form-group">
-//           <label htmlFor="name" className="label">
-//             Name:
-//           </label>
-//           <input
-//             id="name"
-//             type="text"
-//             value={name}
-//             onChange={(e) => {
-//               setName(e.target.value);
-//               setError(""); // Clear error message when user starts typing
-//             }}
-//             className="input"
-//             placeholder="Enter your name"
-//             required
-//           />
-//           {error && !name.trim() && (
-//             <p className="error-message">{error}</p> // Display error message if name is empty
-//           )}
-//         </div>
-//         <div className="video-container">
-//           <video ref={videoRef} autoPlay playsInline className="video" />
-//         </div>
-//         <button type="button" onClick={captureImage} className="capture-button">
-//           Capture Image
-//         </button>
-//         <canvas
-//           ref={canvasRef}
-//           style={{ display: "none" }}
-//           width="640"
-//           height="480"
-//         />
-//       </form>
-//     </div>
-//   );
-// };
-
-// export default Register;
-
-
 "use client";
 import React, { useRef, useState, useEffect } from "react";
 import "./Register.css"; // Import CSS for styling
+import { toast } from "@/hooks/use-toast";
+import { Button } from "../ui/button";
 
 const Register: React.FC = () => {
   const videoRef = useRef<HTMLVideoElement>(null);
@@ -235,18 +99,19 @@ const Register: React.FC = () => {
 
       if (response.ok) {
         const data = await response.json();
-        console.log("API call successful");
-        alert(
-          `Message: ${data.message}\nName: ${data?.name}\nTime taken: ${totalTime.toFixed(2)} seconds`
-        ); // Show success message with time taken
-        setError(""); // Clear error message on success
+        console.log("API call successful", totalTime);
+        toast({
+          title: data.message,
+          description: `Name: ${data?.name} `,
+        });
+        setError("");
       } else {
         console.error("API call failed");
-        setError("Failed to register. Please try again."); // Set error message on failure
+        setError("Failed to register. Please try again.");
       }
     } catch (error) {
       console.error("Error calling API", error);
-      setError("An error occurred. Please try again."); // Set error message on exception
+      setError("An error occurred. Please try again.");
     }
   };
 
@@ -275,15 +140,20 @@ const Register: React.FC = () => {
           )}
         </div>
 
-        <div className="video-container">
-          <video ref={videoRef} autoPlay playsInline className="video" />
-          <button
+        <div className="mb-5">
+          <video
+            ref={videoRef}
+            autoPlay
+            playsInline
+            className="w-full max-w-[500px] rounded-lg shadow-md scale-x-[-1]"
+          />
+          <Button
             type="button"
             onClick={captureImage}
             className="capture-button"
           >
             Capture Image
-          </button>
+          </Button>
         </div>
         <div>Or</div>
 

components/search/Search.tsx

@@ -1,105 +1,7 @@
-// import React, { useRef, useEffect } from "react";
-// import "./Search.css"; // Import CSS for styling
-
-// const Search: React.FC = () => {
-//   const videoRef = useRef<HTMLVideoElement>(null);
-//   const canvasRef = useRef<HTMLCanvasElement>(null);
-
-//   // Automatically open the camera when the component mounts
-//   useEffect(() => {
-//     const openCamera = async () => {
-//       try {
-//         const stream = await navigator.mediaDevices.getUserMedia({
-//           video: true,
-//         });
-//         if (videoRef.current) {
-//           videoRef.current.srcObject = stream;
-//         }
-//       } catch (err) {
-//         console.error("Error accessing the camera", err);
-//       }
-//     };
-
-//     openCamera();
-//   }, []);
-
-//   // Capture image from video stream
-//   const captureImage = () => {
-//     const video = videoRef.current;
-//     const canvas = canvasRef.current;
-
-//     if (video && canvas) {
-//       const context = canvas.getContext("2d");
-//       if (context) {
-//         context.drawImage(video, 0, 0, canvas.width, canvas.height);
-//         canvas.toBlob((blob) => {
-//           if (blob) {
-//             const file = new File([blob], "captured-image.png", {
-//               type: "image/png",
-//             });
-//             callApi(file);
-//           }
-//         }, "image/png");
-//       }
-//     }
-//   };
-
-//   // Call API with the captured image file
-//   const callApi = async (file: File) => {
-//     const formData = new FormData();
-//     formData.append("image", file); // Append the file to the form data
-
-//     try {
-//       const startTime = performance.now();
-//       const response = await fetch(
-//         `${process.env.NEXT_PUBLIC_BASE_URL}/search`,
-//         {
-//           method: "POST",
-//           body: formData, // Send the form data
-//         }
-//       );
-//       // End measuring time
-//       const endTime = performance.now();
-//       const totalTime = (endTime - startTime) / 1000;
-//       if (response.ok) {
-//         const data = await response.json();
-//         console.log("API call successful", data);
-//         alert(
-//           `Search result: ${data.message}\nName: ${
-//             data?.name
-//           }\nTime taken: ${totalTime.toFixed(2)} seconds`
-//         );
-//       } else {
-//         console.error("API call failed");
-//       }
-//     } catch (error) {
-//       console.error("Error calling API", error);
-//     }
-//   };
-
-//   return (
-//     <div className="search-container">
-//       <h2>Search</h2>
-//       <div className="video-container">
-//         <video ref={videoRef} autoPlay playsInline className="video" />
-//       </div>
-//       <button type="button" onClick={captureImage} className="capture-button">
-//         Capture Image
-//       </button>
-//       <canvas
-//         ref={canvasRef}
-//         style={{ display: "none" }}
-//         width="640"
-//         height="480"
-//       />
-//     </div>
-//   );
-// };
-
-// export default Search;
-
 import React, { useRef, useEffect } from "react";
 import "./Search.css"; // Import CSS for styling
+import { Button } from "../ui/button";
+import { toast } from "@/hooks/use-toast";
 
 const Search: React.FC = () => {
   const videoRef = useRef<HTMLVideoElement>(null);
@@ -169,11 +71,12 @@ const Search: React.FC = () => {
       if (response.ok) {
         const data = await response.json();
         console.log("API call successful", data);
-        alert(
-          `Search result: ${data.message}\nName: ${
-            data?.name
-          }\nTime taken: ${totalTime.toFixed(2)} seconds`
-        );
+        toast({
+          title: data.message,
+          description: `Name: ${data?.name}\n\nTime taken: ${totalTime.toFixed(
+            2
+          )} seconds`,
+        });
       } else {
         console.error("API call failed");
       }
@@ -188,9 +91,9 @@ const Search: React.FC = () => {
       <div className="video-container">
         <video ref={videoRef} autoPlay playsInline className="video" />
       </div>
-      <button type="button" onClick={captureImage} className="capture-button">
+      <Button type="button" onClick={captureImage} className="capture-button">
         Capture Image
-      </button>
+      </Button>
       <div>Or</div>
       <div className="upload-container">
         <label htmlFor="upload" className="upload-label">

components/ui/button.tsx

@@ -0,0 +1,56 @@
+import * as React from "react"
+import { Slot } from "@radix-ui/react-slot"
+import { cva, type VariantProps } from "class-variance-authority"
+
+import { cn } from "@/lib/utils"
+
+const buttonVariants = cva(
+  "inline-flex items-center justify-center gap-2 whitespace-nowrap rounded-md text-sm font-medium ring-offset-background transition-colors focus-visible:outline-none focus-visible:ring-2 focus-visible:ring-ring focus-visible:ring-offset-2 disabled:pointer-events-none disabled:opacity-50 [&_svg]:pointer-events-none [&_svg]:size-4 [&_svg]:shrink-0",
+  {
+    variants: {
+      variant: {
+        default: "bg-primary text-primary-foreground hover:bg-primary/90",
+        destructive:
+          "bg-destructive text-destructive-foreground hover:bg-destructive/90",
+        outline:
+          "border border-input bg-background hover:bg-accent hover:text-accent-foreground",
+        secondary:
+          "bg-secondary text-secondary-foreground hover:bg-secondary/80",
+        ghost: "hover:bg-accent hover:text-accent-foreground",
+        link: "text-primary underline-offset-4 hover:underline",
+      },
+      size: {
+        default: "h-10 px-4 py-2",
+        sm: "h-9 rounded-md px-3",
+        lg: "h-11 rounded-md px-8",
+        icon: "h-10 w-10",
+      },
+    },
+    defaultVariants: {
+      variant: "default",
+      size: "default",
+    },
+  }
+)
+
+export interface ButtonProps
+  extends React.ButtonHTMLAttributes<HTMLButtonElement>,
+    VariantProps<typeof buttonVariants> {
+  asChild?: boolean
+}
+
+const Button = React.forwardRef<HTMLButtonElement, ButtonProps>(
+  ({ className, variant, size, asChild = false, ...props }, ref) => {
+    const Comp = asChild ? Slot : "button"
+    return (
+      <Comp
+        className={cn(buttonVariants({ variant, size, className }))}
+        ref={ref}
+        {...props}
+      />
+    )
+  }
+)
+Button.displayName = "Button"
+
+export { Button, buttonVariants }

components/ui/card.tsx

@@ -0,0 +1,79 @@
+import * as React from "react"
+
+import { cn } from "@/lib/utils"
+
+const Card = React.forwardRef<
+  HTMLDivElement,
+  React.HTMLAttributes<HTMLDivElement>
+>(({ className, ...props }, ref) => (
+  <div
+    ref={ref}
+    className={cn(
+      "rounded-lg border bg-card text-card-foreground shadow-sm",
+      className
+    )}
+    {...props}
+  />
+))
+Card.displayName = "Card"
+
+const CardHeader = React.forwardRef<
+  HTMLDivElement,
+  React.HTMLAttributes<HTMLDivElement>
+>(({ className, ...props }, ref) => (
+  <div
+    ref={ref}
+    className={cn("flex flex-col space-y-1.5 p-6", className)}
+    {...props}
+  />
+))
+CardHeader.displayName = "CardHeader"
+
+const CardTitle = React.forwardRef<
+  HTMLDivElement,
+  React.HTMLAttributes<HTMLDivElement>
+>(({ className, ...props }, ref) => (
+  <div
+    ref={ref}
+    className={cn(
+      "text-2xl font-semibold leading-none tracking-tight",
+      className
+    )}
+    {...props}
+  />
+))
+CardTitle.displayName = "CardTitle"
+
+const CardDescription = React.forwardRef<
+  HTMLDivElement,
+  React.HTMLAttributes<HTMLDivElement>
+>(({ className, ...props }, ref) => (
+  <div
+    ref={ref}
+    className={cn("text-sm text-muted-foreground", className)}
+    {...props}
+  />
+))
+CardDescription.displayName = "CardDescription"
+
+const CardContent = React.forwardRef<
+  HTMLDivElement,
+  React.HTMLAttributes<HTMLDivElement>
+>(({ className, ...props }, ref) => (
+  <div ref={ref} className={cn("p-6 pt-0", className)} {...props} />
+))
+CardContent.displayName = "CardContent"
+
+const CardFooter = React.forwardRef<
+  HTMLDivElement,
+  React.HTMLAttributes<HTMLDivElement>
+>(({ className, ...props }, ref) => (
+  <div
+    ref={ref}
+    className={cn("flex items-center p-6 pt-0", className)}
+    {...props}
+  />
+))
+CardFooter.displayName = "CardFooter"
+
+export { Card, CardHeader, CardFooter, CardTitle, CardDescription, CardContent }

components/ui/toast.tsx

@@ -0,0 +1,129 @@
+"use client"
+
+import * as React from "react"
+import * as ToastPrimitives from "@radix-ui/react-toast"
+import { cva, type VariantProps } from "class-variance-authority"
+import { X } from "lucide-react"
+
+import { cn } from "@/lib/utils"
+
+const ToastProvider = ToastPrimitives.Provider
+
+const ToastViewport = React.forwardRef<
+  React.ElementRef<typeof ToastPrimitives.Viewport>,
+  React.ComponentPropsWithoutRef<typeof ToastPrimitives.Viewport>
+>(({ className, ...props }, ref) => (
+  <ToastPrimitives.Viewport
+    ref={ref}
+    className={cn(
+      "fixed top-0 z-[100] flex max-h-screen w-full flex-col-reverse p-4 sm:bottom-0 sm:right-0 sm:top-auto sm:flex-col md:max-w-[420px]",
+      className
+    )}
+    {...props}
+  />
+))
+ToastViewport.displayName = ToastPrimitives.Viewport.displayName
+
+const toastVariants = cva(
+  "group pointer-events-auto relative flex w-full items-center justify-between space-x-4 overflow-hidden rounded-md border p-6 pr-8 shadow-lg transition-all data-[swipe=cancel]:translate-x-0 data-[swipe=end]:translate-x-[var(--radix-toast-swipe-end-x)] data-[swipe=move]:translate-x-[var(--radix-toast-swipe-move-x)] data-[swipe=move]:transition-none data-[state=open]:animate-in data-[state=closed]:animate-out data-[swipe=end]:animate-out data-[state=closed]:fade-out-80 data-[state=closed]:slide-out-to-right-full data-[state=open]:slide-in-from-top-full data-[state=open]:sm:slide-in-from-bottom-full",
+  {
+    variants: {
+      variant: {
+        default: "border bg-background text-foreground",
+        destructive:
+          "destructive group border-destructive bg-destructive text-destructive-foreground",
+      },
+    },
+    defaultVariants: {
+      variant: "default",
+    },
+  }
+)
+
+const Toast = React.forwardRef<
+  React.ElementRef<typeof ToastPrimitives.Root>,
+  React.ComponentPropsWithoutRef<typeof ToastPrimitives.Root> &
+    VariantProps<typeof toastVariants>
+>(({ className, variant, ...props }, ref) => {
+  return (
+    <ToastPrimitives.Root
+      ref={ref}
+      className={cn(toastVariants({ variant }), className)}
+      {...props}
+    />
+  )
+})
+Toast.displayName = ToastPrimitives.Root.displayName
+
+const ToastAction = React.forwardRef<
+  React.ElementRef<typeof ToastPrimitives.Action>,
+  React.ComponentPropsWithoutRef<typeof ToastPrimitives.Action>
+>(({ className, ...props }, ref) => (
+  <ToastPrimitives.Action
+    ref={ref}
+    className={cn(
+      "inline-flex h-8 shrink-0 items-center justify-center rounded-md border bg-transparent px-3 text-sm font-medium ring-offset-background transition-colors hover:bg-secondary focus:outline-none focus:ring-2 focus:ring-ring focus:ring-offset-2 disabled:pointer-events-none disabled:opacity-50 group-[.destructive]:border-muted/40 group-[.destructive]:hover:border-destructive/30 group-[.destructive]:hover:bg-destructive group-[.destructive]:hover:text-destructive-foreground group-[.destructive]:focus:ring-destructive",
+      className
+    )}
+    {...props}
+  />
+))
+ToastAction.displayName = ToastPrimitives.Action.displayName
+
+const ToastClose = React.forwardRef<
+  React.ElementRef<typeof ToastPrimitives.Close>,
+  React.ComponentPropsWithoutRef<typeof ToastPrimitives.Close>
+>(({ className, ...props }, ref) => (
+  <ToastPrimitives.Close
+    ref={ref}
+    className={cn(
+      "absolute right-2 top-2 rounded-md p-1 text-foreground/50 opacity-0 transition-opacity hover:text-foreground focus:opacity-100 focus:outline-none focus:ring-2 group-hover:opacity-100 group-[.destructive]:text-red-300 group-[.destructive]:hover:text-red-50 group-[.destructive]:focus:ring-red-400 group-[.destructive]:focus:ring-offset-red-600",
+      className
+    )}
+    toast-close=""
+    {...props}
+  >
+    <X className="h-4 w-4" />
+  </ToastPrimitives.Close>
+))
+ToastClose.displayName = ToastPrimitives.Close.displayName
+
+const ToastTitle = React.forwardRef<
+  React.ElementRef<typeof ToastPrimitives.Title>,
+  React.ComponentPropsWithoutRef<typeof ToastPrimitives.Title>
+>(({ className, ...props }, ref) => (
+  <ToastPrimitives.Title
+    ref={ref}
+    className={cn("text-sm font-semibold", className)}
+    {...props}
+  />
+))
+ToastTitle.displayName = ToastPrimitives.Title.displayName
+
+const ToastDescription = React.forwardRef<
+  React.ElementRef<typeof ToastPrimitives.Description>,
+  React.ComponentPropsWithoutRef<typeof ToastPrimitives.Description>
+>(({ className, ...props }, ref) => (
+  <ToastPrimitives.Description
+    ref={ref}
+    className={cn("text-sm opacity-90", className)}
+    {...props}
+  />
+))
+ToastDescription.displayName = ToastPrimitives.Description.displayName
+
+type ToastProps = React.ComponentPropsWithoutRef<typeof Toast>
+
+type ToastActionElement = React.ReactElement<typeof ToastAction>
+
+export {
+  type ToastProps,
+  type ToastActionElement,
+  ToastProvider,
+  ToastViewport,
+  Toast,
+  ToastTitle,
+  ToastDescription,
+  ToastClose,
+  ToastAction,
+}

components/ui/toaster.tsx

@@ -0,0 +1,35 @@
+"use client"
+
+import { useToast } from "@/hooks/use-toast"
+import {
+  Toast,
+  ToastClose,
+  ToastDescription,
+  ToastProvider,
+  ToastTitle,
+  ToastViewport,
+} from "@/components/ui/toast"
+
+export function Toaster() {
+  const { toasts } = useToast()
+
+  return (
+    <ToastProvider>
+      {toasts.map(function ({ id, title, description, action, ...props }) {
+        return (
+          <Toast key={id} {...props}>
+            <div className="grid gap-1">
+              {title && <ToastTitle>{title}</ToastTitle>}
+              {description && (
+                <ToastDescription>{description}</ToastDescription>
+              )}
+            </div>
+            {action}
+            <ToastClose />
+          </Toast>
+        )
+      })}
+      <ToastViewport />
+    </ToastProvider>
+  )
+}

face_recognition_model_web

@@ -1 +0,0 @@
-Subproject commit 300e624454be86585500a57bd54a1e0cb28b7a94

hooks/use-toast.ts

@@ -0,0 +1,194 @@
+"use client"
+
+// Inspired by react-hot-toast library
+import * as React from "react"
+
+import type {
+  ToastActionElement,
+  ToastProps,
+} from "@/components/ui/toast"
+
+const TOAST_LIMIT = 1
+const TOAST_REMOVE_DELAY = 1000000
+
+type ToasterToast = ToastProps & {
+  id: string
+  title?: React.ReactNode
+  description?: React.ReactNode
+  action?: ToastActionElement
+}
+
+const actionTypes = {
+  ADD_TOAST: "ADD_TOAST",
+  UPDATE_TOAST: "UPDATE_TOAST",
+  DISMISS_TOAST: "DISMISS_TOAST",
+  REMOVE_TOAST: "REMOVE_TOAST",
+} as const
+
+let count = 0
+
+function genId() {
+  count = (count + 1) % Number.MAX_SAFE_INTEGER
+  return count.toString()
+}
+
+type ActionType = typeof actionTypes
+
+type Action =
+  | {
+      type: ActionType["ADD_TOAST"]
+      toast: ToasterToast
+    }
+  | {
+      type: ActionType["UPDATE_TOAST"]
+      toast: Partial<ToasterToast>
+    }
+  | {
+      type: ActionType["DISMISS_TOAST"]
+      toastId?: ToasterToast["id"]
+    }
+  | {
+      type: ActionType["REMOVE_TOAST"]
+      toastId?: ToasterToast["id"]
+    }
+
+interface State {
+  toasts: ToasterToast[]
+}
+
+const toastTimeouts = new Map<string, ReturnType<typeof setTimeout>>()
+
+const addToRemoveQueue = (toastId: string) => {
+  if (toastTimeouts.has(toastId)) {
+    return
+  }
+
+  const timeout = setTimeout(() => {
+    toastTimeouts.delete(toastId)
+    dispatch({
+      type: "REMOVE_TOAST",
+      toastId: toastId,
+    })
+  }, TOAST_REMOVE_DELAY)
+
+  toastTimeouts.set(toastId, timeout)
+}
+
+export const reducer = (state: State, action: Action): State => {
+  switch (action.type) {
+    case "ADD_TOAST":
+      return {
+        ...state,
+        toasts: [action.toast, ...state.toasts].slice(0, TOAST_LIMIT),
+      }
+
+    case "UPDATE_TOAST":
+      return {
+        ...state,
+        toasts: state.toasts.map((t) =>
+          t.id === action.toast.id ? { ...t, ...action.toast } : t
+        ),
+      }
+
+    case "DISMISS_TOAST": {
+      const { toastId } = action
+
+      // ! Side effects ! - This could be extracted into a dismissToast() action,
+      // but I'll keep it here for simplicity
+      if (toastId) {
+        addToRemoveQueue(toastId)
+      } else {
+        state.toasts.forEach((toast) => {
+          addToRemoveQueue(toast.id)
+        })
+      }
+
+      return {
+        ...state,
+        toasts: state.toasts.map((t) =>
+          t.id === toastId || toastId === undefined
+            ? {
+                ...t,
+                open: false,
+              }
+            : t
+        ),
+      }
+    }
+    case "REMOVE_TOAST":
+      if (action.toastId === undefined) {
+        return {
+          ...state,
+          toasts: [],
+        }
+      }
+      return {
+        ...state,
+        toasts: state.toasts.filter((t) => t.id !== action.toastId),
+      }
+  }
+}
+
+const listeners: Array<(state: State) => void> = []
+
+let memoryState: State = { toasts: [] }
+
+function dispatch(action: Action) {
+  memoryState = reducer(memoryState, action)
+  listeners.forEach((listener) => {
+    listener(memoryState)
+  })
+}
+
+type Toast = Omit<ToasterToast, "id">
+
+function toast({ ...props }: Toast) {
+  const id = genId()
+
+  const update = (props: ToasterToast) =>
+    dispatch({
+      type: "UPDATE_TOAST",
+      toast: { ...props, id },
+    })
+  const dismiss = () => dispatch({ type: "DISMISS_TOAST", toastId: id })
+
+  dispatch({
+    type: "ADD_TOAST",
+    toast: {
+      ...props,
+      id,
+      open: true,
+      onOpenChange: (open) => {
+        if (!open) dismiss()
+      },
+    },
+  })
+
+  return {
+    id: id,
+    dismiss,
+    update,
+  }
+}
+
+function useToast() {
+  const [state, setState] = React.useState<State>(memoryState)
+
+  React.useEffect(() => {
+    listeners.push(setState)
+    return () => {
+      const index = listeners.indexOf(setState)
+      if (index > -1) {
+        listeners.splice(index, 1)
+      }
+    }
+  }, [state])
+
+  return {
+    ...state,
+    toast,
+    dismiss: (toastId?: string) => dispatch({ type: "DISMISS_TOAST", toastId }),
+  }
+}
+
+export { useToast, toast }

lib/utils.ts

@@ -0,0 +1,6 @@
+import { clsx, type ClassValue } from "clsx"
+import { twMerge } from "tailwind-merge"
+
+export function cn(...inputs: ClassValue[]) {
+  return twMerge(clsx(inputs))
+}

package.json

@@ -9,19 +9,32 @@
     "lint": "next lint"
   },
   "dependencies": {
-    "react": "^19.0.0",
-    "react-dom": "^19.0.0",
-    "next": "15.1.6"
+    "@radix-ui/react-slot": "^1.1.1",
+    "@radix-ui/react-toast": "^1.2.5",
+    "class-variance-authority": "^0.7.1",
+    "clsx": "^2.1.1",
+    "encoding": "^0.1.13",
+    "face-api.js": "^0.22.2",
+    "hls.js": "^1.0.3-0.canary.7275",
+    "lucide-react": "^0.474.0",
+    "next": "15.1.6",
+    "react": "^18.3.1",
+    "react-dom": "^18.3.1",
+    "react-webcam": "^7.2.0",
+    "tailwind-merge": "^3.0.1",
+    "tailwindcss-animate": "^1.0.7"
   },
   "devDependencies": {
-    "typescript": "^5",
+    "@babel/core": "^7.26.7",
+    "@babel/helper-plugin-utils": "^7.26.5",
+    "@eslint/eslintrc": "^3",
     "@types/node": "^20",
     "@types/react": "^19",
     "@types/react-dom": "^19",
-    "postcss": "^8",
-    "tailwindcss": "^3.4.1",
     "eslint": "^9",
     "eslint-config-next": "15.1.6",
-    "@eslint/eslintrc": "^3"
+    "postcss": "^8",
+    "tailwindcss": "^3.4.1",
+    "typescript": "^5"
   }
 }

tailwind.config.ts

@@ -1,6 +1,7 @@
 import type { Config } from "tailwindcss";
 
 export default {
+    darkMode: ["class"],
     content: [
     "./pages/**/*.{js,ts,jsx,tsx,mdx}",
     "./components/**/*.{js,ts,jsx,tsx,mdx}",
@@ -9,10 +10,53 @@ export default {
   theme: {
   	extend: {
   		colors: {
-        background: "var(--background)",
-        foreground: "var(--foreground)",
+  			background: 'hsl(var(--background))',
+  			foreground: 'hsl(var(--foreground))',
+  			card: {
+  				DEFAULT: 'hsl(var(--card))',
+  				foreground: 'hsl(var(--card-foreground))'
   			},
+  			popover: {
+  				DEFAULT: 'hsl(var(--popover))',
+  				foreground: 'hsl(var(--popover-foreground))'
   			},
+  			primary: {
+  				DEFAULT: 'hsl(var(--primary))',
+  				foreground: 'hsl(var(--primary-foreground))'
   			},
-  plugins: [],
+  			secondary: {
+  				DEFAULT: 'hsl(var(--secondary))',
+  				foreground: 'hsl(var(--secondary-foreground))'
+  			},
+  			muted: {
+  				DEFAULT: 'hsl(var(--muted))',
+  				foreground: 'hsl(var(--muted-foreground))'
+  			},
+  			accent: {
+  				DEFAULT: 'hsl(var(--accent))',
+  				foreground: 'hsl(var(--accent-foreground))'
+  			},
+  			destructive: {
+  				DEFAULT: 'hsl(var(--destructive))',
+  				foreground: 'hsl(var(--destructive-foreground))'
+  			},
+  			border: 'hsl(var(--border))',
+  			input: 'hsl(var(--input))',
+  			ring: 'hsl(var(--ring))',
+  			chart: {
+  				'1': 'hsl(var(--chart-1))',
+  				'2': 'hsl(var(--chart-2))',
+  				'3': 'hsl(var(--chart-3))',
+  				'4': 'hsl(var(--chart-4))',
+  				'5': 'hsl(var(--chart-5))'
+  			}
+  		},
+  		borderRadius: {
+  			lg: 'var(--radius)',
+  			md: 'calc(var(--radius) - 2px)',
+  			sm: 'calc(var(--radius) - 4px)'
+  		}
+  	}
+  },
+  plugins: [require("tailwindcss-animate")],
 } satisfies Config;