| import streamlit as st
|
| import torch
|
| import torch.nn as nn
|
| from torchvision import models, transforms
|
| from PIL import Image
|
| import numpy as np
|
|
|
|
|
| st.set_page_config(
|
| page_title="DermaScan AI",
|
| page_icon="🔬",
|
| layout="wide",
|
| initial_sidebar_state="expanded"
|
| )
|
|
|
|
|
| st.markdown("""
|
| <style>
|
| @import url('https://fonts.googleapis.com/css2?family=Syne:wght@400;600;700;800&family=DM+Sans:ital,opsz,wght@0,9..40,300;0,9..40,400;0,9..40,500;1,9..40,300&display=swap');
|
|
|
| :root {
|
| --bg-base: #0A0E1A;
|
| --bg-surface: #111827;
|
| --bg-elevated: #1A2235;
|
| --border: rgba(99,179,237,0.12);
|
| --border-bright: rgba(99,179,237,0.35);
|
| --accent-cyan: #63B3ED;
|
| --accent-teal: #4FD1C5;
|
| --accent-green: #68D391;
|
| --accent-amber: #F6AD55;
|
| --accent-red: #FC8181;
|
| --text-primary: #EDF2F7;
|
| --text-secondary:#A0AEC0;
|
| --text-muted: #4A5568;
|
| }
|
|
|
| html, body, [data-testid="stAppViewContainer"] {
|
| background: var(--bg-base) !important;
|
| font-family: 'DM Sans', sans-serif;
|
| color: var(--text-primary);
|
| }
|
|
|
| [data-testid="stSidebar"] {
|
| background: var(--bg-surface) !important;
|
| border-right: 1px solid var(--border) !important;
|
| }
|
|
|
| [data-testid="stSidebar"] * {
|
| color: var(--text-primary) !important;
|
| }
|
|
|
| /* Hide default streamlit chrome */
|
| #MainMenu, footer, header { visibility: hidden; }
|
| [data-testid="stDecoration"] { display: none; }
|
|
|
| /* ── Hero banner ── */
|
| .hero {
|
| display: flex;
|
| align-items: center;
|
| gap: 1.5rem;
|
| padding: 2.2rem 2.5rem;
|
| background: linear-gradient(135deg, #0D1B2E 0%, #112240 60%, #0D2137 100%);
|
| border: 1px solid var(--border-bright);
|
| border-radius: 16px;
|
| margin-bottom: 2rem;
|
| position: relative;
|
| overflow: hidden;
|
| }
|
| .hero::before {
|
| content: '';
|
| position: absolute;
|
| top: -40px; right: -40px;
|
| width: 220px; height: 220px;
|
| background: radial-gradient(circle, rgba(99,179,237,0.12) 0%, transparent 70%);
|
| border-radius: 50%;
|
| }
|
| .hero-icon {
|
| font-size: 3rem;
|
| line-height: 1;
|
| filter: drop-shadow(0 0 12px rgba(99,179,237,0.5));
|
| }
|
| .hero-text h1 {
|
| font-family: 'Syne', sans-serif;
|
| font-size: 2rem;
|
| font-weight: 800;
|
| color: var(--text-primary);
|
| margin: 0 0 0.2rem 0;
|
| letter-spacing: -0.5px;
|
| }
|
| .hero-text p {
|
| font-size: 0.95rem;
|
| color: var(--text-secondary);
|
| margin: 0;
|
| font-weight: 300;
|
| letter-spacing: 0.02em;
|
| }
|
| .hero-badge {
|
| margin-left: auto;
|
| background: rgba(99,179,237,0.1);
|
| border: 1px solid var(--border-bright);
|
| color: var(--accent-cyan);
|
| padding: 0.4rem 1rem;
|
| border-radius: 20px;
|
| font-size: 0.78rem;
|
| font-weight: 600;
|
| letter-spacing: 0.08em;
|
| text-transform: uppercase;
|
| white-space: nowrap;
|
| }
|
|
|
| /* ── Upload zone ── */
|
| .upload-label {
|
| font-family: 'Syne', sans-serif;
|
| font-size: 0.75rem;
|
| font-weight: 700;
|
| letter-spacing: 0.12em;
|
| text-transform: uppercase;
|
| color: var(--accent-cyan);
|
| margin-bottom: 0.5rem;
|
| }
|
| [data-testid="stFileUploader"] {
|
| background: var(--bg-elevated) !important;
|
| border: 1.5px dashed var(--border-bright) !important;
|
| border-radius: 12px !important;
|
| padding: 0.5rem !important;
|
| transition: border-color 0.2s;
|
| }
|
| [data-testid="stFileUploader"]:hover {
|
| border-color: var(--accent-cyan) !important;
|
| }
|
| [data-testid="stFileUploader"] * { color: var(--text-secondary) !important; }
|
|
|
| /* ── Section headers ── */
|
| .section-header {
|
| display: flex;
|
| align-items: center;
|
| gap: 0.6rem;
|
| margin-bottom: 1rem;
|
| }
|
| .section-header span.icon { font-size: 1rem; }
|
| .section-header span.label {
|
| font-family: 'Syne', sans-serif;
|
| font-size: 0.72rem;
|
| font-weight: 700;
|
| letter-spacing: 0.13em;
|
| text-transform: uppercase;
|
| color: var(--accent-cyan);
|
| }
|
| .section-divider {
|
| flex: 1;
|
| height: 1px;
|
| background: var(--border);
|
| margin-left: 0.5rem;
|
| }
|
|
|
| /* ── Image panel ── */
|
| .img-panel {
|
| background: var(--bg-surface);
|
| border: 1px solid var(--border);
|
| border-radius: 14px;
|
| padding: 1.2rem;
|
| overflow: hidden;
|
| }
|
| .img-panel img {
|
| border-radius: 10px !important;
|
| width: 100% !important;
|
| }
|
| .img-meta {
|
| display: flex;
|
| justify-content: space-between;
|
| margin-top: 0.8rem;
|
| padding-top: 0.8rem;
|
| border-top: 1px solid var(--border);
|
| }
|
| .img-meta-item {
|
| text-align: center;
|
| }
|
| .img-meta-item .val {
|
| font-family: 'Syne', sans-serif;
|
| font-size: 0.9rem;
|
| font-weight: 700;
|
| color: var(--text-primary);
|
| }
|
| .img-meta-item .key {
|
| font-size: 0.7rem;
|
| color: var(--text-muted);
|
| text-transform: uppercase;
|
| letter-spacing: 0.08em;
|
| }
|
|
|
| /* ── Primary diagnosis card ── */
|
| .diagnosis-card {
|
| background: linear-gradient(135deg, #0D1B2E 0%, #0D2137 100%);
|
| border: 1px solid var(--border-bright);
|
| border-radius: 14px;
|
| padding: 1.5rem 1.8rem;
|
| margin-bottom: 1.2rem;
|
| position: relative;
|
| overflow: hidden;
|
| }
|
| .diagnosis-card::after {
|
| content: '';
|
| position: absolute;
|
| bottom: -30px; right: -30px;
|
| width: 120px; height: 120px;
|
| background: radial-gradient(circle, rgba(79,209,197,0.1) 0%, transparent 70%);
|
| border-radius: 50%;
|
| }
|
| .diagnosis-label {
|
| font-size: 0.68rem;
|
| font-weight: 700;
|
| letter-spacing: 0.14em;
|
| text-transform: uppercase;
|
| color: var(--text-muted);
|
| margin-bottom: 0.4rem;
|
| }
|
| .diagnosis-name {
|
| font-family: 'Syne', sans-serif;
|
| font-size: 1.5rem;
|
| font-weight: 800;
|
| color: var(--text-primary);
|
| margin-bottom: 0.8rem;
|
| line-height: 1.2;
|
| }
|
| .confidence-pill {
|
| display: inline-flex;
|
| align-items: center;
|
| gap: 0.4rem;
|
| padding: 0.35rem 0.85rem;
|
| border-radius: 20px;
|
| font-size: 0.82rem;
|
| font-weight: 600;
|
| }
|
| .pill-high { background: rgba(104,211,145,0.15); color: var(--accent-green); border: 1px solid rgba(104,211,145,0.3); }
|
| .pill-medium { background: rgba(246,173,85,0.15); color: var(--accent-amber); border: 1px solid rgba(246,173,85,0.3); }
|
| .pill-low { background: rgba(252,129,129,0.15); color: var(--accent-red); border: 1px solid rgba(252,129,129,0.3); }
|
| .pill-dot {
|
| width: 6px; height: 6px;
|
| border-radius: 50%;
|
| background: currentColor;
|
| }
|
|
|
| /* ── Prediction rows ── */
|
| .pred-row {
|
| background: var(--bg-elevated);
|
| border: 1px solid var(--border);
|
| border-radius: 10px;
|
| padding: 0.9rem 1.2rem;
|
| margin-bottom: 0.6rem;
|
| transition: border-color 0.2s;
|
| }
|
| .pred-row:hover { border-color: var(--border-bright); }
|
| .pred-row-top {
|
| display: flex;
|
| justify-content: space-between;
|
| align-items: center;
|
| margin-bottom: 0.5rem;
|
| }
|
| .pred-rank {
|
| font-family: 'Syne', sans-serif;
|
| font-size: 0.65rem;
|
| font-weight: 700;
|
| color: var(--text-muted);
|
| letter-spacing: 0.1em;
|
| }
|
| .pred-name {
|
| font-weight: 500;
|
| color: var(--text-primary);
|
| font-size: 0.92rem;
|
| }
|
| .pred-pct {
|
| font-family: 'Syne', sans-serif;
|
| font-size: 0.88rem;
|
| font-weight: 700;
|
| color: var(--accent-cyan);
|
| }
|
| .pred-bar-track {
|
| height: 4px;
|
| background: var(--bg-base);
|
| border-radius: 4px;
|
| overflow: hidden;
|
| }
|
| .pred-bar-fill {
|
| height: 100%;
|
| border-radius: 4px;
|
| background: linear-gradient(90deg, var(--accent-cyan) 0%, var(--accent-teal) 100%);
|
| transition: width 0.6s ease;
|
| }
|
|
|
| /* ── Clinical note ── */
|
| .clinical-note {
|
| background: rgba(99,179,237,0.06);
|
| border: 1px solid rgba(99,179,237,0.2);
|
| border-left: 3px solid var(--accent-cyan);
|
| border-radius: 10px;
|
| padding: 1rem 1.2rem;
|
| margin-top: 1.5rem;
|
| }
|
| .clinical-note .cn-title {
|
| font-family: 'Syne', sans-serif;
|
| font-size: 0.7rem;
|
| font-weight: 700;
|
| letter-spacing: 0.12em;
|
| text-transform: uppercase;
|
| color: var(--accent-cyan);
|
| margin-bottom: 0.3rem;
|
| }
|
| .clinical-note p {
|
| font-size: 0.83rem;
|
| color: var(--text-secondary);
|
| margin: 0;
|
| line-height: 1.6;
|
| }
|
|
|
| /* ── Sidebar styles ── */
|
| .sidebar-section-title {
|
| font-family: 'Syne', sans-serif;
|
| font-size: 0.68rem;
|
| font-weight: 700;
|
| letter-spacing: 0.13em;
|
| text-transform: uppercase;
|
| color: #63B3ED !important;
|
| margin-bottom: 0.6rem;
|
| }
|
| .sidebar-info {
|
| background: rgba(99,179,237,0.07) !important;
|
| border: 1px solid rgba(99,179,237,0.18) !important;
|
| border-radius: 10px !important;
|
| padding: 0.9rem !important;
|
| font-size: 0.83rem !important;
|
| color: #A0AEC0 !important;
|
| line-height: 1.6 !important;
|
| }
|
| .model-spec-row {
|
| display: flex;
|
| justify-content: space-between;
|
| padding: 0.45rem 0;
|
| border-bottom: 1px solid rgba(99,179,237,0.1);
|
| font-size: 0.82rem;
|
| }
|
| .model-spec-row:last-child { border-bottom: none; }
|
| .spec-key { color: #4A5568; }
|
| .spec-val { color: #EDF2F7; font-weight: 500; }
|
|
|
| /* ── Empty state ── */
|
| .empty-state {
|
| text-align: center;
|
| padding: 4rem 2rem;
|
| background: var(--bg-surface);
|
| border: 1.5px dashed var(--border);
|
| border-radius: 16px;
|
| margin-top: 1rem;
|
| }
|
| .empty-state .es-icon { font-size: 3.5rem; margin-bottom: 1rem; opacity: 0.5; }
|
| .empty-state h3 {
|
| font-family: 'Syne', sans-serif;
|
| color: var(--text-secondary);
|
| font-size: 1.2rem;
|
| margin-bottom: 0.5rem;
|
| }
|
| .empty-state p { color: var(--text-muted); font-size: 0.9rem; }
|
|
|
| /* ── Streamlit overrides ── */
|
| [data-testid="stMarkdownContainer"] p { color: var(--text-secondary); }
|
| .stSpinner > div { color: var(--accent-cyan) !important; }
|
| [data-testid="stDataFrame"] {
|
| background: var(--bg-surface) !important;
|
| border: 1px solid var(--border) !important;
|
| border-radius: 10px !important;
|
| }
|
| [data-testid="stProgress"] > div > div > div > div {
|
| background: linear-gradient(90deg, #63B3ED, #4FD1C5) !important;
|
| border-radius: 4px;
|
| }
|
| [data-testid="stInfo"] {
|
| background: rgba(99,179,237,0.07) !important;
|
| border: 1px solid rgba(99,179,237,0.2) !important;
|
| color: var(--text-secondary) !important;
|
| border-radius: 10px !important;
|
| }
|
| </style>
|
| """, unsafe_allow_html=True)
|
|
|
|
|
| @st.cache_resource
|
| def load_model():
|
| classes = np.load('classes.npy', allow_pickle=True)
|
| num_classes = len(classes)
|
| model = models.resnet50(weights=None)
|
| num_features = model.fc.in_features
|
| model.fc = nn.Sequential(
|
| nn.Linear(num_features, 1024), nn.ReLU(), nn.Dropout(0.2),
|
| nn.Linear(1024, 512), nn.ReLU(), nn.Dropout(0.1),
|
| nn.Linear(512, 128), nn.ReLU(),
|
| nn.Linear(128, num_classes)
|
| )
|
| model.load_state_dict(torch.load('skin_lesion_resnet50_best.pth', map_location='cpu'))
|
| model.eval()
|
| return model, classes
|
|
|
| def preprocess(image):
|
| transform = transforms.Compose([
|
| transforms.Resize((224, 224)),
|
| transforms.ToTensor(),
|
| transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
|
| ])
|
| return transform(image).unsqueeze(0)
|
|
|
| def confidence_pill(conf):
|
| if conf >= 0.80:
|
| return f'<span class="confidence-pill pill-high"><span class="pill-dot"></span>{conf*100:.1f}% High Confidence</span>'
|
| elif conf >= 0.60:
|
| return f'<span class="confidence-pill pill-medium"><span class="pill-dot"></span>{conf*100:.1f}% Moderate</span>'
|
| else:
|
| return f'<span class="confidence-pill pill-low"><span class="pill-dot"></span>{conf*100:.1f}% Low Confidence</span>'
|
|
|
|
|
| st.markdown("""
|
| <div class="hero">
|
| <div class="hero-icon">🔬</div>
|
| <div class="hero-text">
|
| <h1>DermaScan AI</h1>
|
| <p>Deep learning–powered dermoscopy analysis · ResNet50 classification engine</p>
|
| </div>
|
| <div class="hero-badge">⚡ Live Inference</div>
|
| </div>
|
| """, unsafe_allow_html=True)
|
|
|
|
|
| with st.sidebar:
|
| st.markdown('<p class="sidebar-section-title">About</p>', unsafe_allow_html=True)
|
| st.markdown("""
|
| <div class="sidebar-info">
|
| DermaScan AI uses a fine-tuned <strong style="color:#EDF2F7">ResNet50</strong> convolutional network
|
| trained on a comprehensive dermoscopy dataset to classify skin lesion types from uploaded images.
|
| </div>
|
| """, unsafe_allow_html=True)
|
|
|
| st.markdown("<br>", unsafe_allow_html=True)
|
| st.markdown('<p class="sidebar-section-title">Model Specs</p>', unsafe_allow_html=True)
|
| st.markdown("""
|
| <div style="background:rgba(99,179,237,0.05); border:1px solid rgba(99,179,237,0.15); border-radius:10px; padding:0.7rem 1rem;">
|
| <div class="model-spec-row"><span class="spec-key">Architecture</span><span class="spec-val">ResNet50</span></div>
|
| <div class="model-spec-row"><span class="spec-key">Framework</span><span class="spec-val">PyTorch</span></div>
|
| <div class="model-spec-row"><span class="spec-key">Input Size</span><span class="spec-val">224 × 224 px</span></div>
|
| <div class="model-spec-row"><span class="spec-key">Task</span><span class="spec-val">Multi-class</span></div>
|
| <div class="model-spec-row"><span class="spec-key">Normalization</span><span class="spec-val">ImageNet</span></div>
|
| </div>
|
| """, unsafe_allow_html=True)
|
|
|
| st.markdown("<br>", unsafe_allow_html=True)
|
| st.markdown('<p class="sidebar-section-title">Instructions</p>', unsafe_allow_html=True)
|
| st.markdown("""
|
| <div style="font-size:0.82rem; color:#718096; line-height:1.8;">
|
| 1. Upload a dermoscopy image (JPG / PNG)<br>
|
| 2. Wait for model inference to complete<br>
|
| 3. Review ranked predictions and confidence scores<br>
|
| 4. Consult a dermatologist for clinical decisions
|
| </div>
|
| """, unsafe_allow_html=True)
|
|
|
|
|
| st.markdown("""
|
| <div class="section-header">
|
| <span class="icon">📂</span>
|
| <span class="label">Image Upload</span>
|
| <div class="section-divider"></div>
|
| </div>
|
| """, unsafe_allow_html=True)
|
|
|
| uploaded_file = st.file_uploader(
|
| "Drop a dermoscopy image here, or click to browse",
|
| type=["jpg", "jpeg", "png"],
|
| help="Supported: JPG, JPEG, PNG"
|
| )
|
|
|
|
|
| if uploaded_file:
|
| image = Image.open(uploaded_file).convert("RGB")
|
| w, h = image.size
|
|
|
| col1, col2 = st.columns([1, 1], gap="large")
|
|
|
| with col1:
|
| st.markdown("""
|
| <div class="section-header" style="margin-top:1.2rem">
|
| <span class="icon">🖼</span>
|
| <span class="label">Input Image</span>
|
| <div class="section-divider"></div>
|
| </div>
|
| """, unsafe_allow_html=True)
|
|
|
| st.markdown('<div class="img-panel">', unsafe_allow_html=True)
|
| st.image(image, use_column_width=True)
|
| st.markdown(f"""
|
| <div class="img-meta">
|
| <div class="img-meta-item">
|
| <div class="val">{w} × {h}</div>
|
| <div class="key">Resolution</div>
|
| </div>
|
| <div class="img-meta-item">
|
| <div class="val">{uploaded_file.name.split('.')[-1].upper()}</div>
|
| <div class="key">Format</div>
|
| </div>
|
| <div class="img-meta-item">
|
| <div class="val">{uploaded_file.size // 1024} KB</div>
|
| <div class="key">File Size</div>
|
| </div>
|
| <div class="img-meta-item">
|
| <div class="val">RGB</div>
|
| <div class="key">Color Mode</div>
|
| </div>
|
| </div>
|
| </div>
|
| """, unsafe_allow_html=True)
|
|
|
| with col2:
|
| st.markdown("""
|
| <div class="section-header" style="margin-top:1.2rem">
|
| <span class="icon">🎯</span>
|
| <span class="label">Analysis Results</span>
|
| <div class="section-divider"></div>
|
| </div>
|
| """, unsafe_allow_html=True)
|
|
|
| with st.spinner("Running model inference…"):
|
| model, classes = load_model()
|
| tensor = preprocess(image)
|
| with torch.no_grad():
|
| outputs = model(tensor)
|
| probs = torch.softmax(outputs, dim=1)[0]
|
| top_prob, top_idx = torch.topk(probs, 3)
|
|
|
| top_prediction = classes[top_idx[0]]
|
| top_confidence = float(top_prob[0])
|
|
|
|
|
| st.markdown(f"""
|
| <div class="diagnosis-card">
|
| <div class="diagnosis-label">Primary Diagnosis</div>
|
| <div class="diagnosis-name">{top_prediction}</div>
|
| {confidence_pill(top_confidence)}
|
| </div>
|
| """, unsafe_allow_html=True)
|
|
|
|
|
| st.markdown("""
|
| <div class="section-header" style="margin-top:1rem">
|
| <span class="icon">📊</span>
|
| <span class="label">Ranked Predictions</span>
|
| <div class="section-divider"></div>
|
| </div>
|
| """, unsafe_allow_html=True)
|
|
|
| rank_labels = ["1st", "2nd", "3rd"]
|
| for i, (prob, idx) in enumerate(zip(top_prob, top_idx)):
|
| conf = float(prob)
|
| bar_width = int(conf * 100)
|
| st.markdown(f"""
|
| <div class="pred-row">
|
| <div class="pred-row-top">
|
| <div>
|
| <span class="pred-rank">{rank_labels[i]} </span>
|
| <span class="pred-name">{classes[idx]}</span>
|
| </div>
|
| <span class="pred-pct">{conf*100:.1f}%</span>
|
| </div>
|
| <div class="pred-bar-track">
|
| <div class="pred-bar-fill" style="width:{bar_width}%"></div>
|
| </div>
|
| </div>
|
| """, unsafe_allow_html=True)
|
|
|
|
|
| st.markdown("<br>", unsafe_allow_html=True)
|
| st.markdown("""
|
| <div class="section-header">
|
| <span class="icon">📋</span>
|
| <span class="label">Classification Summary</span>
|
| <div class="section-divider"></div>
|
| </div>
|
| """, unsafe_allow_html=True)
|
|
|
| results_data = {
|
| "Rank": [f"#{i+1}" for i in range(len(top_prob))],
|
| "Diagnosis": [classes[idx] for idx in top_idx],
|
| "Confidence": [f"{prob*100:.2f}%" for prob in top_prob],
|
| "Status": [
|
| "✅ Primary" if i == 0 else ("⚠️ Alternate" if i == 1 else "ℹ️ Low prob")
|
| for i in range(len(top_prob))
|
| ]
|
| }
|
| st.dataframe(results_data, use_container_width=True, hide_index=True)
|
|
|
|
|
| st.markdown("""
|
| <div class="clinical-note">
|
| <div class="cn-title">⚕️ Clinical Disclaimer</div>
|
| <p>This AI classification is intended for <strong style="color:#EDF2F7">informational and research purposes only</strong>.
|
| It does not constitute a medical diagnosis. Always consult a board-certified dermatologist
|
| for clinical evaluation, diagnosis, and treatment recommendations.</p>
|
| </div>
|
| """, unsafe_allow_html=True)
|
|
|
| else:
|
| st.markdown("""
|
| <div class="empty-state">
|
| <div class="es-icon">🔬</div>
|
| <h3>No Image Uploaded</h3>
|
| <p>Upload a dermoscopy image above to begin skin lesion classification</p>
|
| </div>
|
| """, unsafe_allow_html=True) |
