(FEAT): implement confidence calculation and visualization utilities

utils/confidence.py

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+"""Confidence calculation and visualization utilities.
+Provides normalized softmax confidence and color-coded badges"""
+from typing import Tuple, List
+import numpy as np
+import torch
+import torch.nn.functional as F
+
+
+def calculate_softmax_confidence(logits: torch.Tensor) -> Tuple[np.ndarray, float, str, str]:
+    """Calculate normalized confidence using softmax
+    Args:
+        logits: Raw model logits tensor
+    Returns:
+        Tuple of (probabilities, max_confidence, confidence_level, confidence_emoji)
+    """
+    # ===Apply softmax to get probabilities===
+    probs_np = F.softmax(logits, dim=1).cpu().numpy().flatten()
+
+    # ===Get maximum probability as confidence===
+    max_confidence = float(np.max(probs_np))
+
+    # ===Determine confidence level and emoji===
+    if max_confidence >= 0.80:
+        confidence_level = "HIGH"
+        confidence_emoji = "🟢"
+    elif max_confidence >= 0.60:
+        confidence_level = "MEDIUM"
+        confidence_emoji = "🟡"
+    else:
+        confidence_level = "LOW"
+        confidence_emoji = "🔴"
+
+    return probs_np, max_confidence, confidence_level, confidence_emoji
+
+
+def get_confidence_badge(confidence: float) -> Tuple[str, str]:
+    """Get confidence badge emoji and level description
+    Args:
+        confidence: Confidence value (0-1)
+    Returns:
+        Tuple of (emoji, level)
+    """
+    if confidence >= 0.80:
+        return "🟢", "HIGH"
+    elif confidence >= 0.60:
+        return "🟡", "MEDIUM"
+    else:
+        return "🔴", "LOW"
+
+
+def format_confidence_display(confidence: float, level: str, emoji: str) -> str:
+    """
+    Format confidence for display in UI
+
+    Args:
+        confidence: Confidence value (0-1)
+        level: Confidence level (HIGH/MEDIUM/LOW)
+        emoji: Confidence emoji
+
+    Returns:
+        Formatted confidence string
+    """
+    return f"{emoji} **{level}** ({confidence:.1%})"
+
+
+def create_confidence_progress_html(
+    probabilities: np.ndarray,
+    labels: List[str],
+    highlight_idx: int
+) -> str:
+    """
+    Create HTML for confidence progress bars
+
+    Args:
+        probabilities: Array of class probabilities
+        labels: List of class labels
+        highlight_idx: Index of predicted class to highlight
+
+    Returns:
+        HTML string for progress bars
+    """
+    if len(probabilities) == 0 or len(labels) == 0:
+        return "<p>No confidence data available</p>"
+
+    html_parts = []
+
+    for i, (prob, label) in enumerate(zip(probabilities, labels)):
+        # ===Color based on whether this is the predicted class===
+        if i == highlight_idx:
+            if prob >= 0.80:
+                color = "#22c55e"  # green-500
+                text_color = "#ffffff"
+            elif prob >= 0.60:
+                color = "#eab308"  # yellow-500
+                text_color = "#000000"
+            else:
+                color = "#ef4444"  # red-500
+                text_color = "#ffffff"
+        else:
+            color = "#e5e7eb"  # gray-200
+            text_color = "#6b7280"  # gray-500
+
+            percentage = prob * 100
+
+            html_parts.append(f"""
+            <div style="margin-bottom: 8px;">
+                <div style="display: flex; justify-content: space-between; align-items: center; margin-bottom: 4px;">
+                    <span style="font-size: 0.875rem; font-weight: 500; color: #374151;">{label}</span>
+                    <span style="font-size: 0.875rem; color: #6b7280;">{percentage:.1f}%</span>
+                </div>
+                <div style="width: 100%; background-color: #f3f4f6; border-radius: 0.375rem; height: 20px; overflow: hidden;">
+                    <div style="
+                        width: {percentage}%; 
+                        height: 100%; 
+                        background-color: {color}; 
+                        display: flex; 
+                        align-items: center; 
+                        justify-content: center;
+                        transition: width 0.3s ease;
+                    ">
+                        {f'<span style="color: {text_color}; font-size: 0.75rem; font-weight: 600;">{percentage:.1f}%</span>' if percentage > 20 else ''}
+                </div>
+            </div>
+        </div>
+        """)
+
+    return f""" 
+    <div style="padding: 16px; background-color: #f9fafb; border-radius: 0.5rem; border: 1px solid #e5e7eb;">
+        <h4 style="margin: 0 0 12px 0; font-size: 1rem; color: #374151;">Confidence Breakdown</h4>
+        {''.join(html_parts)}
+    </div>
+    """
+
+
+def calculate_legacy_confidence(logits_list: List[float]) -> Tuple[float, str, str]:
+    """
+    Calculate confidence using legacy logit margin method for backward compatibility
+
+    Args:
+        logits_list: List of raw logits
+
+    Returns:
+        Tuple of (margin, confidence_level, confidence_emoji)
+    """
+    if len(logits_list) < 2:
+        return 0.0, "LOW", "🔴"
+
+    logits_array = np.array(logits_list)
+    sorted_logits = np.sort(logits_array)[::-1]  # Descending order
+    margin = sorted_logits[0] - sorted_logits[1]
+
+    # ===Define thresholds for margin-based confidence===
+    if margin >= 2.0:
+        confidence_level = "HIGH"
+        confidence_emoji = "🟢"
+    elif margin >= 1.0:
+        confidence_level = "MEDIUM"
+        confidence_emoji = "🟡"
+    else:
+        confidence_level = "LOW"
+        confidence_emoji = "🔴"
+
+    return margin, confidence_level, confidence_emoji