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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Mon Jun 9 11:22:33 2025
@author: amit
"""
<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>White Blood Cell Classifier</title>
<style>
* {
margin: 0;
padding: 0;
box-sizing: border-box;
}
body {
font-family: 'Arial', sans-serif;
background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
min-height: 100vh;
padding: 20px;
}
.container {
max-width: 1200px;
margin: 0 auto;
background: white;
border-radius: 15px;
box-shadow: 0 15px 35px rgba(0, 0, 0, 0.1);
overflow: hidden;
}
.header {
background: linear-gradient(135deg, #2c3e50, #3498db);
color: white;
padding: 30px;
text-align: center;
}
.header h1 {
font-size: 2.5rem;
margin-bottom: 10px;
}
.header p {
font-size: 1.1rem;
opacity: 0.9;
}
.main-content {
padding: 40px;
}
.upload-section {
background: #f8f9fa;
border-radius: 10px;
padding: 30px;
margin-bottom: 30px;
border: 2px dashed #dee2e6;
transition: all 0.3s ease;
}
.upload-section:hover {
border-color: #007bff;
background: #f0f8ff;
}
.upload-area {
text-align: center;
padding: 20px;
}
.upload-icon {
font-size: 3rem;
color: #6c757d;
margin-bottom: 20px;
}
.file-input {
display: none;
}
.upload-btn {
background: linear-gradient(135deg, #28a745, #20c997);
color: white;
padding: 12px 30px;
border: none;
border-radius: 25px;
font-size: 1.1rem;
cursor: pointer;
transition: all 0.3s ease;
}
.upload-btn:hover {
transform: translateY(-2px);
box-shadow: 0 5px 15px rgba(40, 167, 69, 0.3);
}
.predict-btn {
background: linear-gradient(135deg, #007bff, #0056b3);
color: white;
padding: 15px 40px;
border: none;
border-radius: 25px;
font-size: 1.2rem;
cursor: pointer;
transition: all 0.3s ease;
margin-top: 20px;
}
.predict-btn:hover {
transform: translateY(-2px);
box-shadow: 0 5px 15px rgba(0, 123, 255, 0.3);
}
.predict-btn:disabled {
background: #6c757d;
cursor: not-allowed;
transform: none;
}
.preview-section {
display: none;
margin-top: 30px;
}
.image-preview {
display: flex;
flex-wrap: wrap;
gap: 20px;
margin-bottom: 20px;
}
.preview-item {
position: relative;
background: white;
border-radius: 10px;
box-shadow: 0 5px 15px rgba(0, 0, 0, 0.1);
overflow: hidden;
transition: transform 0.3s ease;
}
.preview-item:hover {
transform: translateY(-5px);
}
.preview-item img {
width: 200px;
height: 200px;
object-fit: cover;
}
.preview-info {
padding: 15px;
text-align: center;
}
.filename {
font-weight: bold;
color: #2c3e50;
margin-bottom: 5px;
}
.filesize {
color: #6c757d;
font-size: 0.9rem;
}
.results-section {
display: none;
margin-top: 30px;
}
.result-item {
background: white;
border-radius: 10px;
box-shadow: 0 5px 15px rgba(0, 0, 0, 0.1);
margin-bottom: 20px;
overflow: hidden;
animation: slideIn 0.5s ease;
}
@keyframes slideIn {
from {
opacity: 0;
transform: translateY(20px);
}
to {
opacity: 1;
transform: translateY(0);
}
}
.result-header {
background: linear-gradient(135deg, #17a2b8, #138496);
color: white;
padding: 15px 20px;
display: flex;
justify-content: space-between;
align-items: center;
}
.result-body {
padding: 20px;
}
.prediction-main {
display: flex;
align-items: center;
gap: 20px;
margin-bottom: 20px;
}
.prediction-main img {
width: 100px;
height: 100px;
object-fit: cover;
border-radius: 10px;
}
.prediction-details {
flex: 1;
}
.predicted-class {
font-size: 1.5rem;
font-weight: bold;
color: #2c3e50;
margin-bottom: 10px;
}
.confidence {
font-size: 1.2rem;
color: #28a745;
font-weight: bold;
}
.probabilities {
margin-top: 20px;
}
.probability-item {
display: flex;
align-items: center;
margin-bottom: 10px;
}
.probability-label {
width: 120px;
font-weight: bold;
color: #495057;
}
.probability-bar {
flex: 1;
height: 20px;
background: #e9ecef;
border-radius: 10px;
overflow: hidden;
margin: 0 10px;
}
.probability-fill {
height: 100%;
background: linear-gradient(135deg, #007bff, #0056b3);
transition: width 0.5s ease;
}
.probability-value {
width: 60px;
text-align: right;
font-weight: bold;
color: #495057;
}
.loading {
display: none;
text-align: center;
margin: 20px 0;
}
.spinner {
border: 4px solid #f3f3f3;
border-top: 4px solid #007bff;
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 {
background: #f8d7da;
color: #721c24;
padding: 15px;
border-radius: 5px;
margin: 10px 0;
border: 1px solid #f5c6cb;
}
.wbc-info {
background: #e3f2fd;
border-radius: 10px;
padding: 20px;
margin-top: 30px;
}
.wbc-info h3 {
color: #1976d2;
margin-bottom: 15px;
}
.wbc-types {
display: grid;
grid-template-columns: repeat(auto-fit, minmax(200px, 1fr));
gap: 15px;
}
.wbc-type {
background: white;
padding: 15px;
border-radius: 8px;
box-shadow: 0 2px 5px rgba(0, 0, 0, 0.1);
}
.wbc-type-name {
font-weight: bold;
color: #1976d2;
margin-bottom: 5px;
}
.wbc-type-desc {
font-size: 0.9rem;
color: #666;
}
@media (max-width: 768px) {
.container {
margin: 10px;
}
.main-content {
padding: 20px;
}
.header h1 {
font-size: 2rem;
}
.image-preview {
justify-content: center;
}
.prediction-main {
flex-direction: column;
text-align: center;
}
}
</style>
</head>
<body>
<div class="container">
<div class="header">
<h1>🩸 White Blood Cell Classifier</h1>
<p>AI-Powered Classification of White Blood Cell Types</p>
</div>
<div class="main-content">
<div class="upload-section">
<div class="upload-area">
<div class="upload-icon">📤</div>
<h3>Upload WBC Images</h3>
<p>Select one or multiple images of white blood cells for classification</p>
<input type="file" id="imageInput" class="file-input" multiple accept="image/*">
<button class="upload-btn" onclick="document.getElementById('imageInput').click()">
Choose Images
</button>
</div>
</div>
<div class="preview-section" id="previewSection">
<h3>Selected Images</h3>
<div class="image-preview" id="imagePreview"></div>
<div style="text-align: center;">
<button class="predict-btn" id="predictBtn" onclick="classifyImages()">
🔬 Classify WBC Types
</button>
</div>
</div>
<div class="loading" id="loading">
<div class="spinner"></div>
<p>Analyzing images... Please wait</p>
</div>
<div class="results-section" id="resultsSection">
<h3>Classification Results</h3>
<div id="resultsContainer"></div>
</div>
<div class="wbc-info">
<h3>🧬 White Blood Cell Types</h3>
<div class="wbc-types">
<div class="wbc-type">
<div class="wbc-type-name">Neutrophil</div>
<div class="wbc-type-desc">Most abundant WBC, first responders to infection</div>
</div>
<div class="wbc-type">
<div class="wbc-type-name">Lymphocyte</div>
<div class="wbc-type-desc">Key players in adaptive immunity</div>
</div>
<div class="wbc-type">
<div class="wbc-type-name">Monocyte</div>
<div class="wbc-type-desc">Develop into macrophages and dendritic cells</div>
</div>
<div class="wbc-type">
<div class="wbc-type-name">Eosinophil</div>
<div class="wbc-type-desc">Combat parasites and involved in allergic reactions</div>
</div>
<div class="wbc-type">
<div class="wbc-type-name">Basophil</div>
<div class="wbc-type-desc">Least common, release histamine during allergic reactions</div>
</div>
</div>
</div>
</div>
</div>
<script>
let selectedFiles = [];
document.getElementById('imageInput').addEventListener('change', function(e) {
selectedFiles = Array.from(e.target.files);
displayPreview();
});
function displayPreview() {
const previewSection = document.getElementById('previewSection');
const imagePreview = document.getElementById('imagePreview');
if (selectedFiles.length === 0) {
previewSection.style.display = 'none';
return;
}
previewSection.style.display = 'block';
imagePreview.innerHTML = '';
selectedFiles.forEach((file, index) => {
const reader = new FileReader();
reader.onload = function(e) {
const previewItem = document.createElement('div');
previewItem.className = 'preview-item';
previewItem.innerHTML = `
<img src="${e.target.result}" alt="Preview ${index + 1}">
<div class="preview-info">
<div class="filename">${file.name}</div>
<div class="filesize">${(file.size / 1024).toFixed(1)} KB</div>
</div>
`;
imagePreview.appendChild(previewItem);
};
reader.readAsDataURL(file);
});
}
async function classifyImages() {
if (selectedFiles.length === 0) {
alert('Please select at least one image first!');
return;
}
const loading = document.getElementById('loading');
const resultsSection = document.getElementById('resultsSection');
const resultsContainer = document.getElementById('resultsContainer');
const predictBtn = document.getElementById('predictBtn');
// Show loading
loading.style.display = 'block';
resultsSection.style.display = 'none';
predictBtn.disabled = true;
try {
const formData = new FormData();
selectedFiles.forEach(file => {
formData.append('images', file);
});
const response = await fetch('/api/predict_batch', {
method: 'POST',
body: formData
});
const data = await response.json();
if (data.success) {
displayResults(data.batch_results);
} else {
throw new Error(data.error || 'Classification failed');
}
} catch (error) {
console.error('Error:', error);
resultsContainer.innerHTML = `
<div class="error">
<strong>Error:</strong> ${error.message}
</div>
`;
resultsSection.style.display = 'block';
} finally {
loading.style.display = 'none';
predictBtn.disabled = false;
}
}
function displayResults(results) {
const resultsContainer = document.getElementById('resultsContainer');
const resultsSection = document.getElementById('resultsSection');
resultsContainer.innerHTML = '';
results.forEach((result, index) => {
const resultItem = document.createElement('div');
resultItem.className = 'result-item';
if (result.success) {
const predictions = result.results;
const imageUrl = URL.createObjectURL(selectedFiles[index]);
resultItem.innerHTML = `
<div class="result-header">
<span><strong>${result.filename}</strong></span>
<span>Classification Complete ✅</span>
</div>
<div class="result-body">
<div class="prediction-main">
<img src="${imageUrl}" alt="${result.filename}">
<div class="prediction-details">
<div class="predicted-class">
🩸 ${predictions.predicted_class}
</div>
<div class="confidence">
Confidence: ${(predictions.confidence * 100).toFixed(1)}%
</div>
</div>
</div>
<div class="probabilities">
<h4>All Class Probabilities:</h4>
${Object.entries(predictions.all_probabilities)
.map(([className, probability]) => `
<div class="probability-item">
<div class="probability-label">${className}</div>
<div class="probability-bar">
<div class="probability-fill" style="width: ${probability * 100}%"></div>
</div>
<div class="probability-value">${(probability * 100).toFixed(1)}%</div>
</div>
`).join('')}
</div>
</div>
`;
} else {
resultItem.innerHTML = `
<div class="result-header">
<span><strong>${result.filename}</strong></span>
<span>Error ❌</span>
</div>
<div class="result-body">
<div class="error">
<strong>Classification failed:</strong> ${result.error}
</div>
</div>
`;
}
resultsContainer.appendChild(resultItem);
});
resultsSection.style.display = 'block';
}
// Health check on page load
window.addEventListener('load', async function() {
try {
const response = await fetch('/api/health');
const data = await response.json();
if (!data.model_loaded) {
document.body.innerHTML = `
<div style="text-align: center; padding: 50px; color: red;">
<h1>⚠️ Model Loading Error</h1>
<p>The classification model could not be loaded. Please check the server logs.</p>
</div>
`;
}
} catch (error) {
console.error('Health check failed:', error);
}
});
</script>
</body>
</html>