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<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>Breast Cancer Classification - AI Diagnostic Tool</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%);
min-height: 100vh;
padding: 20px;
}
.container {
max-width: 900px;
margin: 0 auto;
background: white;
border-radius: 20px;
box-shadow: 0 20px 60px rgba(0,0,0,0.3);
overflow: hidden;
}
.header {
background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
color: white;
padding: 30px;
text-align: center;
}
.header h1 {
font-size: 2.5em;
margin-bottom: 10px;
}
.header p {
font-size: 1.1em;
opacity: 0.9;
}
.content {
padding: 40px;
}
.info-box {
background: #f8f9fa;
border-left: 4px solid #667eea;
padding: 20px;
margin-bottom: 30px;
border-radius: 8px;
}
.info-box h3 {
color: #667eea;
margin-bottom: 10px;
}
.info-box ul {
margin-left: 20px;
line-height: 1.8;
}
.github-link {
display: inline-flex;
align-items: center;
gap: 8px;
color: #667eea;
text-decoration: none;
font-weight: 600;
font-size: 1.1em;
padding: 10px 20px;
background: white;
border: 2px solid #667eea;
border-radius: 8px;
transition: all 0.3s;
margin: 20px 0;
}
.github-link:hover {
transform: translateY(-2px);
box-shadow: 0 4px 12px rgba(102, 126, 234, 0.3);
background: #667eea;
color: white;
}
.test-button {
background: #6c757d;
color: white;
padding: 12px 30px;
border: none;
border-radius: 25px;
font-size: 1.1em;
cursor: pointer;
transition: transform 0.2s;
margin-left: 10px;
}
.test-button:hover {
background: #5a6268;
transform: scale(1.05);
}
.result-image-container {
position: relative;
margin-bottom: 20px;
}
.result-image {
max-width: 100%;
max-height: 300px;
border-radius: 10px;
box-shadow: 0 5px 15px rgba(0,0,0,0.2);
}
.result-overlay {
position: absolute;
top: 10px;
left: 50%;
transform: translateX(-50%);
background: rgba(0, 0, 0, 0.8);
color: white;
padding: 10px 20px;
border-radius: 8px;
font-size: 1.2em;
font-weight: bold;
}
.upload-section {
text-align: center;
padding: 40px;
border: 3px dashed #667eea;
border-radius: 15px;
margin-bottom: 30px;
transition: all 0.3s;
cursor: pointer;
}
.upload-section:hover {
border-color: #764ba2;
background: #f8f9fa;
}
.upload-section.drag-over {
background: #e3f2fd;
border-color: #2196F3;
}
.upload-icon {
font-size: 4em;
color: #667eea;
margin-bottom: 20px;
}
.upload-text {
font-size: 1.2em;
color: #666;
margin-bottom: 15px;
}
.file-input {
display: none;
}
.upload-button {
background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
color: white;
padding: 12px 30px;
border: none;
border-radius: 25px;
font-size: 1.1em;
cursor: pointer;
transition: transform 0.2s;
}
.upload-button:hover {
transform: scale(1.05);
}
.preview-section {
display: none;
margin-bottom: 30px;
}
.preview-image {
max-width: 100%;
max-height: 400px;
border-radius: 10px;
box-shadow: 0 5px 15px rgba(0,0,0,0.2);
display: block;
margin: 0 auto;
}
.result-section {
display: none;
padding: 30px;
border-radius: 15px;
text-align: center;
}
.result-benign {
background: linear-gradient(135deg, #11998e 0%, #38ef7d 100%);
color: white;
}
.result-malignant {
background: linear-gradient(135deg, #ee0979 0%, #ff6a00 100%);
color: white;
}
.result-title {
font-size: 2em;
margin-bottom: 20px;
}
.confidence-bar {
background: rgba(255,255,255,0.3);
border-radius: 10px;
height: 30px;
margin: 20px 0;
position: relative;
overflow: hidden;
}
.confidence-fill {
height: 100%;
background: white;
border-radius: 10px;
transition: width 1s ease;
display: flex;
align-items: center;
justify-content: center;
color: #667eea;
font-weight: bold;
}
.probabilities {
display: flex;
justify-content: space-around;
margin-top: 20px;
}
.prob-item {
flex: 1;
padding: 15px;
background: rgba(255,255,255,0.2);
border-radius: 10px;
margin: 0 10px;
}
.prob-label {
font-size: 0.9em;
margin-bottom: 5px;
}
.prob-value {
font-size: 1.8em;
font-weight: bold;
}
.loading {
display: none;
text-align: center;
padding: 30px;
}
.spinner {
border: 4px solid #f3f3f3;
border-top: 4px solid #667eea;
border-radius: 50%;
width: 50px;
height: 50px;
animation: spin 1s linear infinite;
margin: 0 auto 20px;
}
@keyframes spin {
0% { transform: rotate(0deg); }
100% { transform: rotate(360deg); }
}
.error {
display: none;
background: #ff5252;
color: white;
padding: 15px;
border-radius: 10px;
margin-bottom: 20px;
}
.try-again-button {
background: white;
color: #667eea;
padding: 12px 30px;
border: none;
border-radius: 25px;
font-size: 1.1em;
cursor: pointer;
margin-top: 20px;
transition: transform 0.2s;
}
.try-again-button:hover {
transform: scale(1.05);
}
.note {
background: #fff3cd;
border-left: 4px solid #ffc107;
padding: 15px;
margin-top: 30px;
border-radius: 8px;
font-size: 0.9em;
}
.footer {
background: #f8f9fa;
padding: 20px;
text-align: center;
color: #666;
font-size: 0.9em;
}
</style>
</head>
<body>
<div class="container">
<div class="header">
<h1>🔬 Breast Cancer Classification</h1>
<p>AI-Powered Mammogram Analysis</p>
</div>
<div class="content">
<div class="info-box">
<h3>📋 How to Use This Tool</h3>
<ul>
<li><strong>Upload Image:</strong> Click the upload area or drag & drop a mammogram image</li>
<li><strong>Supported Formats:</strong> JPG, JPEG, PNG</li>
<li><strong>Image Requirements:</strong> Clear mammogram image, preferably full breast view</li>
<li><strong>Classification:</strong> The AI will classify the image as Benign (non-cancerous) or Malignant (cancerous)</li>
<li><strong>Confidence Score:</strong> Shows the model's confidence in its prediction</li>
</ul>
</div>
<div class="info-box">
<h3>🤖 About the Model</h3>
<p>This tool uses an integrated ensemble of VGG16 and ResNet50V2 deep learning models trained on the CBIS-DDSM dataset. The model combines transfer learning with custom classification layers to analyze mammogram images and predict breast cancer classification.</p>
<div style="margin-top: 15px;">
<a href="https://github.com/koesan/Breast_Cancer_Classification" target="_blank" class="github-link">
<svg width="24" height="24" viewBox="0 0 24 24" fill="currentColor">
<path d="M12 0c-6.626 0-12 5.373-12 12 0 5.302 3.438 9.8 8.207 11.387.599.111.793-.261.793-.577v-2.234c-3.338.726-4.033-1.416-4.033-1.416-.546-1.387-1.333-1.756-1.333-1.756-1.089-.745.083-.729.083-.729 1.205.084 1.839 1.237 1.839 1.237 1.07 1.834 2.807 1.304 3.492.997.107-.775.418-1.305.762-1.604-2.665-.305-5.467-1.334-5.467-5.931 0-1.311.469-2.381 1.236-3.221-.124-.303-.535-1.524.117-3.176 0 0 1.008-.322 3.301 1.23.957-.266 1.983-.399 3.003-.404 1.02.005 2.047.138 3.006.404 2.291-1.552 3.297-1.23 3.297-1.23.653 1.653.242 2.874.118 3.176.77.84 1.235 1.911 1.235 3.221 0 4.609-2.807 5.624-5.479 5.921.43.372.823 1.102.823 2.222v3.293c0 .319.192.694.801.576 4.765-1.589 8.199-6.086 8.199-11.386 0-6.627-5.373-12-12-12z"/>
</svg>
View on GitHub
</a>
</div>
</div>
<div class="error" id="errorMessage"></div>
<div class="upload-section" id="uploadSection">
<div class="upload-icon">📤</div>
<div class="upload-text">Drag & Drop your mammogram image here</div>
<div style="margin: 20px 0;">or</div>
<input type="file" id="fileInput" class="file-input" accept="image/*">
<button class="upload-button" onclick="document.getElementById('fileInput').click()">
Choose File
</button>
<button class="test-button" onclick="testExample()">
🧪 Test Example
</button>
</div>
<div class="preview-section" id="previewSection">
<h3 style="margin-bottom: 15px;">Uploaded Image:</h3>
<img id="previewImage" class="preview-image" alt="Preview">
<div style="text-align: center; margin-top: 20px;">
<button class="upload-button" onclick="analyzeImage()">
🔍 Analyze Image
</button>
</div>
</div>
<div class="loading" id="loading">
<div class="spinner"></div>
<p>Analyzing mammogram image...</p>
</div>
<div class="result-section" id="resultSection">
<div class="result-image-container" id="resultImageContainer" style="display: none;">
<img id="resultImage" class="result-image" alt="Result">
<div class="result-overlay" id="resultOverlay"></div>
</div>
<div class="result-title" id="resultTitle"></div>
<div class="confidence-bar">
<div class="confidence-fill" id="confidenceFill"></div>
</div>
<div class="probabilities">
<div class="prob-item">
<div class="prob-label">Benign Probability</div>
<div class="prob-value" id="benignProb">-</div>
</div>
<div class="prob-item">
<div class="prob-label">Malignant Probability</div>
<div class="prob-value" id="malignantProb">-</div>
</div>
</div>
<button class="try-again-button" onclick="resetAnalysis()">
🔄 Analyze Another Image
</button>
</div>
<div class="note">
<strong>⚠️ Important Notice:</strong> This is an AI diagnostic assistance tool for educational and research purposes. It should NOT be used as a substitute for professional medical diagnosis. Always consult with qualified healthcare professionals for medical advice and diagnosis.
</div>
</div>
<div class="footer">
<p>Powered by VGG16 + ResNet50V2 Ensemble Model | CBIS-DDSM Dataset</p>
<p>© 2025 Breast Cancer Classification Project</p>
</div>
</div>
<script>
let selectedFile = null;
// File input change handler
document.getElementById('fileInput').addEventListener('change', function(e) {
handleFile(e.target.files[0]);
});
// Drag and drop handlers
const uploadSection = document.getElementById('uploadSection');
uploadSection.addEventListener('dragover', function(e) {
e.preventDefault();
uploadSection.classList.add('drag-over');
});
uploadSection.addEventListener('dragleave', function(e) {
e.preventDefault();
uploadSection.classList.remove('drag-over');
});
uploadSection.addEventListener('drop', function(e) {
e.preventDefault();
uploadSection.classList.remove('drag-over');
handleFile(e.dataTransfer.files[0]);
});
function handleFile(file) {
if (!file) return;
// Check if file is an image
if (!file.type.startsWith('image/')) {
showError('Please upload a valid image file (JPG, JPEG, PNG)');
return;
}
selectedFile = file;
// Show preview
const reader = new FileReader();
reader.onload = function(e) {
document.getElementById('previewImage').src = e.target.result;
document.getElementById('uploadSection').style.display = 'none';
document.getElementById('previewSection').style.display = 'block';
document.getElementById('errorMessage').style.display = 'none';
};
reader.readAsDataURL(file);
}
async function analyzeImage() {
if (!selectedFile) return;
// Show loading
document.getElementById('previewSection').style.display = 'none';
document.getElementById('loading').style.display = 'block';
// Create FormData
const formData = new FormData();
formData.append('file', selectedFile);
try {
const response = await fetch('/predict', {
method: 'POST',
body: formData
});
const data = await response.json();
if (response.ok) {
showResult(data);
} else {
showError(data.error || 'An error occurred during analysis');
}
} catch (error) {
showError('Failed to connect to the server. Please try again.');
} finally {
document.getElementById('loading').style.display = 'none';
}
}
function testExample() {
// Show loading
document.getElementById('loading').style.display = 'block';
document.getElementById('uploadSection').style.display = 'none';
document.getElementById('previewSection').style.display = 'none';
document.getElementById('resultSection').style.display = 'none';
document.getElementById('errorMessage').style.display = 'none';
fetch('/test-example', {
method: 'POST'
})
.then(response => response.json())
.then(data => {
document.getElementById('loading').style.display = 'none';
if (data.error) {
showError(data.error);
} else {
showResult(data, true);
}
})
.catch(error => {
document.getElementById('loading').style.display = 'none';
showError('Failed to analyze example: ' + error.message);
});
}
function showResult(data, showImage = false) {
const resultSection = document.getElementById('resultSection');
const resultTitle = document.getElementById('resultTitle');
const confidenceFill = document.getElementById('confidenceFill');
const benignProb = document.getElementById('benignProb');
const malignantProb = document.getElementById('malignantProb');
const resultImageContainer = document.getElementById('resultImageContainer');
const resultImage = document.getElementById('resultImage');
const resultOverlay = document.getElementById('resultOverlay');
// Show image if available
if (showImage && data.image) {
resultImage.src = data.image;
resultOverlay.textContent = data.class;
resultImageContainer.style.display = 'block';
} else {
resultImageContainer.style.display = 'none';
}
// Update content
resultTitle.textContent = `Classification: ${data.class}`;
confidenceFill.style.width = data.confidence.toFixed(2) + '%';
confidenceFill.textContent = data.confidence.toFixed(2) + '%';
benignProb.textContent = data.benign_prob.toFixed(2) + '%';
malignantProb.textContent = data.malignant_prob.toFixed(2) + '%';
// Update styling based on classification
if (data.class === 'Benign') {
resultSection.className = 'result-section result-benign';
} else {
resultSection.className = 'result-section result-malignant';
}
resultSection.style.display = 'block';
}
function showError(message) {
const errorElement = document.getElementById('errorMessage');
errorElement.textContent = '❌ Error: ' + message;
errorElement.style.display = 'block';
document.getElementById('uploadSection').style.display = 'block';
document.getElementById('loading').style.display = 'none';
}
function resetAnalysis() {
selectedFile = null;
document.getElementById('fileInput').value = '';
document.getElementById('uploadSection').style.display = 'block';
document.getElementById('previewSection').style.display = 'none';
document.getElementById('resultSection').style.display = 'none';
document.getElementById('errorMessage').style.display = 'none';
}
</script>
</body>
</html>