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kronos
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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>Kronos API Prediction Visualizer</title>
    <script src="https://cdn.jsdelivr.net/npm/echarts@5.3.3/dist/echarts.min.js"></script>
    <style>
        body { font-family: sans-serif; margin: 2em; }
        .container { max-width: 1200px; margin: auto; }
        .form-group { margin-bottom: 1em; }
        label { display: block; margin-bottom: 0.5em; }
        input, textarea, select { width: 100%; padding: 0.5em; box-sizing: border-box; }
        textarea { min-height: 150px; }
        button { padding: 0.7em 1.5em; cursor: pointer; }
        #chart { width: 100%; height: 600px; margin-top: 2em; border: 1px solid #ccc; }
        .error { color: red; }
        .success { color: green; }
        .status { margin-top: 1em; font-weight: bold; }
        .info { background-color: #f0f0f0; border-left: 4px solid #007bff; padding: 1em; margin-bottom: 1em; }
        hr { margin: 2em 0; }
    </style>
</head>
<body>
    <div class="container">
        <h1>Kronos API Control Panel</h1>

        <div class="info">
            <p>
                For local testing, first run <code>app.py</code> in VS Code (press F5). The API endpoints will be available at <code>http://localhost:7860</code>.
            </p>
        </div>

        <!-- Section for Model Loading -->
        <section id="model-loader">
            <h2>1. Load Model</h2>
            <div class="form-group">
                <label for="model-select">Available Models:</label>
                <select id="model-select"></select>
            </div>
            <button id="load-model-btn">Load Selected Model</button>
            <div id="model-status" class="status"></div>
        </section>

        <hr>

        <!-- Section for Prediction -->
        <section id="predictor">
            <h2>2. Get Prediction</h2>
            <div class="form-group">
                <label for="api-key">API Key (Bearer Token, if required):</label>
                <input type="password" id="api-key" placeholder="Enter your API Key">
            </div>
            <div class="form-group">
                <label for="k-lines">K-line Data (JSON Array of Arrays):</label>
                <textarea id="k-lines" placeholder="Paste your k-line data here..."></textarea>
            </div>
            <div class="form-group">
                <label for="pred-len">Prediction Length:</label>
                <input type="number" id="pred-len" value="120">
            </div>
            <button id="predict-btn">Get Prediction & Visualize</button>
        </section>

        <div id="chart"></div>
        <div id="error-message" class="status error"></div>
    </div>

    <script>
        // --- Global DOM Elements ---
        const modelSelect = document.getElementById('model-select');
        const loadModelBtn = document.getElementById('load-model-btn');
        const modelStatusDiv = document.getElementById('model-status');
        const predictBtn = document.getElementById('predict-btn');
        const apiKeyInput = document.getElementById('api-key');
        const kLinesTextarea = document.getElementById('k-lines');
        const predLenInput = document.getElementById('pred-len');
        const chartDom = document.getElementById('chart');
        const errorDiv = document.getElementById('error-message');

        // --- API Base URLs ---
        // Use relative paths for deployed environment, detect local for testing.
        const isLocal = window.location.hostname === 'localhost' || window.location.hostname === '127.0.0.1';
        const apiBaseUrl = isLocal ? 'http://localhost:7860' : '';

        // --- Helper Functions ---
        async function apiFetch(endpoint, options) {
            const apiKey = apiKeyInput.value;
            const headers = {
                'Content-Type': 'application/json',
                ...options.headers,
            };
            if (apiKey) {
                headers['Authorization'] = `Bearer ${apiKey}`;
            }
            
            const response = await fetch(apiBaseUrl + endpoint, { ...options, headers });

            if (!response.ok) {
                const errorData = await response.json();
                throw new Error(`API Error (${response.status}): ${errorData.error || 'Unknown error'}`);
            }
            return response.json();
        }

        // --- Model Loading Logic ---
        async function populateModels() {
            try {
                const models = await apiFetch('/api/available-models', { method: 'GET' });
                modelSelect.innerHTML = ''; // Clear existing options
                for (const key in models) {
                    const option = document.createElement('option');
                    option.value = key;
                    option.textContent = `${models[key].name} (${models[key].params}) - ${models[key].description}`;
                    modelSelect.appendChild(option);
                }
            } catch (error) {
                modelStatusDiv.className = 'status error';
                modelStatusDiv.textContent = `Failed to fetch models: ${error.message}`;
            }
        }

        loadModelBtn.addEventListener('click', async () => {
            const modelKey = modelSelect.value;
            modelStatusDiv.className = 'status';
            modelStatusDiv.textContent = `Loading model '${modelKey}'...`;
            try {
                const result = await apiFetch('/api/load-model', {
                    method: 'POST',
                    body: JSON.stringify({ model_key: modelKey })
                });
                modelStatusDiv.className = 'status success';
                modelStatusDiv.textContent = result.status;
            } catch (error) {
                modelStatusDiv.className = 'status error';
                modelStatusDiv.textContent = error.message;
            }
        });

        // --- Prediction Logic ---
        predictBtn.addEventListener('click', async () => {
            const kLinesText = kLinesTextarea.value;
            const predLen = parseInt(predLenInput.value, 10);
            
            errorDiv.textContent = '';
            const myChart = echarts.init(chartDom);
            myChart.showLoading();

            if (!kLinesText) {
                errorDiv.textContent = 'K-line data cannot be empty.';
                myChart.hideLoading();
                return;
            }

            let kLines;
            try {
                kLines = JSON.parse(kLinesText);
            } catch (e) {
                errorDiv.textContent = 'Invalid JSON in K-line data. Please check the format.';
                myChart.hideLoading();
                return;
            }

            const payload = {
                k_lines: kLines,
                prediction_params: { pred_len: predLen }
            };

            try {
                const result = await apiFetch('/api/predict', {
                    method: 'POST',
                    body: JSON.stringify(payload)
                });
                
                const historicalData = kLines.map(item => [
                    item[0], parseFloat(item[1]), parseFloat(item[4]), parseFloat(item[3]), parseFloat(item[2])
                ]);
                
                const predictionData = result.prediction_results.map(item => [
                    item[0], parseFloat(item[1]), parseFloat(item[4]), parseFloat(item[3]), parseFloat(item[2])
                ]);

                const allTimestamps = [...historicalData.map(d => d[0]), ...predictionData.map(d => d[0])];
                
                const option = {
                    tooltip: { trigger: 'axis', axisPointer: { type: 'cross' } },
                    legend: { data: ['Historical', 'Prediction'] },
                    grid: { left: '10%', right: '10%', bottom: '15%' },
                    xAxis: { type: 'time', min: allTimestamps[0], max: allTimestamps[allTimestamps.length - 1] },
                    yAxis: { scale: true, splitArea: { show: true } },
                    dataZoom: [
                        { type: 'inside', start: 50, end: 100 },
                        { show: true, type: 'slider', top: '90%', start: 50, end: 100 }
                    ],
                    series: [
                        { name: 'Historical', type: 'candlestick', data: historicalData, itemStyle: { color: '#00da3c', color0: '#ec0000', borderColor: '#008F28', borderColor0: '#8A0000' } },
                        { name: 'Prediction', type: 'candlestick', data: predictionData, itemStyle: { color: '#4287f5', color0: '#f54242', borderColor: '#285199', borderColor0: '#992828' } }
                    ]
                };
                
                myChart.hideLoading();
                myChart.setOption(option);

            } catch (error) {
                myChart.hideLoading();
                errorDiv.textContent = error.message;
                console.error('Fetch error:', error);
            }
        });

        // --- Initial Load ---
        document.addEventListener('DOMContentLoaded', populateModels);
    </script>
</body>
</html>