--- a/utils.py	Mon Sep 08 22:38:35 2025 +0000
+++ b/utils.py	Sat Oct 18 03:17:09 2025 +0000
@@ -104,7 +104,7 @@
     /* show ~30 rows with a scrollbar (tweak if you want) */
     .scroll-rows-30 {
       max-height: 900px;       /* ~30 rows depending on row height */
-      overflow-y: auto;        /* vertical scrollbar (“sidebar”) */
+      overflow-y: auto;        /* vertical scrollbar ("sidebar") */
       overflow-x: auto;
     }
 
@@ -212,7 +212,7 @@
         };
 
         document.querySelectorAll('table.performance-summary th.sortable').forEach(th => {
-          // initialize to “none”
+          // initialize to "none"
           th.classList.remove('sorted-asc','sorted-desc');
           th.classList.add('sorted-none');
 
@@ -394,119 +394,119 @@
         '    <span class="close">×</span>'
         "    <h2>Model Evaluation Metrics — Help Guide</h2>"
         '    <div class="metrics-guide">'
-        "      <h3>1) General Metrics (Regression and Classification)</h3>"
-        "      <p><strong>Loss (Regression & Classification):</strong> "
-        "Measures the difference between predicted and actual values, "
-        "optimized during training. Lower is better. "
-        "For regression, this is often Mean Squared Error (MSE) or "
-        "Mean Absolute Error (MAE). For classification, it’s typically "
-        "cross-entropy or log loss.</p>"
-        "      <h3>2) Regression Metrics</h3>"
-        "      <p><strong>Mean Absolute Error (MAE):</strong> "
-        "Average of absolute differences between predicted and actual values, "
-        "in the same units as the target. Use for interpretable error measurement "
-        "when all errors are equally important. Less sensitive to outliers than MSE.</p>"
-        "      <p><strong>Mean Squared Error (MSE):</strong> "
-        "Average of squared differences between predicted and actual values. "
-        "Penalizes larger errors more heavily, useful when large deviations are critical. "
-        "Often used as the loss function in regression.</p>"
-        "      <p><strong>Root Mean Squared Error (RMSE):</strong> "
-        "Square root of MSE, in the same units as the target. "
-        "Balances interpretability and sensitivity to large errors. "
-        "Widely used for regression evaluation.</p>"
-        "      <p><strong>Mean Absolute Percentage Error (MAPE):</strong> "
-        "Average absolute error as a percentage of actual values. "
-        "Scale-independent, ideal for comparing relative errors across datasets. "
-        "Avoid when actual values are near zero.</p>"
-        "      <p><strong>Root Mean Squared Percentage Error (RMSPE):</strong> "
-        "Square root of mean squared percentage error. Scale-independent, "
-        "penalizes larger relative errors more than MAPE. Use for forecasting "
-        "or when relative accuracy matters.</p>"
-        "      <p><strong>R² Score:</strong> Proportion of variance in the target "
-        "explained by the model. Ranges from negative infinity to 1 (perfect prediction). "
-        "Use to assess model fit; negative values indicate poor performance "
-        "compared to predicting the mean.</p>"
-        "      <h3>3) Classification Metrics</h3>"
-        "      <p><strong>Accuracy:</strong> Proportion of correct predictions "
-        "among all predictions. Simple but misleading for imbalanced datasets, "
-        "where high accuracy may hide poor performance on minority classes.</p>"
-        "      <p><strong>Micro Accuracy:</strong> Sums true positives and true negatives "
-        "across all classes before computing accuracy. Suitable for multiclass or "
-        "multilabel problems with imbalanced data.</p>"
-        "      <p><strong>Token Accuracy:</strong> Measures how often predicted tokens "
-        "(e.g., in sequences) match true tokens. Common in NLP tasks like text generation "
-        "or token classification.</p>"
-        "      <p><strong>Precision:</strong> Proportion of positive predictions that are "
-        "correct (TP / (TP + FP)). Use when false positives are costly, e.g., spam detection.</p>"
-        "      <p><strong>Recall (Sensitivity):</strong> Proportion of actual positives "
-        "correctly predicted (TP / (TP + FN)). Use when missing positives is risky, "
-        "e.g., disease detection.</p>"
-        "      <p><strong>Specificity:</strong> True negative rate (TN / (TN + FP)). "
-        "Measures ability to identify negatives. Useful in medical testing to avoid "
-        "false alarms.</p>"
-        "      <h3>4) Classification: Macro, Micro, and Weighted Averages</h3>"
-        "      <p><strong>Macro Precision / Recall / F1:</strong> Averages the metric "
-        "across all classes, treating each equally. Best for balanced datasets where "
-        "all classes are equally important.</p>"
-        "      <p><strong>Micro Precision / Recall / F1:</strong> Aggregates true positives, "
-        "false positives, and false negatives across all classes before computing. "
-        "Ideal for imbalanced or multilabel classification.</p>"
-        "      <p><strong>Weighted Precision / Recall / F1:</strong> Averages metrics "
-        "across classes, weighted by the number of true instances per class. Balances "
-        "class importance based on frequency.</p>"
-        "      <h3>5) Classification: Average Precision (PR-AUC Variants)</h3>"
-        "      <p><strong>Average Precision Macro:</strong> Precision-Recall AUC averaged "
-        "equally across classes. Use for balanced multiclass problems.</p>"
-        "      <p><strong>Average Precision Micro:</strong> Global Precision-Recall AUC "
-        "using all instances. Best for imbalanced or multilabel classification.</p>"
-        "      <p><strong>Average Precision Samples:</strong> Precision-Recall AUC averaged "
-        "across individual samples. Ideal for multilabel tasks where samples have multiple "
-        "labels.</p>"
-        "      <h3>6) Classification: ROC-AUC Variants</h3>"
-        "      <p><strong>ROC-AUC:</strong> Measures ability to distinguish between classes. "
-        "AUC = 1 is perfect; 0.5 is random guessing. Use for binary classification.</p>"
-        "      <p><strong>Macro ROC-AUC:</strong> Averages AUC across all classes equally. "
-        "Suitable for balanced multiclass problems.</p>"
-        "      <p><strong>Micro ROC-AUC:</strong> Computes AUC from aggregated predictions "
-        "across all classes. Useful for imbalanced or multilabel settings.</p>"
-        "      <h3>7) Classification: Confusion Matrix Stats (Per Class)</h3>"
-        "      <p><strong>True Positives / Negatives (TP / TN):</strong> Correct predictions "
-        "for positives and negatives, respectively.</p>"
-        "      <p><strong>False Positives / Negatives (FP / FN):</strong> Incorrect predictions "
-        "— false alarms and missed detections.</p>"
-        "      <h3>8) Classification: Ranking Metrics</h3>"
-        "      <p><strong>Hits at K:</strong> Measures whether the true label is among the "
-        "top-K predictions. Common in recommendation systems and retrieval tasks.</p>"
-        "      <h3>9) Other Metrics (Classification)</h3>"
-        "      <p><strong>Cohen's Kappa:</strong> Measures agreement between predicted and "
-        "actual labels, adjusted for chance. Useful for multiclass classification with "
-        "imbalanced data.</p>"
-        "      <p><strong>Matthews Correlation Coefficient (MCC):</strong> Balanced measure "
-        "using TP, TN, FP, and FN. Effective for imbalanced datasets.</p>"
-        "      <h3>10) Metric Recommendations</h3>"
-        "      <ul>"
-        "        <li><strong>Regression:</strong> Use <strong>RMSE</strong> or "
-        "<strong>MAE</strong> for general evaluation, <strong>MAPE</strong> for relative "
-        "errors, and <strong>R²</strong> to assess model fit. Use <strong>MSE</strong> or "
-        "<strong>RMSPE</strong> when large errors are critical.</li>"
-        "        <li><strong>Classification (Balanced Data):</strong> Use <strong>Accuracy</strong> "
-        "and <strong>F1</strong> for overall performance.</li>"
-        "        <li><strong>Classification (Imbalanced Data):</strong> Use <strong>Precision</strong>, "
-        "<strong>Recall</strong>, and <strong>ROC-AUC</strong> to focus on minority class "
-        "performance.</li>"
-        "        <li><strong>Multilabel or Imbalanced Classification:</strong> Use "
-        "<strong>Micro Precision/Recall/F1</strong> or <strong>Micro ROC-AUC</strong>.</li>"
-        "        <li><strong>Balanced Multiclass:</strong> Use <strong>Macro Precision/Recall/F1</strong> "
-        "or <strong>Macro ROC-AUC</strong>.</li>"
-        "        <li><strong>Class Frequency Matters:</strong> Use <strong>Weighted Precision/Recall/F1</strong> "
-        "to account for class imbalance.</li>"
-        "        <li><strong>Recommendation/Ranking:</strong> Use <strong>Hits at K</strong> for retrieval tasks.</li>"
-        "        <li><strong>Detailed Analysis:</strong> Use <strong>Confusion Matrix stats</strong> "
-        "for class-wise performance in classification.</li>"
-        "      </ul>"
-        "    </div>"
-        "  </div>"
-        "</div>"
+        '      <h3>1) General Metrics (Regression and Classification)</h3>'
+        '      <p><strong>Loss (Regression & Classification):</strong> '
+        'Measures the difference between predicted and actual values, '
+        'optimized during training. Lower is better. '
+        'For regression, this is often Mean Squared Error (MSE) or '
+        'Mean Absolute Error (MAE). For classification, it\'s typically '
+        'cross-entropy or log loss.</p>'
+        '      <h3>2) Regression Metrics</h3>'
+        '      <p><strong>Mean Absolute Error (MAE):</strong> '
+        'Average of absolute differences between predicted and actual values, '
+        'in the same units as the target. Use for interpretable error measurement '
+        'when all errors are equally important. Less sensitive to outliers than MSE.</p>'
+        '      <p><strong>Mean Squared Error (MSE):</strong> '
+        'Average of squared differences between predicted and actual values. '
+        'Penalizes larger errors more heavily, useful when large deviations are critical. '
+        'Often used as the loss function in regression.</p>'
+        '      <p><strong>Root Mean Squared Error (RMSE):</strong> '
+        'Square root of MSE, in the same units as the target. '
+        'Balances interpretability and sensitivity to large errors. '
+        'Widely used for regression evaluation.</p>'
+        '      <p><strong>Mean Absolute Percentage Error (MAPE):</strong> '
+        'Average absolute error as a percentage of actual values. '
+        'Scale-independent, ideal for comparing relative errors across datasets. '
+        'Avoid when actual values are near zero.</p>'
+        '      <p><strong>Root Mean Squared Percentage Error (RMSPE):</strong> '
+        'Square root of mean squared percentage error. Scale-independent, '
+        'penalizes larger relative errors more than MAPE. Use for forecasting '
+        'or when relative accuracy matters.</p>'
+        '      <p><strong>R² Score:</strong> Proportion of variance in the target '
+        'explained by the model. Ranges from negative infinity to 1 (perfect prediction). '
+        'Use to assess model fit; negative values indicate poor performance '
+        'compared to predicting the mean.</p>'
+        '      <h3>3) Classification Metrics</h3>'
+        '      <p><strong>Accuracy:</strong> Proportion of correct predictions '
+        'among all predictions. Simple but misleading for imbalanced datasets, '
+        'where high accuracy may hide poor performance on minority classes.</p>'
+        '      <p><strong>Micro Accuracy:</strong> Sums true positives and true negatives '
+        'across all classes before computing accuracy. Suitable for multiclass or '
+        'multilabel problems with imbalanced data.</p>'
+        '      <p><strong>Token Accuracy:</strong> Measures how often predicted tokens '
+        '(e.g., in sequences) match true tokens. Common in NLP tasks like text generation '
+        'or token classification.</p>'
+        '      <p><strong>Precision:</strong> Proportion of positive predictions that are '
+        'correct (TP / (TP + FP)). Use when false positives are costly, e.g., spam detection.</p>'
+        '      <p><strong>Recall (Sensitivity):</strong> Proportion of actual positives '
+        'correctly predicted (TP / (TP + FN)). Use when missing positives is risky, '
+        'e.g., disease detection.</p>'
+        '      <p><strong>Specificity:</strong> True negative rate (TN / (TN + FP)). '
+        'Measures ability to identify negatives. Useful in medical testing to avoid '
+        'false alarms.</p>'
+        '      <h3>4) Classification: Macro, Micro, and Weighted Averages</h3>'
+        '      <p><strong>Macro Precision / Recall / F1:</strong> Averages the metric '
+        'across all classes, treating each equally. Best for balanced datasets where '
+        'all classes are equally important.</p>'
+        '      <p><strong>Micro Precision / Recall / F1:</strong> Aggregates true positives, '
+        'false positives, and false negatives across all classes before computing. '
+        'Ideal for imbalanced or multilabel classification.</p>'
+        '      <p><strong>Weighted Precision / Recall / F1:</strong> Averages metrics '
+        'across classes, weighted by the number of true instances per class. Balances '
+        'class importance based on frequency.</p>'
+        '      <h3>5) Classification: Average Precision (PR-AUC Variants)</h3>'
+        '      <p><strong>Average Precision Macro:</strong> Precision-Recall AUC averaged '
+        'equally across classes. Use for balanced multiclass problems.</p>'
+        '      <p><strong>Average Precision Micro:</strong> Global Precision-Recall AUC '
+        'using all instances. Best for imbalanced or multilabel classification.</p>'
+        '      <p><strong>Average Precision Samples:</strong> Precision-Recall AUC averaged '
+        'across individual samples. Ideal for multilabel tasks where samples have multiple '
+        'labels.</p>'
+        '      <h3>6) Classification: ROC-AUC Variants</h3>'
+        '      <p><strong>ROC-AUC:</strong> Measures ability to distinguish between classes. '
+        'AUC = 1 is perfect; 0.5 is random guessing. Use for binary classification.</p>'
+        '      <p><strong>Macro ROC-AUC:</strong> Averages AUC across all classes equally. '
+        'Suitable for balanced multiclass problems.</p>'
+        '      <p><strong>Micro ROC-AUC:</strong> Computes AUC from aggregated predictions '
+        'across all classes. Useful for imbalanced or multilabel settings.</p>'
+        '      <h3>7) Classification: Confusion Matrix Stats (Per Class)</h3>'
+        '      <p><strong>True Positives / Negatives (TP / TN):</strong> Correct predictions '
+        'for positives and negatives, respectively.</p>'
+        '      <p><strong>False Positives / Negatives (FP / FN):</strong> Incorrect predictions '
+        '— false alarms and missed detections.</p>'
+        '      <h3>8) Classification: Ranking Metrics</h3>'
+        '      <p><strong>Hits at K:</strong> Measures whether the true label is among the '
+        'top-K predictions. Common in recommendation systems and retrieval tasks.</p>'
+        '      <h3>9) Other Metrics (Classification)</h3>'
+        '      <p><strong>Cohen\'s Kappa:</strong> Measures agreement between predicted and '
+        'actual labels, adjusted for chance. Useful for multiclass classification with '
+        'imbalanced data.</p>'
+        '      <p><strong>Matthews Correlation Coefficient (MCC):</strong> Balanced measure '
+        'using TP, TN, FP, and FN. Effective for imbalanced datasets.</p>'
+        '      <h3>10) Metric Recommendations</h3>'
+        '      <ul>'
+        '        <li><strong>Regression:</strong> Use <strong>RMSE</strong> or '
+        '<strong>MAE</strong> for general evaluation, <strong>MAPE</strong> for relative '
+        'errors, and <strong>R²</strong> to assess model fit. Use <strong>MSE</strong> or '
+        '<strong>RMSPE</strong> when large errors are critical.</li>'
+        '        <li><strong>Classification (Balanced Data):</strong> Use <strong>Accuracy</strong> '
+        'and <strong>F1</strong> for overall performance.</li>'
+        '        <li><strong>Classification (Imbalanced Data):</strong> Use <strong>Precision</strong>, '
+        '<strong>Recall</strong>, and <strong>ROC-AUC</strong> to focus on minority class '
+        'performance.</li>'
+        '        <li><strong>Multilabel or Imbalanced Classification:</strong> Use '
+        '<strong>Micro Precision/Recall/F1</strong> or <strong>Micro ROC-AUC</strong>.</li>'
+        '        <li><strong>Balanced Multiclass:</strong> Use <strong>Macro Precision/Recall/F1</strong> '
+        'or <strong>Macro ROC-AUC</strong>.</li>'
+        '        <li><strong>Class Frequency Matters:</strong> Use <strong>Weighted Precision/Recall/F1</strong> '
+        'to account for class imbalance.</li>'
+        '        <li><strong>Recommendation/Ranking:</strong> Use <strong>Hits at K</strong> for retrieval tasks.</li>'
+        '        <li><strong>Detailed Analysis:</strong> Use <strong>Confusion Matrix stats</strong> '
+        'for class-wise performance in classification.</li>'
+        '      </ul>'
+        '    </div>'
+        '  </div>'
+        '</div>'
     )
 
     modal_js = (