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<!DOCTYPE html>
<html lang="en">
<head>
    <meta charset="UTF-8">
    <meta name="viewport" content="width=device-width, initial-scale=1.0">
    <title>HTTP Image Upload Demo</title>
    <style>

        /* Reset and base styles */

        * {

            box-sizing: border-box;

            margin: 0;

            padding: 0;

        }



        body {

            font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif;

            color: #333;

            line-height: 1.6;

        }



        /* Demo section styling */

        .execution-section {

            max-width: 1200px;

            margin: 0 auto;

            padding: 2rem;

            background-color: #f8f9fa;

            border-radius: 8px;

            box-shadow: 0 4px 6px rgba(0,0,0,0.1);

        }



        .section-title {

            font-size: 2rem;

            color: #384B70;

            margin-bottom: 1rem;

            padding-bottom: 0.5rem;

            border-bottom: 2px solid #507687;

        }



        .demo-container {

            display: flex;

            flex-wrap: wrap;

            gap: 2rem;

            margin-top: 1.5rem;

        }



        .upload-container, .response-container {

            flex: 1;

            min-width: 300px;

            padding: 1.5rem;

            background-color: white;

            border-radius: 8px;

            box-shadow: 0 2px 4px rgba(0,0,0,0.05);

        }



        .container-title {

            font-size: 1.5rem;

            margin-bottom: 1rem;

            color: #384B70;

        }



        /* Upload area styling */

        .file-input-container {

            border: 2px dashed #ccc;

            border-radius: 5px;

            padding: 2rem;

            text-align: center;

            margin-bottom: 1rem;

            transition: all 0.3s ease;

        }



        .file-input-container:hover {

            border-color: #507687;

            background-color: #f8f9fa;

        }



        #fileInput {

            display: none;

        }



        .file-label {

            cursor: pointer;

            display: flex;

            flex-direction: column;

            align-items: center;

            gap: 0.5rem;

        }



        .file-icon {

            font-size: 2.5rem;

            color: #507687;

            width: 64px;

            height: 64px;

        }

        .file-placeholder {

            max-width: 100%;

            height: auto;

            margin-top: 1rem;

            border-radius: 4px;

            display: none;

        }



        #sendButton {

            background-color: #384B70;

            color: white;

            border: none;

            border-radius: 4px;

            padding: 0.75rem 1.5rem;

            font-size: 1rem;

            cursor: pointer;

            transition: background-color 0.3s;

            width: 100%;

            margin-top: 1rem;

        }



        #sendButton:disabled {

            background-color: #cccccc;

            cursor: not-allowed;

        }



        #sendButton:hover:not(:disabled) {

            background-color: #507687;

        }



        /* Response area styling */

        .response-output {

            height: 300px;

            overflow-y: auto;

            background-color: #f8f9fa;

            border: 1px solid #ddd;

            border-radius: 4px;

            padding: 1rem;

            font-family: monospace;

            white-space: pre-wrap;

        }



        /* Tabs styling */

        .tabs {

            display: flex;

            border-bottom: 1px solid #ddd;

            margin-bottom: 1rem;

        }



        .tab-button {

            padding: 0.5rem 1rem;

            background-color: #f1f1f1;

            border: none;

            cursor: pointer;

            transition: background-color 0.3s;

            font-size: 1rem;

        }



        .tab-button.active {

            background-color: #384B70;

            color: white;

        }



        .tab-content {

            display: none;

            height: 300px;

        }



        .tab-content.active {

            display: block;

        }



        /* Visualization area styling */

        #visualizationContainer {

            position: relative;

            height: 100%;

            overflow: auto;

            background-color: #f8f9fa;

            border: 1px solid #ddd;

            border-radius: 4px;

        }



        .detection-canvas {

            display: block;

            margin: 0 auto;

        }



        /* Utilities */

        #loading {

            display: none;

            margin-top: 1rem;

            color: #384B70;

            font-weight: bold;

            text-align: center;

        }



        #message {

            margin-top: 1rem;

            padding: 0.75rem;

            border-radius: 4px;

            text-align: center;

            display: none;

        }



        .error {

            background-color: #ffebee;

            color: #d32f2f;

        }



        .success {

            background-color: #e8f5e9;

            color: #388e3c;

        }



        .info {

            font-size: 0.9rem;

            color: #666;

            margin-top: 0.5rem;

        }



        .stats {

            margin-top: 1rem;

            font-size: 0.9rem;

            color: #666;

        }



        /* Debug output */

        #debugOutput {

            margin-top: 0.5rem;

            font-size: 0.8rem;

            color: #999;

            border-top: 1px dashed #ddd;

            padding-top: 0.5rem;

            display: none;

        }

    </style>
</head>
<body>
    <!-- Interactive Demo Section -->
    <section class="execution-section">
        <h2 class="section-title">Try It Yourself</h2>
        <p>Upload an image and see the object detection and depth estimation results in real-time.</p>

        <div class="demo-container">
            <!-- Upload Container -->
            <div class="upload-container">
                <h3 class="container-title">Upload Image</h3>

                <div class="file-input-container">
                    <label for="fileInput" class="file-label">
                        <img src="https://upload.wikimedia.org/wikipedia/commons/a/a1/Icons8_flat_folder.svg" class="file-icon"/>
                        <span>Click to select image</span>
                        <p class="info">PNG or JPEG, max 2MB</p>
                    </label>
                    <input type="file" accept="image/*" id="fileInput" />
                    <img id="imagePreview" class="file-placeholder" alt="Image preview" />
                </div>

                <button id="sendButton" disabled>Process Image</button>
                <div id="loading">Processing your image...</div>
                <div id="message"></div>

                <div class="stats">
                    <div id="imageSize"></div>
                    <div id="processingTime"></div>
                </div>

                <div id="debugOutput"></div>
            </div>

            <!-- Response Container with Tabs -->
            <div class="response-container">
                <h3 class="container-title">Response</h3>

                <div class="tabs">
                    <button class="tab-button active" data-tab="raw">Raw Output</button>
                    <button class="tab-button" data-tab="visual">Visual Output</button>
                </div>

                <!-- Raw Output Tab -->
                <div id="rawTab" class="tab-content active">
                    <pre class="response-output" id="responseOutput">// Response will appear here after processing</pre>
                </div>

                <!-- Visual Output Tab -->
                <div id="visualTab" class="tab-content">
                    <div id="visualizationContainer">
                        <canvas id="detectionCanvas" class="detection-canvas"></canvas>
                    </div>
                </div>
            </div>
        </div>
    </section>

    <script>

        // DOM Elements

        const fileInput = document.getElementById('fileInput');

        const imagePreview = document.getElementById('imagePreview');

        const sendButton = document.getElementById('sendButton');

        const loading = document.getElementById('loading');

        const message = document.getElementById('message');

        const responseOutput = document.getElementById('responseOutput');

        const imageSizeInfo = document.getElementById('imageSize');

        const processingTimeInfo = document.getElementById('processingTime');

        const tabButtons = document.querySelectorAll('.tab-button');

        const tabContents = document.querySelectorAll('.tab-content');

        const detectionCanvas = document.getElementById('detectionCanvas');

        const ctx = detectionCanvas.getContext('2d');

        const debugOutput = document.getElementById('debugOutput');



        // Enable debug mode (set to false in production)

        const DEBUG = true;



        // API endpoint URL

        const API_URL = '/api/predict';

        

        let imageFile = null;

        let startTime = null;

        let originalImage = null;

        let processingWidth = 0;

        let processingHeight = 0;

        let responseData = null;



        // Tab switching functionality

        tabButtons.forEach(button => {

            button.addEventListener('click', () => {

                const tabName = button.getAttribute('data-tab');



                // Update button states

                tabButtons.forEach(btn => btn.classList.remove('active'));

                button.classList.add('active');



                // Update tab content visibility

                tabContents.forEach(content => content.classList.remove('active'));

                document.getElementById(tabName + 'Tab').classList.add('active');



                // If switching to visual tab and we have data, ensure visualization is rendered

                if (tabName === 'visual' && responseData && originalImage) {

                    visualizeResults(originalImage, responseData);

                }

            });

        });



        // Handle file input change

        fileInput.addEventListener('change', (event) => {

            const file = event.target.files[0];



            // Clear previous selections

            imageFile = null;

            imagePreview.style.display = 'none';

            sendButton.disabled = true;

            originalImage = null;

            responseData = null;



            // Validate file

            if (!file) return;



            if (file.size > 2 * 1024 * 1024) {

                showMessage('File size exceeds 2MB limit.', 'error');

                return;

            }



            if (!['image/png', 'image/jpeg'].includes(file.type)) {

                showMessage('Only PNG and JPEG formats are supported.', 'error');

                return;

            }



            // Store file for upload

            imageFile = file;



            // Show image preview

            const reader = new FileReader();

            reader.onload = (e) => {

                const image = new Image();

                image.src = e.target.result;



                image.onload = () => {

                    // Store original image for visualization

                    originalImage = image;



                    // Set preview

                    imagePreview.src = e.target.result;

                    imagePreview.style.display = 'block';



                    // Update image info

                    imageSizeInfo.textContent = `Original size: ${image.width}x${image.height} pixels`;



                    // Calculate processing dimensions (for visualization)

                    calculateProcessingDimensions(image.width, image.height);

                    

                    // Enable send button

                    sendButton.disabled = false;

                    showMessage('Image ready to process.', 'info');

                };

            };

            reader.readAsDataURL(file);

        });



        // Calculate dimensions for processing visualization

        function calculateProcessingDimensions(width, height) {

            const maxWidth = 640;

            const maxHeight = 320;



            // Calculate dimensions

            if (width > height) {

                if (width > maxWidth) {

                    height = Math.round((height * maxWidth) / width);

                    width = maxWidth;

                }

            } else {

                if (height > maxHeight) {

                    width = Math.round((width * maxHeight) / height);

                    height = maxHeight;

                }

            }



            // Store processing dimensions for visualization

            processingWidth = width;

            processingHeight = height;

        }



        // Handle send button click

        sendButton.addEventListener('click', async () => {

            if (!imageFile) {

                showMessage('No image selected.', 'error');

                return;

            }



            // Clear previous response

            responseOutput.textContent = "// Processing...";

            clearCanvas();

            responseData = null;

            debugOutput.style.display = 'none';



            // Show loading state

            loading.style.display = 'block';

            message.style.display = 'none';



            // Reset processing time

            processingTimeInfo.textContent = '';



            // Record start time

            startTime = performance.now();



            // Create form data for HTTP request

            const formData = new FormData();

            formData.append('file', imageFile);



            try {

                // Send HTTP request

                const response = await fetch(API_URL, {

                    method: 'POST',

                    body: formData

                });



                // Handle response

                if (!response.ok) {

                    const errorText = await response.text();

                    throw new Error(`HTTP error ${response.status}: ${errorText}`);

                }



                // Parse JSON response

                const data = await response.json();

                responseData = data;



                // Calculate processing time

                const endTime = performance.now();

                const timeTaken = endTime - startTime;

                

                // Format and display raw response

                responseOutput.textContent = JSON.stringify(data, null, 2);

                processingTimeInfo.textContent = `Processing time: ${timeTaken.toFixed(2)} ms`;



                // Visualize the results

                if (originalImage) {

                    visualizeResults(originalImage, data);

                }



                // Show success message

                showMessage('Image processed successfully!', 'success');

            } catch (error) {

                console.error('Error processing image:', error);

                showMessage(`Error: ${error.message}`, 'error');

                responseOutput.textContent = `// Error: ${error.message}`;

                

                if (DEBUG) {

                    debugOutput.style.display = 'block';

                    debugOutput.textContent = `Error: ${error.message}\n${error.stack || ''}`;

                }

            } finally {

                loading.style.display = 'none';

            }

        });



        // Visualize detection results

        function visualizeResults(image, data) {

            try {

                // Set canvas dimensions

                detectionCanvas.width = processingWidth;

                detectionCanvas.height = processingHeight;



                // Draw the original image

                ctx.drawImage(image, 0, 0, processingWidth, processingHeight);



                // Set styles for bounding boxes

                ctx.lineWidth = 3;

                ctx.font = 'bold 14px Arial';



                // Find detections (checking all common formats)

                let detections = [];

                let detectionSource = '';



                if (data.detections && Array.isArray(data.detections)) {

                    detections = data.detections;

                    detectionSource = 'detections';

                } else if (data.predictions && Array.isArray(data.predictions)) {

                    detections = data.predictions;

                    detectionSource = 'predictions';

                } else if (data.objects && Array.isArray(data.objects)) {

                    detections = data.objects;

                    detectionSource = 'objects';

                } else if (data.results && Array.isArray(data.results)) {

                    detections = data.results;

                    detectionSource = 'results';

                } else {

                    // Try to look one level deeper if no detections found

                    for (const key in data) {

                        if (typeof data[key] === 'object' && data[key] !== null) {

                            if (Array.isArray(data[key])) {

                                detections = data[key];

                                detectionSource = key;

                                break;

                            } else {

                                // Look one more level down

                                for (const subKey in data[key]) {

                                    if (Array.isArray(data[key][subKey])) {

                                        detections = data[key][subKey];

                                        detectionSource = `${key}.${subKey}`;

                                        break;

                                    }

                                }

                            }

                        }

                    }

                }



                // Process each detection

                detections.forEach((detection, index) => {

                    // Try to extract bounding box information

                    let bbox = null;

                    let label = null;

                    let confidence = null;

                    let distance = null;



                    // Extract label/class

                    if (detection.class !== undefined) {

                        label = detection.class;

                    } else {

                        // Fallback to other common property names

                        for (const key of ['label', 'name', 'category', 'className']) {

                            if (detection[key] !== undefined) {

                                label = detection[key];

                                break;

                            }

                        }

                    }



                    // Default label if none found

                    if (!label) label = `Object ${index + 1}`;



                    // Extract confidence score if available

                    for (const key of ['confidence', 'score', 'probability', 'conf']) {

                        if (detection[key] !== undefined) {

                            confidence = detection[key];

                            break;

                        }

                    }



                    // Extract distance - specifically look for distance_estimated first

                    if (detection.distance_estimated !== undefined) {

                        distance = detection.distance_estimated;

                    } else {

                        // Fallback to other common distance properties

                        for (const key of ['distance', 'depth', 'z', 'dist', 'range']) {

                            if (detection[key] !== undefined) {

                                distance = detection[key];

                                break;

                            }

                        }

                    }



                    // Look for bounding box in features

                    if (detection.features &&

                        detection.features.xmin !== undefined &&

                        detection.features.ymin !== undefined &&

                        detection.features.xmax !== undefined &&

                        detection.features.ymax !== undefined) {



                        bbox = {

                            xmin: detection.features.xmin,

                            ymin: detection.features.ymin,

                            xmax: detection.features.xmax,

                            ymax: detection.features.ymax

                        };

                    } else {

                        // Recursively search for bbox-like properties

                        function findBBox(obj, path = '') {

                            if (!obj || typeof obj !== 'object') return null;



                            // Check if this object looks like a bbox

                            if ((obj.x !== undefined && obj.y !== undefined &&

                                (obj.width !== undefined || obj.w !== undefined ||

                                 obj.height !== undefined || obj.h !== undefined)) ||

                                (obj.xmin !== undefined && obj.ymin !== undefined &&

                                 obj.xmax !== undefined && obj.ymax !== undefined)) {

                                return obj;

                            }



                            // Check if it's an array of 4 numbers (potential bbox)

                            if (Array.isArray(obj) && obj.length === 4 &&

                                obj.every(item => typeof item === 'number')) {

                                return obj;

                            }



                            // Check common bbox property names

                            for (const key of ['bbox', 'box', 'bounding_box', 'boundingBox']) {

                                if (obj[key] !== undefined) {

                                    return obj[key];

                                }

                            }



                            // Search nested properties

                            for (const key in obj) {

                                const result = findBBox(obj[key], path ? `${path}.${key}` : key);

                                if (result) return result;

                            }



                            return null;

                        }



                        // Find bbox using recursive search as fallback

                        bbox = findBBox(detection);

                    }



                    // If we found a bounding box, draw it

                    if (bbox) {

                        // Parse different bbox formats

                        let x, y, width, height;



                        if (Array.isArray(bbox)) {

                            // Try to determine array format

                            if (bbox.length === 4) {

                                if (bbox[0] >= 0 && bbox[1] >= 0 && bbox[2] <= 1 && bbox[3] <= 1) {

                                    // Likely normalized [x1, y1, x2, y2]

                                    x = bbox[0] * processingWidth;

                                    y = bbox[1] * processingHeight;

                                    width = (bbox[2] - bbox[0]) * processingWidth;

                                    height = (bbox[3] - bbox[1]) * processingHeight;

                                } else if (bbox[2] > bbox[0] && bbox[3] > bbox[1]) {

                                    // Likely [x1, y1, x2, y2]

                                    x = bbox[0];

                                    y = bbox[1];

                                    width = bbox[2] - bbox[0];

                                    height = bbox[3] - bbox[1];

                                } else {

                                    // Assume [x, y, width, height]

                                    x = bbox[0];

                                    y = bbox[1];

                                    width = bbox[2];

                                    height = bbox[3];

                                }

                            }

                        } else {

                            // Object format with named properties

                            if (bbox.x !== undefined && bbox.y !== undefined) {

                                x = bbox.x;

                                y = bbox.y;

                                width = bbox.width || bbox.w || 0;

                                height = bbox.height || bbox.h || 0;

                            } else if (bbox.xmin !== undefined && bbox.ymin !== undefined) {

                                x = bbox.xmin;

                                y = bbox.ymin;

                                width = (bbox.xmax || 0) - bbox.xmin;

                                height = (bbox.ymax || 0) - bbox.ymin;

                            }

                        }



                        // Validate coordinates

                        if (x === undefined || y === undefined || width === undefined || height === undefined) {

                            return;

                        }



                        // Check if we need to scale normalized coordinates (0-1)

                        if (x >= 0 && x <= 1 && y >= 0 && y <= 1 && width >= 0 && width <= 1 && height >= 0 && height <= 1) {

                            x = x * processingWidth;

                            y = y * processingHeight;

                            width = width * processingWidth;

                            height = height * processingHeight;

                        }



                        // Generate a color based on the class name

                        const hue = stringToHue(label);

                        ctx.strokeStyle = `hsl(${hue}, 100%, 40%)`;

                        ctx.fillStyle = `hsla(${hue}, 100%, 40%, 0.3)`;



                        // Draw bounding box

                        ctx.beginPath();

                        ctx.rect(x, y, width, height);

                        ctx.stroke();

                        ctx.fill();



                        // Format confidence value

                        let confidenceText = "";

                        if (confidence !== null && confidence !== undefined) {

                            // Convert to percentage if it's a probability (0-1)

                            if (confidence <= 1) {

                                confidence = (confidence * 100).toFixed(0);

                            } else {

                                confidence = confidence.toFixed(0);

                            }

                            confidenceText = ` ${confidence}%`;

                        }



                        // Format distance value

                        let distanceText = "";

                        if (distance !== null && distance !== undefined) {

                            distanceText = ` : ${distance.toFixed(2)} m`;

                        }



                        // Create label text

                        const labelText = `${label}${confidenceText}${distanceText}`;



                        // Measure text width

                        const textWidth = ctx.measureText(labelText).width + 10;



                        // Draw label background

                        ctx.fillStyle = `hsl(${hue}, 100%, 40%)`;

                        ctx.fillRect(x, y - 20, textWidth, 20);



                        // Draw label text

                        ctx.fillStyle = "white";

                        ctx.fillText(labelText, x + 5, y - 5);

                    }

                });



            } catch (error) {

                console.error('Error visualizing results:', error);

                debugOutput.style.display = 'block';

                debugOutput.textContent += `VISUALIZATION ERROR: ${error.message}\n`;

                debugOutput.textContent += `Error stack: ${error.stack}\n`;

            }

        }



        // Generate consistent hue for string

        function stringToHue(str) {

            let hash = 0;

            for (let i = 0; i < str.length; i++) {

                hash = str.charCodeAt(i) + ((hash << 5) - hash);

            }

            return hash % 360;

        }



        // Clear canvas

        function clearCanvas() {

            if (detectionCanvas.getContext) {

                ctx.clearRect(0, 0, detectionCanvas.width, detectionCanvas.height);

            }

        }



        // Show message function

        function showMessage(text, type) {

            message.textContent = text;

            message.className = '';

            message.classList.add(type);

            message.style.display = 'block';



            if (type === 'info') {

                setTimeout(() => {

                    message.style.display = 'none';

                }, 3000);

            }

        }

    </script>
</body>
</html>