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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
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