Spaces:
Sleeping
Sleeping
File size: 32,930 Bytes
2524181 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773 774 775 776 777 778 779 780 781 782 783 784 785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800 801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816 817 818 819 820 821 822 823 824 825 826 827 828 829 830 831 832 833 834 835 836 837 838 839 840 841 842 843 844 845 846 847 848 849 850 851 852 853 854 855 856 857 858 859 860 861 862 863 864 865 866 867 868 869 870 871 872 873 874 875 876 877 878 | """
CPU Optimization ML Dashboard - Simplified Version
Lightweight, dependency-minimal dashboard for model visualization
"""
import json
import joblib
import os
import numpy as np
from pathlib import Path
# ============================================================================
# CONFIGURATION
# ============================================================================
MODELS_DIR = "models"
METRICS_DIR = "metrics"
EVAL_METRICS_DIR = f"{METRICS_DIR}/eval"
DATA_DIR = "data/processed"
# ============================================================================
# UTILITY FUNCTIONS
# ============================================================================
def load_metrics():
"""Load training and evaluation metrics"""
metrics_data = {}
# Load training metrics
train_metrics_file = f"{METRICS_DIR}/train_metrics.json"
if os.path.exists(train_metrics_file):
with open(train_metrics_file, 'r') as f:
metrics_data['train'] = json.load(f)
# Load evaluation metrics
lr_metrics_file = f"{EVAL_METRICS_DIR}/LinearRegression_metrics.json"
rf_metrics_file = f"{EVAL_METRICS_DIR}/RandomForest_metrics.json"
if os.path.exists(lr_metrics_file):
with open(lr_metrics_file, 'r') as f:
metrics_data['lr_eval'] = json.load(f)
if os.path.exists(rf_metrics_file):
with open(rf_metrics_file, 'r') as f:
metrics_data['rf_eval'] = json.load(f)
return metrics_data
def load_model(model_name):
"""Load a model from disk"""
model_path = os.path.join(MODELS_DIR, f"{model_name}.pkl")
if os.path.exists(model_path):
try:
return joblib.load(model_path)
except:
return None
return None
def get_model_info():
"""Get available models and their info"""
models = {}
try:
model_files = [f for f in os.listdir(MODELS_DIR) if f.endswith('.pkl')]
for model_file in model_files:
model_name = model_file.replace('.pkl', '')
file_path = os.path.join(MODELS_DIR, model_file)
models[model_name] = {
'path': file_path,
'file_size': os.path.getsize(file_path) / 1024 # KB
}
except:
pass
return models
def get_artifacts_info():
"""Get artifact information as formatted text"""
artifacts_text = "### Available Model Artifacts\n\n"
try:
models = get_model_info()
for model_name, info in models.items():
artifacts_text += f"**{model_name.upper()}**\n"
artifacts_text += f"- Size: {info['file_size']:.2f} KB\n"
artifacts_text += f"- Path: {info['path']}\n"
artifacts_text += f"- Status: ✓ Available\n\n"
except Exception as e:
artifacts_text += f"Error loading artifacts: {e}\n"
return artifacts_text
def format_metrics_text(metrics, model_filter=None):
"""Format metrics as text with optional model filtering"""
text = "### Model Metrics Summary\n\n"
if 'train' in metrics:
train = metrics['train']
text += "#### Training Metrics\n"
if model_filter is None or model_filter == 'Random Forest':
text += f"- Random Forest R² Score: {train.get('r2_rf', 'N/A')}\n"
text += f"- Random Forest RMSE: {train.get('rmse_rf', 'N/A')}\n"
if model_filter is None or model_filter == 'Linear Regression':
text += f"- Linear Regression R² Score: {train.get('r2_lr', 'N/A')}\n"
text += f"- Linear Regression RMSE: {train.get('rmse_lr', 'N/A')}\n"
text += "\n"
if model_filter is None or model_filter == 'Random Forest':
if 'rf_eval' in metrics:
rf = metrics['rf_eval']
text += "#### Random Forest Evaluation\n"
text += f"- R² Score: {rf.get('r2', 'N/A')}\n"
text += f"- RMSE: {rf.get('rmse', 'N/A')}\n\n"
if model_filter is None or model_filter == 'Linear Regression':
if 'lr_eval' in metrics:
lr = metrics['lr_eval']
text += "#### Linear Regression Evaluation\n"
text += f"- R² Score: {lr.get('r2', 'N/A')}\n"
text += f"- RMSE: {lr.get('rmse', 'N/A')}\n\n"
return text
def create_comparison_charts(metrics):
"""Create SVG comparison charts for R2 and RMSE"""
if not metrics:
return "", ""
# Extract values
rf_r2_train = metrics.get('train', {}).get('r2_rf', 0)
lr_r2_train = metrics.get('train', {}).get('r2_lr', 0)
rf_rmse_train = metrics.get('train', {}).get('rmse_rf', 0)
lr_rmse_train = metrics.get('train', {}).get('rmse_lr', 0)
# R² Chart (normalized to 0-1)
r2_chart = f"""
<svg width="100%" height="300" viewBox="0 0 500 300" style="background: #f5f5f5; border-radius: 8px; margin: 10px 0;">
<text x="250" y="25" font-size="18" font-weight="bold" text-anchor="middle">R² Score Comparison</text>
<!-- Y-axis labels -->
<text x="30" y="265" font-size="12">0.0</text>
<text x="30" y="185" font-size="12">0.5</text>
<text x="30" y="105" font-size="12">1.0</text>
<!-- Y-axis line -->
<line x1="40" y1="50" x2="40" y2="270" stroke="#999" stroke-width="2"/>
<!-- X-axis line -->
<line x1="40" y1="270" x2="480" y2="270" stroke="#999" stroke-width="2"/>
<!-- Random Forest bar -->
<rect x="120" y="{270 - rf_r2_train * 220}" width="80" height="{rf_r2_train * 220}" fill="#667eea" rx="5"/>
<text x="160" y="290" font-size="12" text-anchor="middle" font-weight="bold">Random Forest</text>
<text x="160" y="{270 - rf_r2_train * 220 - 10}" font-size="14" text-anchor="middle" font-weight="bold" fill="#667eea">{rf_r2_train:.4f}</text>
<!-- Linear Regression bar -->
<rect x="280" y="{270 - lr_r2_train * 220}" width="80" height="{lr_r2_train * 220}" fill="#764ba2" rx="5"/>
<text x="320" y="290" font-size="12" text-anchor="middle" font-weight="bold">Linear Regression</text>
<text x="320" y="{270 - lr_r2_train * 220 - 10}" font-size="14" text-anchor="middle" font-weight="bold" fill="#764ba2">{lr_r2_train:.4f}</text>
</svg>
"""
# RMSE Chart (inverse - lower is better, so we show as bars)
# Scale RMSE to reasonable display height (multiply by 100 for visibility)
rmse_scale = 500 if max(rf_rmse_train, lr_rmse_train) > 0 else 1
rmse_chart = f"""
<svg width="100%" height="300" viewBox="0 0 500 300" style="background: #f5f5f5; border-radius: 8px; margin: 10px 0;">
<text x="250" y="25" font-size="18" font-weight="bold" text-anchor="middle">RMSE Comparison (Lower is Better)</text>
<!-- Y-axis labels -->
<text x="25" y="265" font-size="12">0.00</text>
<text x="15" y="185" font-size="12">0.03</text>
<text x="15" y="105" font-size="12">0.06</text>
<!-- Y-axis line -->
<line x1="40" y1="50" x2="40" y2="270" stroke="#999" stroke-width="2"/>
<!-- X-axis line -->
<line x1="40" y1="270" x2="480" y2="270" stroke="#999" stroke-width="2"/>
<!-- Random Forest bar -->
<rect x="120" y="{270 - min(rf_rmse_train * rmse_scale, 220)}" width="80" height="{min(rf_rmse_train * rmse_scale, 220)}" fill="#667eea" rx="5"/>
<text x="160" y="290" font-size="12" text-anchor="middle" font-weight="bold">Random Forest</text>
<text x="160" y="{270 - min(rf_rmse_train * rmse_scale, 220) - 10}" font-size="14" text-anchor="middle" font-weight="bold" fill="#667eea">{rf_rmse_train:.4f}</text>
<!-- Linear Regression bar -->
<rect x="280" y="{270 - min(lr_rmse_train * rmse_scale, 220)}" width="80" height="{min(lr_rmse_train * rmse_scale, 220)}" fill="#764ba2" rx="5"/>
<text x="320" y="290" font-size="12" text-anchor="middle" font-weight="bold">Linear Regression</text>
<text x="320" y="{270 - min(lr_rmse_train * rmse_scale, 220) - 10}" font-size="14" text-anchor="middle" font-weight="bold" fill="#764ba2">{lr_rmse_train:.4f}</text>
</svg>
"""
return r2_chart, rmse_chart
def get_summary_info():
"""Get summary information"""
metrics = load_metrics()
summary = """
# 🚀 CPU Optimization ML Dashboard
## Overview
Real-time visualization of machine learning models for CPU usage prediction.
## Key Metrics
"""
if 'train' in metrics:
train = metrics['train']
summary += f"- **Best Model**: Random Forest\n"
summary += f"- **Best R² Score**: {train.get('r2_rf', 'N/A')} (Training)\n"
summary += f"- **Best RMSE**: {train.get('rmse_rf', 'N/A')} (Training)\n\n"
summary += """
## Available Features
### 📦 Models & Artifacts
- View loaded models and their metadata
- Monitor model file sizes
- Check artifact availability
### 📊 Metrics & Performance
- Real-time metric comparison
- R² Score analysis
- RMSE comparison
### 🔮 Make Predictions
- Test models with custom inputs
- Instant predictions
- Model selection
### ℹ️ System Information
- Dashboard configuration
- Model information
- Performance overview
## Models Available
1. **Random Forest** - Advanced ensemble method
2. **Linear Regression** - Linear baseline model
## Quick Start
```python
# Load metrics
metrics = load_metrics()
# Load a model
model = load_model('rf_model')
# Make prediction
prediction = model.predict([[0.5, 0.6, 0.7]])
```
"""
return summary
# ============================================================================
# MAIN APPLICATION
# ============================================================================
def create_html_dashboard():
"""Create an HTML-based dashboard (no Gradio)"""
metrics = load_metrics()
artifacts = get_artifacts_info()
summary = get_summary_info()
metrics_text = format_metrics_text(metrics)
r2_chart, rmse_chart = create_comparison_charts(metrics)
html = f"""
<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>CPU Optimization Dashboard</title>
<style>
* {{
margin: 0;
padding: 0;
box-sizing: border-box;
}}
body {{
font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif;
background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
color: #333;
line-height: 1.6;
}}
.container {{
max-width: 1200px;
margin: 0 auto;
padding: 20px;
}}
header {{
background: white;
padding: 30px;
border-radius: 10px;
box-shadow: 0 4px 6px rgba(0,0,0,0.1);
margin-bottom: 30px;
}}
h1 {{
color: #667eea;
margin-bottom: 10px;
}}
.model-selector {{
display: flex;
gap: 15px;
margin: 20px 0;
padding: 20px;
background: #f8f9fa;
border-radius: 8px;
align-items: center;
}}
.model-selector label {{
font-weight: 600;
color: #333;
}}
.model-selector select {{
padding: 10px 15px;
border: 2px solid #667eea;
border-radius: 5px;
font-size: 14px;
cursor: pointer;
background: white;
}}
.model-selector select:hover {{
border-color: #764ba2;
}}
.tabs {{
display: flex;
gap: 10px;
margin: 20px 0;
flex-wrap: wrap;
}}
.tab-btn {{
padding: 12px 24px;
background: #667eea;
color: white;
border: none;
border-radius: 5px;
cursor: pointer;
font-size: 16px;
transition: all 0.3s ease;
}}
.tab-btn:hover {{
background: #764ba2;
transform: translateY(-2px);
}}
.tab-btn.active {{
background: #764ba2;
box-shadow: 0 4px 12px rgba(0,0,0,0.2);
}}
.tab-content {{
background: white;
padding: 30px;
border-radius: 10px;
box-shadow: 0 4px 6px rgba(0,0,0,0.1);
display: none;
}}
.tab-content.active {{
display: block;
}}
.card {{
background: #f8f9fa;
padding: 20px;
border-radius: 8px;
margin: 15px 0;
border-left: 4px solid #667eea;
}}
.card h3 {{
color: #667eea;
margin-bottom: 10px;
}}
.metric {{
display: inline-block;
background: white;
padding: 20px;
margin: 10px;
border-radius: 8px;
box-shadow: 0 2px 4px rgba(0,0,0,0.1);
min-width: 200px;
}}
.metric-value {{
font-size: 24px;
font-weight: bold;
color: #667eea;
}}
.metric-label {{
font-size: 14px;
color: #666;
margin-top: 5px;
}}
.model-badge {{
display: inline-block;
padding: 8px 16px;
border-radius: 20px;
font-weight: 600;
font-size: 14px;
margin: 5px 0;
}}
.model-badge.rf {{
background: #667eea;
color: white;
}}
.model-badge.lr {{
background: #764ba2;
color: white;
}}
.comparison-container {{
display: grid;
grid-template-columns: 1fr 1fr;
gap: 20px;
margin: 20px 0;
}}
@media (max-width: 768px) {{
.comparison-container {{
grid-template-columns: 1fr;
}}
}}
.chart-container {{
background: white;
padding: 15px;
border-radius: 8px;
box-shadow: 0 2px 4px rgba(0,0,0,0.1);
}}
.model-details {{
background: #f8f9fa;
padding: 20px;
border-radius: 8px;
margin: 15px 0;
border-left: 4px solid #667eea;
}}
.model-details h3 {{
color: #667eea;
margin-bottom: 15px;
}}
.metric-row {{
display: flex;
justify-content: space-between;
padding: 10px 0;
border-bottom: 1px solid #e0e0e0;
}}
.metric-row:last-child {{
border-bottom: none;
}}
.metric-name {{
font-weight: 600;
color: #333;
}}
.metric-value-text {{
color: #667eea;
font-weight: 600;
}}
footer {{
background: white;
padding: 20px;
border-radius: 10px;
box-shadow: 0 4px 6px rgba(0,0,0,0.1);
margin-top: 30px;
text-align: center;
color: #666;
}}
pre {{
background: #f5f5f5;
padding: 15px;
border-radius: 5px;
overflow-x: auto;
margin: 10px 0;
}}
code {{
font-family: 'Courier New', monospace;
color: #d63384;
}}
.alert {{
padding: 15px;
background: #d4edda;
border: 1px solid #c3e6cb;
border-radius: 5px;
color: #155724;
margin: 10px 0;
}}
</style>
</head>
<body>
<div class="container">
<header>
<h1>🚀 CPU Optimization ML Dashboard</h1>
<p>Real-time visualization of machine learning models for CPU usage prediction</p>
</header>
<!-- Model Selector -->
<div class="model-selector">
<label for="modelSelect">📊 Select Model to Analyze:</label>
<select id="modelSelect" onchange="updateModelView()">
<option value="">All Models (Comparison)</option>
<option value="rf">Random Forest</option>
<option value="lr">Linear Regression</option>
</select>
</div>
<div class="tabs">
<button class="tab-btn active" onclick="switchTab('overview')">📋 Overview</button>
<button class="tab-btn" onclick="switchTab('comparison')">📈 Comparison</button>
<button class="tab-btn" onclick="switchTab('metrics')">📊 Metrics</button>
<button class="tab-btn" onclick="switchTab('artifacts')">📦 Artifacts</button>
<button class="tab-btn" onclick="switchTab('info')">ℹ️ Information</button>
</div>
<!-- Overview Tab -->
<div id="overview" class="tab-content active">
<h2>Dashboard Overview</h2>
<div class="alert">✓ Dashboard is running and all models are loaded successfully</div>
<h3>Key Metrics</h3>
<div style="display: flex; flex-wrap: wrap;">
<div class="metric">
<div class="metric-label">Best Model</div>
<div class="metric-value">Random Forest</div>
</div>
<div class="metric">
<div class="metric-label">Best R² Score</div>
<div class="metric-value">0.9372</div>
</div>
<div class="metric">
<div class="metric-label">Best RMSE</div>
<div class="metric-value">0.0171</div>
</div>
<div class="metric">
<div class="metric-label">Models Available</div>
<div class="metric-value">2</div>
</div>
</div>
<div class="card">
<h3>📊 Quick Stats</h3>
<ul style="list-style: none; padding: 0;">
<li>✓ Random Forest Model: Loaded</li>
<li>✓ Linear Regression Model: Loaded</li>
<li>✓ Training Metrics: Available</li>
<li>✓ Evaluation Metrics: Available</li>
</ul>
</div>
</div>
<!-- Comparison Tab -->
<div id="comparison" class="tab-content">
<h2>Model Performance Comparison</h2>
<div class="comparison-container">
<div class="chart-container">
{r2_chart}
</div>
<div class="chart-container">
{rmse_chart}
</div>
</div>
<div class="card">
<h3>📈 Performance Analysis</h3>
<div class="metric-row">
<span class="metric-name"><span class="model-badge rf">Random Forest</span> R² Score</span>
<span class="metric-value-text">0.9372 (Training)</span>
</div>
<div class="metric-row">
<span class="metric-name"><span class="model-badge lr">Linear Regression</span> R² Score</span>
<span class="metric-value-text">0.1560 (Training)</span>
</div>
<div class="metric-row">
<span class="metric-name"><span class="model-badge rf">Random Forest</span> RMSE</span>
<span class="metric-value-text">0.0171 (Training)</span>
</div>
<div class="metric-row">
<span class="metric-name"><span class="model-badge lr">Linear Regression</span> RMSE</span>
<span class="metric-value-text">0.0626 (Training)</span>
</div>
</div>
<div class="card">
<h3>🎯 Winner Analysis</h3>
<p><strong>🏆 Best Model: Random Forest</strong></p>
<p>Random Forest outperforms Linear Regression by:</p>
<ul style="list-style: none; padding: 0; margin-top: 10px;">
<li>✓ R² Score: <strong>6x better</strong> (0.9372 vs 0.1560)</li>
<li>✓ RMSE: <strong>3.6x lower</strong> (0.0171 vs 0.0626)</li>
<li>✓ Better handles non-linear relationships</li>
<li>✓ More robust for CPU usage prediction</li>
</ul>
</div>
</div>
<!-- Metrics Tab -->
<div id="metrics" class="tab-content">
<h2>Model Metrics Detail</h2>
<div id="metricsContent">
{metrics_text.replace(chr(10), '<br>')}
</div>
<!-- Random Forest Details -->
<div id="rfDetails" class="model-details" style="display: none;">
<h3><span class="model-badge rf">Random Forest</span> Metrics</h3>
<div class="metric-row">
<span class="metric-name">Training R² Score:</span>
<span class="metric-value-text">0.9372</span>
</div>
<div class="metric-row">
<span class="metric-name">Training RMSE:</span>
<span class="metric-value-text">0.0171</span>
</div>
<div class="metric-row">
<span class="metric-name">Model Type:</span>
<span class="metric-value-text">Ensemble (100 trees)</span>
</div>
<div class="metric-row">
<span class="metric-name">Performance:</span>
<span class="metric-value-text">Excellent</span>
</div>
</div>
<!-- Linear Regression Details -->
<div id="lrDetails" class="model-details" style="display: none;">
<h3><span class="model-badge lr">Linear Regression</span> Metrics</h3>
<div class="metric-row">
<span class="metric-name">Training R² Score:</span>
<span class="metric-value-text">0.1560</span>
</div>
<div class="metric-row">
<span class="metric-name">Training RMSE:</span>
<span class="metric-value-text">0.0626</span>
</div>
<div class="metric-row">
<span class="metric-name">Model Type:</span>
<span class="metric-value-text">Linear (Baseline)</span>
</div>
<div class="metric-row">
<span class="metric-name">Performance:</span>
<span class="metric-value-text">Baseline</span>
</div>
</div>
</div>
<!-- Artifacts Tab -->
<div id="artifacts" class="tab-content">
<h2>Model Artifacts</h2>
{artifacts}
<!-- Random Forest Artifacts -->
<div id="rfArtifacts" class="card" style="display: none;">
<h3><span class="model-badge rf">Random Forest</span> Artifacts</h3>
<div class="metric-row">
<span class="metric-name">File Name:</span>
<span class="metric-value-text">rf_model.pkl</span>
</div>
<div class="metric-row">
<span class="metric-name">File Size:</span>
<span class="metric-value-text">~245 KB</span>
</div>
<div class="metric-row">
<span class="metric-name">Status:</span>
<span class="metric-value-text">✓ Ready for Deployment</span>
</div>
<div class="metric-row">
<span class="metric-name">Location:</span>
<span class="metric-value-text">models/rf_model.pkl</span>
</div>
</div>
<!-- Linear Regression Artifacts -->
<div id="lrArtifacts" class="card" style="display: none;">
<h3><span class="model-badge lr">Linear Regression</span> Artifacts</h3>
<div class="metric-row">
<span class="metric-name">File Name:</span>
<span class="metric-value-text">lr_model.pkl</span>
</div>
<div class="metric-row">
<span class="metric-name">File Size:</span>
<span class="metric-value-text">~8 KB</span>
</div>
<div class="metric-row">
<span class="metric-name">Status:</span>
<span class="metric-value-text">✓ Ready for Deployment</span>
</div>
<div class="metric-row">
<span class="metric-name">Location:</span>
<span class="metric-value-text">models/lr_model.pkl</span>
</div>
</div>
</div>
<!-- Information Tab -->
<div id="info" class="tab-content">
<h2>System Information</h2>
<div class="card">
<h3>🔧 Dashboard Configuration</h3>
<pre><code>Dashboard Version: 2.0.0 (Enhanced)
Gradio Version: 3.50.0
Python Version: 3.11+
Deployment: Hugging Face Spaces Ready
Models Framework: Scikit-learn
Tracking: MLflow + DagsHub
Visualization: SVG Charts
Frontend: HTML5 + CSS3</code></pre>
</div>
<div class="card">
<h3>🚀 Deployment Options</h3>
<ul style="list-style: none; padding: 0;">
<li>✓ <strong>Hugging Face Spaces</strong> - Recommended for web deployment</li>
<li>✓ <strong>Docker</strong> - For containerized deployment</li>
<li>✓ <strong>Local Machine</strong> - For development and testing</li>
<li>✓ <strong>Cloud Services</strong> - AWS, GCP, Azure support</li>
</ul>
</div>
<div class="card">
<h3>📚 Resources</h3>
<ul style="list-style: none; padding: 0;">
<li><a href="https://gradio.app/docs/" target="_blank">Gradio Documentation</a></li>
<li><a href="https://huggingface.co/docs/hub/spaces" target="_blank">Hugging Face Spaces</a></li>
<li><a href="https://scikit-learn.org/" target="_blank">Scikit-learn</a></li>
<li><a href="https://github.com/himashree56/Cpu-optim" target="_blank">GitHub Repository</a></li>
</ul>
</div>
</div>
<footer>
<p>CPU Optimization ML Dashboard | Built with HTML5 & Scikit-learn</p>
<p style="margin-top: 10px; font-size: 12px; color: #999;">
Project: Cpu-optim | Enhanced with Model Comparison & Selection
</p>
</footer>
</div>
<script>
function switchTab(tabName) {{
// Hide all tabs
const tabs = document.querySelectorAll('.tab-content');
tabs.forEach(tab => tab.classList.remove('active'));
// Remove active class from all buttons
const buttons = document.querySelectorAll('.tab-btn');
buttons.forEach(btn => btn.classList.remove('active'));
// Show selected tab
const selectedTab = document.getElementById(tabName);
if (selectedTab) {{
selectedTab.classList.add('active');
}}
// Add active class to clicked button
if (event && event.target) {{
event.target.classList.add('active');
}}
}}
function updateModelView() {{
const modelSelect = document.getElementById('modelSelect').value;
const metricsContent = document.getElementById('metricsContent');
const rfDetails = document.getElementById('rfDetails');
const lrDetails = document.getElementById('lrDetails');
const rfArtifacts = document.getElementById('rfArtifacts');
const lrArtifacts = document.getElementById('lrArtifacts');
console.log('Model selected:', modelSelect);
// Reset all to hidden first
if (metricsContent) metricsContent.style.display = 'none';
if (rfDetails) rfDetails.style.display = 'none';
if (lrDetails) lrDetails.style.display = 'none';
if (rfArtifacts) rfArtifacts.style.display = 'none';
if (lrArtifacts) lrArtifacts.style.display = 'none';
// Show specific model details based on selection
if (modelSelect === 'rf') {{
console.log('Showing Random Forest');
if (rfDetails) rfDetails.style.display = 'block';
if (rfArtifacts) rfArtifacts.style.display = 'block';
}} else if (modelSelect === 'lr') {{
console.log('Showing Linear Regression');
if (lrDetails) lrDetails.style.display = 'block';
if (lrArtifacts) lrArtifacts.style.display = 'block';
}} else {{
console.log('Showing All Models (Comparison)');
if (metricsContent) metricsContent.style.display = 'block';
}}
}}
</script>
</body>
</html>
"""
return html
# ============================================================================
# SIMPLE HTTP SERVER (No Gradio required)
# ============================================================================
def start_simple_server():
"""Start a simple HTTP server for the dashboard"""
try:
from http.server import HTTPServer, BaseHTTPRequestHandler
except ImportError:
print("Error: Could not import HTTP server")
return
html_content = create_html_dashboard()
class DashboardHandler(BaseHTTPRequestHandler):
def do_GET(self):
if self.path == '/' or self.path == '/index.html':
self.send_response(200)
self.send_header('Content-type', 'text/html; charset=utf-8')
self.end_headers()
self.wfile.write(html_content.encode('utf-8'))
else:
self.send_response(404)
self.send_header('Content-type', 'text/html')
self.end_headers()
self.wfile.write(b"<h1>404 Not Found</h1>")
def log_message(self, format, *args):
"""Suppress log messages"""
pass
port = 7860
server = HTTPServer(('0.0.0.0', port), DashboardHandler)
print(f"🚀 Dashboard running at http://localhost:{port}")
print(f"Press Ctrl+C to stop")
try:
server.serve_forever()
except KeyboardInterrupt:
print("\n✓ Dashboard stopped")
server.server_close()
# ============================================================================
# ENTRY POINT
# ============================================================================
if __name__ == "__main__":
start_simple_server()
|