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|
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""" |
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|
Generate an interactive HTML visualization for the gloss-to-feature alignment. |
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|
This mirrors the frame_alignment.png layout but lets viewers adjust confidence thresholds. |
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|
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Usage: |
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|
python generate_interactive_alignment.py <sample_dir> |
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|
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Example: |
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python generate_interactive_alignment.py detailed_prediction_20251226_022246/sample_000 |
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""" |
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|
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import sys |
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import json |
|
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import numpy as np |
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from pathlib import Path |
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def generate_interactive_html(sample_dir, output_path): |
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"""Create the interactive alignment HTML for the given sample directory.""" |
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sample_dir = Path(sample_dir) |
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attention_weights = np.load(sample_dir / "attention_weights.npy") |
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if attention_weights.ndim == 2: |
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attn_weights = attention_weights |
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elif attention_weights.ndim == 3: |
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attn_weights = attention_weights[:, :, 0] |
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else: |
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raise ValueError(f"Unexpected attention weights shape: {attention_weights.shape}") |
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with open(sample_dir / "translation.txt", 'r') as f: |
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lines = f.readlines() |
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gloss_sequence = None |
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for line in lines: |
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if line.startswith('Clean:'): |
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gloss_sequence = line.replace('Clean:', '').strip() |
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break |
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if not gloss_sequence: |
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print("Error: translation text not found") |
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return |
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glosses = gloss_sequence.split() |
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num_glosses = len(glosses) |
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num_features = attn_weights.shape[1] |
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print(f"Gloss sequence: {glosses}") |
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print(f"Feature count: {num_features}") |
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print(f"Attention shape: {attn_weights.shape}") |
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attn_data = [] |
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for word_idx in range(min(num_glosses, attn_weights.shape[0])): |
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weights = attn_weights[word_idx, :].tolist() |
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attn_data.append({ |
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'word': glosses[word_idx], |
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'word_idx': word_idx, |
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'weights': weights |
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}) |
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html_content = f"""<!DOCTYPE html> |
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<html lang="en"> |
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<head> |
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<meta charset="UTF-8"> |
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<meta name="viewport" content="width=device-width, initial-scale=1.0"> |
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<title>Interactive Word-Frame Alignment</title> |
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<style> |
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body {{ |
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font-family: 'Arial', sans-serif; |
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margin: 20px; |
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background-color: #f5f5f5; |
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}} |
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.container {{ |
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max-width: 1800px; |
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margin: 0 auto; |
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background-color: white; |
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padding: 30px; |
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border-radius: 8px; |
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box-shadow: 0 2px 10px rgba(0,0,0,0.1); |
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}} |
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h1 {{ |
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color: #333; |
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border-bottom: 3px solid #4CAF50; |
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padding-bottom: 10px; |
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margin-bottom: 20px; |
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}} |
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.stats {{ |
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background-color: #E3F2FD; |
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padding: 15px; |
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border-radius: 5px; |
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margin-bottom: 20px; |
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border-left: 4px solid #2196F3; |
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|
font-size: 14px; |
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|
}} |
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|
.controls {{ |
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|
background-color: #f9f9f9; |
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|
padding: 20px; |
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border-radius: 5px; |
|
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margin-bottom: 30px; |
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|
border: 1px solid #ddd; |
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|
}} |
|
|
.control-group {{ |
|
|
margin-bottom: 15px; |
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|
}} |
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label {{ |
|
|
font-weight: bold; |
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|
display: inline-block; |
|
|
width: 250px; |
|
|
color: #555; |
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|
}} |
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input[type="range"] {{ |
|
|
width: 400px; |
|
|
vertical-align: middle; |
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|
}} |
|
|
.value-display {{ |
|
|
display: inline-block; |
|
|
width: 80px; |
|
|
font-family: monospace; |
|
|
font-size: 14px; |
|
|
color: #2196F3; |
|
|
font-weight: bold; |
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|
}} |
|
|
.reset-btn {{ |
|
|
margin-top: 15px; |
|
|
padding: 10px 25px; |
|
|
background-color: #2196F3; |
|
|
color: white; |
|
|
border: none; |
|
|
border-radius: 5px; |
|
|
cursor: pointer; |
|
|
font-size: 14px; |
|
|
font-weight: bold; |
|
|
}} |
|
|
.reset-btn:hover {{ |
|
|
background-color: #1976D2; |
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|
}} |
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|
canvas {{ |
|
|
border: 1px solid #999; |
|
|
display: block; |
|
|
margin: 20px auto; |
|
|
background: white; |
|
|
}} |
|
|
.legend {{ |
|
|
margin-top: 20px; |
|
|
padding: 15px; |
|
|
background-color: #fff; |
|
|
border: 1px solid #ddd; |
|
|
border-radius: 5px; |
|
|
}} |
|
|
.legend-item {{ |
|
|
display: inline-block; |
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|
margin-right: 25px; |
|
|
font-size: 13px; |
|
|
margin-bottom: 10px; |
|
|
}} |
|
|
.color-box {{ |
|
|
display: inline-block; |
|
|
width: 30px; |
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|
height: 15px; |
|
|
margin-right: 8px; |
|
|
vertical-align: middle; |
|
|
border: 1px solid #666; |
|
|
}} |
|
|
.info-panel {{ |
|
|
margin-top: 20px; |
|
|
padding: 15px; |
|
|
background-color: #f9f9f9; |
|
|
border-radius: 5px; |
|
|
border: 1px solid #ddd; |
|
|
}} |
|
|
.confidence {{ |
|
|
display: inline-block; |
|
|
padding: 3px 10px; |
|
|
border-radius: 10px; |
|
|
font-weight: bold; |
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|
font-size: 11px; |
|
|
text-transform: uppercase; |
|
|
}} |
|
|
.confidence.high {{ |
|
|
background-color: #4CAF50; |
|
|
color: white; |
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|
}} |
|
|
.confidence.medium {{ |
|
|
background-color: #FF9800; |
|
|
color: white; |
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|
}} |
|
|
.confidence.low {{ |
|
|
background-color: #f44336; |
|
|
color: white; |
|
|
}} |
|
|
</style> |
|
|
</head> |
|
|
<body> |
|
|
<div class="container"> |
|
|
<h1>🎯 Interactive Word-to-Frame Alignment Visualizer</h1> |
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|
|
|
<div class="stats"> |
|
|
<strong>Translation:</strong> {' '.join(glosses)}<br> |
|
|
<strong>Total Words:</strong> {num_glosses} | |
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|
<strong>Total Features:</strong> {num_features} |
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|
</div> |
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|
<div class="controls"> |
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|
<h3>⚙️ Threshold Controls</h3> |
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|
|
|
<div class="control-group"> |
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|
<label for="peak-threshold">Peak Threshold (% of max):</label> |
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|
<input type="range" id="peak-threshold" min="1" max="100" value="90" step="1"> |
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|
<span class="value-display" id="peak-threshold-value">90%</span> |
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|
<br> |
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|
<small style="margin-left: 255px; color: #666;"> |
|
|
A frame is considered “significant” if its attention ≥ (peak × threshold%) |
|
|
</small> |
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|
</div> |
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|
|
<div class="control-group"> |
|
|
<label for="confidence-high">High Confidence (avg attn >):</label> |
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|
<input type="range" id="confidence-high" min="0" max="100" value="50" step="1"> |
|
|
<span class="value-display" id="confidence-high-value">0.50</span> |
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|
</div> |
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|
|
<div class="control-group"> |
|
|
<label for="confidence-medium">Medium Confidence (avg attn >):</label> |
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|
<input type="range" id="confidence-medium" min="0" max="100" value="20" step="1"> |
|
|
<span class="value-display" id="confidence-medium-value">0.20</span> |
|
|
</div> |
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|
|
|
<button class="reset-btn" onclick="resetDefaults()"> |
|
|
Reset to Defaults |
|
|
</button> |
|
|
</div> |
|
|
|
|
|
<div> |
|
|
<h3>Word-to-Frame Alignment</h3> |
|
|
<p style="color: #666; font-size: 13px;"> |
|
|
Each word appears as a colored block. Width = frame span, ★ = peak frame, waveform = attention trace. |
|
|
</p> |
|
|
<canvas id="alignment-canvas" width="1600" height="600"></canvas> |
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|
|
|
<h3 style="margin-top: 30px;">Timeline Progress Bar</h3> |
|
|
<canvas id="timeline-canvas" width="1600" height="100"></canvas> |
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|
|
|
<div class="legend"> |
|
|
<strong>Legend:</strong><br><br> |
|
|
<div class="legend-item"> |
|
|
<span class="confidence high">High</span> |
|
|
<span class="confidence medium">Medium</span> |
|
|
<span class="confidence low">Low</span> |
|
|
Confidence Levels (opacity reflects confidence) |
|
|
</div> |
|
|
<div class="legend-item"> |
|
|
<span style="color: red; font-size: 20px;">★</span> |
|
|
Peak Frame (highest attention) |
|
|
</div> |
|
|
<div class="legend-item"> |
|
|
<span style="color: blue;">━</span> |
|
|
Attention Waveform (within word region) |
|
|
</div> |
|
|
</div> |
|
|
</div> |
|
|
|
|
|
<div class="info-panel"> |
|
|
<h3>Alignment Details</h3> |
|
|
<div id="alignment-details"></div> |
|
|
</div> |
|
|
</div> |
|
|
|
|
|
<script> |
|
|
// Attention data from Python |
|
|
const attentionData = {json.dumps(attn_data, ensure_ascii=False)}; |
|
|
const numGlosses = {num_glosses}; |
|
|
const numFeatures = {num_features}; |
|
|
|
|
|
// Colors for different words (matching matplotlib tab20) |
|
|
const colors = [ |
|
|
'#1f77b4', '#ff7f0e', '#2ca02c', '#d62728', '#9467bd', |
|
|
'#8c564b', '#e377c2', '#7f7f7f', '#bcbd22', '#17becf', |
|
|
'#aec7e8', '#ffbb78', '#98df8a', '#ff9896', '#c5b0d5', |
|
|
'#c49c94', '#f7b6d2', '#c7c7c7', '#dbdb8d', '#9edae5' |
|
|
]; |
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|
|
|
|
// Get controls |
|
|
const peakThresholdSlider = document.getElementById('peak-threshold'); |
|
|
const peakThresholdValue = document.getElementById('peak-threshold-value'); |
|
|
const confidenceHighSlider = document.getElementById('confidence-high'); |
|
|
const confidenceHighValue = document.getElementById('confidence-high-value'); |
|
|
const confidenceMediumSlider = document.getElementById('confidence-medium'); |
|
|
const confidenceMediumValue = document.getElementById('confidence-medium-value'); |
|
|
const alignmentCanvas = document.getElementById('alignment-canvas'); |
|
|
const timelineCanvas = document.getElementById('timeline-canvas'); |
|
|
const alignmentCtx = alignmentCanvas.getContext('2d'); |
|
|
const timelineCtx = timelineCanvas.getContext('2d'); |
|
|
|
|
|
// Update displays when sliders change |
|
|
peakThresholdSlider.oninput = function() {{ |
|
|
peakThresholdValue.textContent = this.value + '%'; |
|
|
updateVisualization(); |
|
|
}}; |
|
|
|
|
|
confidenceHighSlider.oninput = function() {{ |
|
|
confidenceHighValue.textContent = (this.value / 100).toFixed(2); |
|
|
updateVisualization(); |
|
|
}}; |
|
|
|
|
|
confidenceMediumSlider.oninput = function() {{ |
|
|
confidenceMediumValue.textContent = (this.value / 100).toFixed(2); |
|
|
updateVisualization(); |
|
|
}}; |
|
|
|
|
|
function resetDefaults() {{ |
|
|
peakThresholdSlider.value = 90; |
|
|
confidenceHighSlider.value = 50; |
|
|
confidenceMediumSlider.value = 20; |
|
|
peakThresholdValue.textContent = '90%'; |
|
|
confidenceHighValue.textContent = '0.50'; |
|
|
confidenceMediumValue.textContent = '0.20'; |
|
|
updateVisualization(); |
|
|
}} |
|
|
|
|
|
function calculateAlignment(weights, peakThreshold) {{ |
|
|
// Find peak |
|
|
let peakIdx = 0; |
|
|
let peakWeight = weights[0]; |
|
|
for (let i = 1; i < weights.length; i++) {{ |
|
|
if (weights[i] > peakWeight) {{ |
|
|
peakWeight = weights[i]; |
|
|
peakIdx = i; |
|
|
}} |
|
|
}} |
|
|
|
|
|
// Find significant frames |
|
|
const threshold = peakWeight * (peakThreshold / 100); |
|
|
let startIdx = peakIdx; |
|
|
let endIdx = peakIdx; |
|
|
let sumWeight = 0; |
|
|
let count = 0; |
|
|
|
|
|
for (let i = 0; i < weights.length; i++) {{ |
|
|
if (weights[i] >= threshold) {{ |
|
|
if (i < startIdx) startIdx = i; |
|
|
if (i > endIdx) endIdx = i; |
|
|
sumWeight += weights[i]; |
|
|
count++; |
|
|
}} |
|
|
}} |
|
|
|
|
|
const avgWeight = count > 0 ? sumWeight / count : peakWeight; |
|
|
|
|
|
return {{ |
|
|
startIdx: startIdx, |
|
|
endIdx: endIdx, |
|
|
peakIdx: peakIdx, |
|
|
peakWeight: peakWeight, |
|
|
avgWeight: avgWeight, |
|
|
threshold: threshold |
|
|
}}; |
|
|
}} |
|
|
|
|
|
function getConfidenceLevel(avgWeight, highThreshold, mediumThreshold) {{ |
|
|
if (avgWeight > highThreshold) return 'high'; |
|
|
if (avgWeight > mediumThreshold) return 'medium'; |
|
|
return 'low'; |
|
|
}} |
|
|
|
|
|
function drawAlignmentChart() {{ |
|
|
const peakThreshold = parseInt(peakThresholdSlider.value); |
|
|
const highThreshold = parseInt(confidenceHighSlider.value) / 100; |
|
|
const mediumThreshold = parseInt(confidenceMediumSlider.value) / 100; |
|
|
|
|
|
// Canvas dimensions |
|
|
const width = alignmentCanvas.width; |
|
|
const height = alignmentCanvas.height; |
|
|
const leftMargin = 180; |
|
|
const rightMargin = 50; |
|
|
const topMargin = 60; |
|
|
const bottomMargin = 80; |
|
|
|
|
|
const plotWidth = width - leftMargin - rightMargin; |
|
|
const plotHeight = height - topMargin - bottomMargin; |
|
|
|
|
|
const rowHeight = plotHeight / numGlosses; |
|
|
const featureWidth = plotWidth / numFeatures; |
|
|
|
|
|
// Clear canvas |
|
|
alignmentCtx.clearRect(0, 0, width, height); |
|
|
|
|
|
// Draw title |
|
|
alignmentCtx.fillStyle = '#333'; |
|
|
alignmentCtx.font = 'bold 18px Arial'; |
|
|
alignmentCtx.textAlign = 'center'; |
|
|
alignmentCtx.fillText('Word-to-Frame Alignment', width / 2, 30); |
|
|
alignmentCtx.font = '13px Arial'; |
|
|
alignmentCtx.fillText('(based on attention peaks, ★ = peak frame)', width / 2, 48); |
|
|
|
|
|
// Calculate alignments |
|
|
const alignments = []; |
|
|
for (let wordIdx = 0; wordIdx < numGlosses; wordIdx++) {{ |
|
|
const data = attentionData[wordIdx]; |
|
|
const alignment = calculateAlignment(data.weights, peakThreshold); |
|
|
alignment.word = data.word; |
|
|
alignment.wordIdx = wordIdx; |
|
|
alignment.weights = data.weights; |
|
|
alignments.push(alignment); |
|
|
}} |
|
|
|
|
|
// Draw grid |
|
|
alignmentCtx.strokeStyle = '#e0e0e0'; |
|
|
alignmentCtx.lineWidth = 0.5; |
|
|
for (let i = 0; i <= numFeatures; i++) {{ |
|
|
const x = leftMargin + i * featureWidth; |
|
|
alignmentCtx.beginPath(); |
|
|
alignmentCtx.moveTo(x, topMargin); |
|
|
alignmentCtx.lineTo(x, topMargin + plotHeight); |
|
|
alignmentCtx.stroke(); |
|
|
}} |
|
|
|
|
|
// Draw word regions |
|
|
for (let wordIdx = 0; wordIdx < numGlosses; wordIdx++) {{ |
|
|
const alignment = alignments[wordIdx]; |
|
|
const confidence = getConfidenceLevel(alignment.avgWeight, highThreshold, mediumThreshold); |
|
|
const y = topMargin + wordIdx * rowHeight; |
|
|
|
|
|
// Alpha based on confidence |
|
|
const alpha = confidence === 'high' ? 0.9 : confidence === 'medium' ? 0.7 : 0.5; |
|
|
|
|
|
// Draw rectangle for word region |
|
|
const startX = leftMargin + alignment.startIdx * featureWidth; |
|
|
const rectWidth = (alignment.endIdx - alignment.startIdx + 1) * featureWidth; |
|
|
|
|
|
alignmentCtx.fillStyle = colors[wordIdx % 20]; |
|
|
alignmentCtx.globalAlpha = alpha; |
|
|
alignmentCtx.fillRect(startX, y, rectWidth, rowHeight * 0.8); |
|
|
alignmentCtx.globalAlpha = 1.0; |
|
|
|
|
|
// Draw border |
|
|
alignmentCtx.strokeStyle = '#000'; |
|
|
alignmentCtx.lineWidth = 2; |
|
|
alignmentCtx.strokeRect(startX, y, rectWidth, rowHeight * 0.8); |
|
|
|
|
|
// Draw attention waveform inside rectangle |
|
|
alignmentCtx.strokeStyle = 'rgba(0, 0, 255, 0.8)'; |
|
|
alignmentCtx.lineWidth = 1.5; |
|
|
alignmentCtx.beginPath(); |
|
|
for (let i = alignment.startIdx; i <= alignment.endIdx; i++) {{ |
|
|
const x = leftMargin + i * featureWidth + featureWidth / 2; |
|
|
const weight = alignment.weights[i]; |
|
|
const maxWeight = alignment.peakWeight; |
|
|
const normalizedWeight = weight / (maxWeight * 1.2); // Scale for visibility |
|
|
const waveY = y + rowHeight * 0.8 - (normalizedWeight * rowHeight * 0.6); |
|
|
|
|
|
if (i === alignment.startIdx) {{ |
|
|
alignmentCtx.moveTo(x, waveY); |
|
|
}} else {{ |
|
|
alignmentCtx.lineTo(x, waveY); |
|
|
}} |
|
|
}} |
|
|
alignmentCtx.stroke(); |
|
|
|
|
|
// Draw word label |
|
|
const labelX = startX + rectWidth / 2; |
|
|
const labelY = y + rowHeight * 0.4; |
|
|
|
|
|
alignmentCtx.fillStyle = 'rgba(0, 0, 0, 0.7)'; |
|
|
alignmentCtx.fillRect(labelX - 60, labelY - 12, 120, 24); |
|
|
alignmentCtx.fillStyle = '#fff'; |
|
|
alignmentCtx.font = 'bold 13px Arial'; |
|
|
alignmentCtx.textAlign = 'center'; |
|
|
alignmentCtx.textBaseline = 'middle'; |
|
|
alignmentCtx.fillText(alignment.word, labelX, labelY); |
|
|
|
|
|
// Mark peak frame with star |
|
|
const peakX = leftMargin + alignment.peakIdx * featureWidth + featureWidth / 2; |
|
|
const peakY = y + rowHeight * 0.4; |
|
|
|
|
|
// Draw star |
|
|
alignmentCtx.fillStyle = '#ff0000'; |
|
|
alignmentCtx.strokeStyle = '#ffff00'; |
|
|
alignmentCtx.lineWidth = 1.5; |
|
|
alignmentCtx.font = '20px Arial'; |
|
|
alignmentCtx.textAlign = 'center'; |
|
|
alignmentCtx.strokeText('★', peakX, peakY); |
|
|
alignmentCtx.fillText('★', peakX, peakY); |
|
|
|
|
|
// Y-axis label (word names) |
|
|
alignmentCtx.fillStyle = '#333'; |
|
|
alignmentCtx.font = '12px Arial'; |
|
|
alignmentCtx.textAlign = 'right'; |
|
|
alignmentCtx.textBaseline = 'middle'; |
|
|
alignmentCtx.fillText(alignment.word, leftMargin - 10, y + rowHeight * 0.4); |
|
|
}} |
|
|
|
|
|
// Draw horizontal grid lines |
|
|
alignmentCtx.strokeStyle = '#ccc'; |
|
|
alignmentCtx.lineWidth = 0.5; |
|
|
for (let i = 0; i <= numGlosses; i++) {{ |
|
|
const y = topMargin + i * rowHeight; |
|
|
alignmentCtx.beginPath(); |
|
|
alignmentCtx.moveTo(leftMargin, y); |
|
|
alignmentCtx.lineTo(leftMargin + plotWidth, y); |
|
|
alignmentCtx.stroke(); |
|
|
}} |
|
|
|
|
|
// Draw axes |
|
|
alignmentCtx.strokeStyle = '#000'; |
|
|
alignmentCtx.lineWidth = 2; |
|
|
alignmentCtx.strokeRect(leftMargin, topMargin, plotWidth, plotHeight); |
|
|
|
|
|
// X-axis labels (frame indices) |
|
|
alignmentCtx.fillStyle = '#000'; |
|
|
alignmentCtx.font = '11px Arial'; |
|
|
alignmentCtx.textAlign = 'center'; |
|
|
alignmentCtx.textBaseline = 'top'; |
|
|
for (let i = 0; i < numFeatures; i++) {{ |
|
|
const x = leftMargin + i * featureWidth + featureWidth / 2; |
|
|
alignmentCtx.fillText(i.toString(), x, topMargin + plotHeight + 10); |
|
|
}} |
|
|
|
|
|
// Axis titles |
|
|
alignmentCtx.fillStyle = '#333'; |
|
|
alignmentCtx.font = 'bold 14px Arial'; |
|
|
alignmentCtx.textAlign = 'center'; |
|
|
alignmentCtx.fillText('Feature Frame Index', leftMargin + plotWidth / 2, height - 20); |
|
|
|
|
|
alignmentCtx.save(); |
|
|
alignmentCtx.translate(30, topMargin + plotHeight / 2); |
|
|
alignmentCtx.rotate(-Math.PI / 2); |
|
|
alignmentCtx.fillText('Generated Word', 0, 0); |
|
|
alignmentCtx.restore(); |
|
|
|
|
|
return alignments; |
|
|
}} |
|
|
|
|
|
function drawTimeline(alignments) {{ |
|
|
const highThreshold = parseInt(confidenceHighSlider.value) / 100; |
|
|
const mediumThreshold = parseInt(confidenceMediumSlider.value) / 100; |
|
|
|
|
|
const width = timelineCanvas.width; |
|
|
const height = timelineCanvas.height; |
|
|
const leftMargin = 180; |
|
|
const rightMargin = 50; |
|
|
const plotWidth = width - leftMargin - rightMargin; |
|
|
const featureWidth = plotWidth / numFeatures; |
|
|
|
|
|
// Clear canvas |
|
|
timelineCtx.clearRect(0, 0, width, height); |
|
|
|
|
|
// Background bar |
|
|
timelineCtx.fillStyle = '#ddd'; |
|
|
timelineCtx.fillRect(leftMargin, 30, plotWidth, 40); |
|
|
timelineCtx.strokeStyle = '#000'; |
|
|
timelineCtx.lineWidth = 2; |
|
|
timelineCtx.strokeRect(leftMargin, 30, plotWidth, 40); |
|
|
|
|
|
// Draw word regions on timeline |
|
|
for (let wordIdx = 0; wordIdx < alignments.length; wordIdx++) {{ |
|
|
const alignment = alignments[wordIdx]; |
|
|
const confidence = getConfidenceLevel(alignment.avgWeight, highThreshold, mediumThreshold); |
|
|
const alpha = confidence === 'high' ? 0.9 : confidence === 'medium' ? 0.7 : 0.5; |
|
|
|
|
|
const startX = leftMargin + alignment.startIdx * featureWidth; |
|
|
const rectWidth = (alignment.endIdx - alignment.startIdx + 1) * featureWidth; |
|
|
|
|
|
timelineCtx.fillStyle = colors[wordIdx % 20]; |
|
|
timelineCtx.globalAlpha = alpha; |
|
|
timelineCtx.fillRect(startX, 30, rectWidth, 40); |
|
|
timelineCtx.globalAlpha = 1.0; |
|
|
timelineCtx.strokeStyle = '#000'; |
|
|
timelineCtx.lineWidth = 0.5; |
|
|
timelineCtx.strokeRect(startX, 30, rectWidth, 40); |
|
|
}} |
|
|
|
|
|
// Title |
|
|
timelineCtx.fillStyle = '#333'; |
|
|
timelineCtx.font = 'bold 13px Arial'; |
|
|
timelineCtx.textAlign = 'left'; |
|
|
timelineCtx.fillText('Timeline Progress Bar', leftMargin, 20); |
|
|
}} |
|
|
|
|
|
function updateDetailsPanel(alignments, highThreshold, mediumThreshold) {{ |
|
|
const panel = document.getElementById('alignment-details'); |
|
|
let html = '<table style="width: 100%; border-collapse: collapse;">'; |
|
|
html += '<tr style="background: #f0f0f0; font-weight: bold;">'; |
|
|
html += '<th style="padding: 8px; border: 1px solid #ddd;">Word</th>'; |
|
|
html += '<th style="padding: 8px; border: 1px solid #ddd;">Feature Range</th>'; |
|
|
html += '<th style="padding: 8px; border: 1px solid #ddd;">Peak</th>'; |
|
|
html += '<th style="padding: 8px; border: 1px solid #ddd;">Span</th>'; |
|
|
html += '<th style="padding: 8px; border: 1px solid #ddd;">Avg Attention</th>'; |
|
|
html += '<th style="padding: 8px; border: 1px solid #ddd;">Confidence</th>'; |
|
|
html += '</tr>'; |
|
|
|
|
|
for (const align of alignments) {{ |
|
|
const confidence = getConfidenceLevel(align.avgWeight, highThreshold, mediumThreshold); |
|
|
const span = align.endIdx - align.startIdx + 1; |
|
|
|
|
|
html += '<tr>'; |
|
|
html += `<td style="padding: 8px; border: 1px solid #ddd;"><strong>${{align.word}}</strong></td>`; |
|
|
html += `<td style="padding: 8px; border: 1px solid #ddd;">${{align.startIdx}} → ${{align.endIdx}}</td>`; |
|
|
html += `<td style="padding: 8px; border: 1px solid #ddd;">${{align.peakIdx}}</td>`; |
|
|
html += `<td style="padding: 8px; border: 1px solid #ddd;">${{span}}</td>`; |
|
|
html += `<td style="padding: 8px; border: 1px solid #ddd;">${{align.avgWeight.toFixed(4)}}</td>`; |
|
|
html += `<td style="padding: 8px; border: 1px solid #ddd;"><span class="confidence ${{confidence}}">${{confidence}}</span></td>`; |
|
|
html += '</tr>'; |
|
|
}} |
|
|
|
|
|
html += '</table>'; |
|
|
panel.innerHTML = html; |
|
|
}} |
|
|
|
|
|
function updateVisualization() {{ |
|
|
const alignments = drawAlignmentChart(); |
|
|
drawTimeline(alignments); |
|
|
const highThreshold = parseInt(confidenceHighSlider.value) / 100; |
|
|
const mediumThreshold = parseInt(confidenceMediumSlider.value) / 100; |
|
|
updateDetailsPanel(alignments, highThreshold, mediumThreshold); |
|
|
}} |
|
|
|
|
|
// Event listeners for sliders |
|
|
peakSlider.addEventListener('input', function() {{ |
|
|
peakValue.textContent = peakSlider.value + '%'; |
|
|
updateVisualization(); |
|
|
}}); |
|
|
|
|
|
confidenceHighSlider.addEventListener('input', function() {{ |
|
|
const val = parseInt(confidenceHighSlider.value) / 100; |
|
|
confidenceHighValue.textContent = val.toFixed(2); |
|
|
updateVisualization(); |
|
|
}}); |
|
|
|
|
|
confidenceMediumSlider.addEventListener('input', function() {{ |
|
|
const val = parseInt(confidenceMediumSlider.value) / 100; |
|
|
confidenceMediumValue.textContent = val.toFixed(2); |
|
|
updateVisualization(); |
|
|
}}); |
|
|
|
|
|
// Initial visualization |
|
|
updateVisualization(); |
|
|
</script> |
|
|
</body> |
|
|
</html> |
|
|
""" |
|
|
|
|
|
|
|
|
with open(output_path, 'w', encoding='utf-8') as f: |
|
|
f.write(html_content) |
|
|
|
|
|
print(f"✓ Interactive HTML generated: {output_path}") |
|
|
print(" Open this file in a browser and use the sliders to adjust thresholds.") |
|
|
|
|
|
if __name__ == "__main__": |
|
|
if len(sys.argv) != 2: |
|
|
print("Usage: python generate_interactive_alignment.py <sample_dir>") |
|
|
print("Example: python generate_interactive_alignment.py detailed_prediction_20251226_022246/sample_000") |
|
|
sys.exit(1) |
|
|
|
|
|
sample_dir = Path(sys.argv[1]) |
|
|
|
|
|
if not sample_dir.exists(): |
|
|
print(f"Error: directory not found: {sample_dir}") |
|
|
sys.exit(1) |
|
|
|
|
|
output_path = sample_dir / "interactive_alignment.html" |
|
|
generate_interactive_html(sample_dir, output_path) |
|
|
|
|
|
print("\nUsage:") |
|
|
print(f" Open in a browser: {output_path.absolute()}") |
|
|
print(" Move the sliders to preview different threshold settings in real time.") |
|
|
|
