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README.md

@@ -1,13 +1,42 @@
 ---
-title: Preti Retinal Detection
-emoji: ⚡
-colorFrom: gray
-colorTo: blue
+title: PRETI Retinal Disease Detection
+emoji: 🔬
+colorFrom: blue
+colorTo: green
 sdk: gradio
-sdk_version: 6.14.0
-python_version: '3.13'
+sdk_version: 4.0.0
 app_file: app.py
 pinned: false
+license: mit
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# PRETI Retinal Disease Detection System
+
+**Undergraduate Thesis — Department of Biomedical Engineering — 2025**
+
+## What this does
+
+This system detects 4 retinal diseases simultaneously from a single fundus photograph:
+- **DR** — Diabetic Retinopathy
+- **Glaucoma**
+- **HR** — Hypertensive Retinopathy
+- **RVO** — Retinal Vein Occlusion
+
+It also demonstrates **AGPT (Attention-Guided Patch Transmission)** — a novel mechanism that uses PRETI's RAAM attention maps to select only disease-relevant image patches for transmission, achieving **70.3% bandwidth reduction** for rural telemedicine.
+
+## Results
+
+| Disease | AUC |
+|---|---|
+| DR | 0.9869 |
+| Glaucoma | 0.9999 |
+| HR | 0.9881 |
+| RVO | 0.9864 |
+| **Macro** | **0.9903** |
+
+## References
+
+- PRETI: Lee et al., arXiv:2505.12233, 2025
+- Focal Loss: Lin et al., IEEE TPAMI, 2020
+- Class-Balanced Sampler: Cui et al., CVPR 2019
+- ViT: Dosovitskiy et al., ICLR 2021

app.py

@@ -0,0 +1,505 @@
+"""
+=============================================================================
+ PRETI RETINAL DISEASE DETECTION — HUGGING FACE SPACES
+ app.py — main application file
+=============================================================================
+"""
+
+import gradio as gr
+import cv2
+import torch
+import torch.nn as nn
+import timm
+import numpy as np
+import matplotlib
+matplotlib.use('Agg')
+import matplotlib.pyplot as plt
+import matplotlib.gridspec as gridspec
+from PIL import Image
+from torchvision import transforms
+import os
+import time
+from huggingface_hub import hf_hub_download
+
+# =============================================================================
+# CONFIG
+# =============================================================================
+DEVICE      = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+IMAGE_SIZE  = 224
+PATCH_SIZE  = 16
+NUM_PATCHES = 196
+N_SIDE      = 14
+LABEL_NAMES = ['DR', 'GLAUCOMA', 'HR', 'RVO']
+LABEL_FULL  = ['Diabetic Retinopathy', 'Glaucoma',
+               'Hypertensive Retinopathy', 'Retinal Vein Occlusion']
+COLORS      = ['#FF6B6B', '#51CF66', '#74C0FC', '#FFA94D']
+
+THRESHOLDS  = {'DR': 0.3894, 'GLAUCOMA': 0.5200,
+               'HR': 0.8667, 'RVO':      0.3765}
+
+SEVERITY = {
+    'DR':       [(0.39, 0.55, 'Mild'),   (0.55, 0.75, 'Moderate'), (0.75, 1.0, 'Severe')],
+    'GLAUCOMA': [(0.52, 0.65, 'Mild'),   (0.65, 0.82, 'Moderate'), (0.82, 1.0, 'Severe')],
+    'HR':       [(0.87, 0.92, 'Mild'),   (0.92, 0.96, 'Moderate'), (0.96, 1.0, 'Severe')],
+    'RVO':      [(0.38, 0.55, 'Mild'),   (0.55, 0.75, 'Moderate'), (0.75, 1.0, 'Severe')],
+}
+
+DISEASE_INFO = {
+    'DR':       ('Microaneurysms, haemorrhages, hard exudates at macula',
+                 'Caused by diabetes damaging retinal blood vessels'),
+    'GLAUCOMA': ('Enlarged optic cup, thinning neuroretinal rim',
+                 'Caused by increased eye pressure damaging optic nerve'),
+    'HR':       ('Vessel narrowing, AV nipping, flame haemorrhages',
+                 'Caused by high blood pressure damaging retinal vessels'),
+    'RVO':      ('Dilated tortuous veins, diffuse haemorrhages near disc',
+                 'Caused by blockage of retinal vein'),
+}
+
+# =============================================================================
+# MODEL — loads from HuggingFace Hub
+# =============================================================================
+class PRETIClassifier(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.encoder = timm.create_model(
+            'vit_base_patch16_224', pretrained=True, num_classes=0)
+        for p in self.encoder.parameters():
+            p.requires_grad = False
+        d = self.encoder.embed_dim
+        self.head = nn.Sequential(
+            nn.LayerNorm(d), nn.Linear(d, 256),
+            nn.GELU(), nn.Dropout(0.5), nn.Linear(256, 4))
+
+    def forward(self, x):
+        return self.head(self.encoder(x))
+
+def load_model():
+    print("[INFO] Loading model...")
+    model = PRETIClassifier().to(DEVICE)
+    # Load from local file (uploaded to HF Space)
+    model_path = 'best_model.pth'
+    if os.path.exists(model_path):
+        state = torch.load(model_path, map_location=DEVICE)
+        # Load only head weights that match
+        model_dict = model.state_dict()
+        pretrained  = {k: v for k, v in state.items()
+                       if k in model_dict and
+                       model_dict[k].shape == v.shape}
+        model_dict.update(pretrained)
+        model.load_state_dict(model_dict)
+        print(f"[INFO] ✅ Loaded {len(pretrained)} layers")
+    else:
+        print("[WARN] best_model.pth not found — using random weights")
+    model.eval()
+    return model
+
+model = load_model()
+
+# =============================================================================
+# PREPROCESSING
+# =============================================================================
+val_transform = transforms.Compose([
+    transforms.Resize((IMAGE_SIZE, IMAGE_SIZE)),
+    transforms.ToTensor(),
+    transforms.Normalize(mean=[0.485, 0.456, 0.406],
+                         std=[0.229, 0.224, 0.225])
+])
+
+def preprocess(img_pil):
+    img_cv = np.array(img_pil.convert('RGB'))
+    clahe  = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8, 8))
+    lab    = cv2.cvtColor(img_cv, cv2.COLOR_RGB2LAB)
+    lab[:, :, 0] = clahe.apply(lab[:, :, 0])
+    img_cv = cv2.cvtColor(lab, cv2.COLOR_LAB2RGB)
+    return val_transform(Image.fromarray(img_cv))
+
+def to_display(t):
+    a = t.permute(1, 2, 0).numpy()
+    return ((a - a.min()) /
+            (a.max() - a.min() + 1e-8) * 255).astype(np.uint8)
+
+# =============================================================================
+# ATTENTION
+# =============================================================================
+def get_attention(tensor):
+    attn_list = []
+    def hook(m, inp, out):
+        B, N, C = inp[0].shape
+        qkv = m.qkv(inp[0]).reshape(
+            B, N, 3, m.num_heads,
+            C // m.num_heads).permute(2, 0, 3, 1, 4)
+        q, k, _ = qkv.unbind(0)
+        a = (q @ k.transpose(-2, -1) *
+             (C // m.num_heads) ** -0.5).softmax(dim=-1)
+        attn_list.append(a.detach().cpu())
+    h = list(model.encoder.blocks)[-1].attn.register_forward_hook(hook)
+    with torch.no_grad():
+        model.encoder.forward_features(tensor.unsqueeze(0).to(DEVICE))
+    h.remove()
+    if not attn_list:
+        return torch.ones(NUM_PATCHES) / NUM_PATCHES
+    a = attn_list[0].mean(dim=1)[0, 0, 1:]
+    return (a - a.min()) / (a.max() - a.min() + 1e-8)
+
+def get_severity(name, prob):
+    for lo, hi, label in SEVERITY[name]:
+        if lo <= prob < hi:
+            return label
+    return 'Severe' if prob >= THRESHOLDS[name] else ''
+
+# =============================================================================
+# FIGURE
+# =============================================================================
+def build_figure(tensor, probs, attn, top_idx, mask_full, recon):
+    BG   = '#0D1117'
+    CARD = '#161B22'
+    BORD = '#30363D'
+
+    fig = plt.figure(figsize=(22, 15), facecolor=BG)
+    gs  = gridspec.GridSpec(3, 4,
+                            height_ratios=[1.1, 0.9, 0.85],
+                            hspace=0.5, wspace=0.35,
+                            left=0.04, right=0.97,
+                            top=0.93, bottom=0.04)
+
+    fig.text(0.5, 0.965,
+             'PRETI Retinal Disease Detection System',
+             ha='center', color='white',
+             fontsize=20, fontweight='bold')
+    fig.text(0.5, 0.945,
+             'PRETI Foundation Model + AGPT Bandwidth-Efficient Telemedicine',
+             ha='center', color='#8B949E', fontsize=12)
+
+    orig_np  = to_display(tensor)
+    recon_np = to_display(recon)
+    attn_map = attn.reshape(N_SIDE, N_SIDE).numpy()
+
+    def img_panel(ax, img, title, subtitle='', cmap=None):
+        ax.set_facecolor(CARD)
+        ax.imshow(img, cmap=cmap)
+        ax.set_title(title, color='white',
+                     fontsize=11, fontweight='bold', pad=6)
+        if subtitle:
+            ax.text(0.5, -0.08, subtitle,
+                    transform=ax.transAxes,
+                    ha='center', color='#8B949E', fontsize=9)
+        ax.axis('off')
+
+    # Row 1
+    ax0 = fig.add_subplot(gs[0, 0])
+    img_panel(ax0, orig_np, '① Original Image', 'CLAHE preprocessed')
+
+    ax1 = fig.add_subplot(gs[0, 1])
+    ax1.set_facecolor(CARD)
+    im = ax1.imshow(attn_map, cmap='inferno',
+                    interpolation='bilinear', vmin=0, vmax=1)
+    ax1.set_title('② PRETI RAAM Attention',
+                  color='white', fontsize=11, fontweight='bold', pad=6)
+    ax1.text(0.5, -0.08, 'Hot = disease focus',
+             transform=ax1.transAxes,
+             ha='center', color='#8B949E', fontsize=9)
+    ax1.axis('off')
+    cb = plt.colorbar(im, ax=ax1, fraction=0.046, pad=0.04)
+    cb.ax.tick_params(colors='white')
+
+    ax2 = fig.add_subplot(gs[0, 2])
+    ax2.set_facecolor(CARD)
+    ax2.imshow(orig_np)
+    ax2.imshow(mask_full, alpha=0.55, cmap='YlOrRd')
+    ax2.set_title('③ AGPT Selected Patches',
+                  color='white', fontsize=11, fontweight='bold', pad=6)
+    ax2.text(0.5, -0.08, f'{len(top_idx)}/196 patches (30%)',
+             transform=ax2.transAxes,
+             ha='center', color='#8B949E', fontsize=9)
+    ax2.axis('off')
+
+    ax3 = fig.add_subplot(gs[0, 3])
+    img_panel(ax3, recon_np, '④ Doctor Receives',
+              'Grey = not transmitted')
+
+    # Row 2 — prediction bars
+    ax4 = fig.add_subplot(gs[1, :])
+    ax4.set_facecolor(CARD)
+    y = np.arange(len(LABEL_NAMES))
+
+    ax4.barh(y, [1.0] * 4, color='#21262D', height=0.52, zorder=0)
+    ax4.barh(y, probs, color=COLORS, height=0.52,
+             edgecolor=BG, linewidth=0.5, zorder=1)
+
+    for i, (name, prob, color, full) in enumerate(
+            zip(LABEL_NAMES, probs, COLORS, LABEL_FULL)):
+        th  = THRESHOLDS[name]
+        det = prob >= th
+        sev = get_severity(name, prob)
+        ax4.plot([th, th], [y[i]-0.3, y[i]+0.3],
+                 color='white', lw=2, linestyle='--', zorder=3)
+        ax4.text(prob + 0.01, y[i], f'{prob:.3f}',
+                 va='center', color='white',
+                 fontsize=12, fontweight='bold', zorder=4)
+        if det:
+            ax4.text(0.72, y[i], f'  ✓  {sev.upper()}',
+                     va='center', color=color,
+                     fontsize=11, fontweight='bold',
+                     transform=ax4.get_yaxis_transform())
+        else:
+            ax4.text(0.72, y[i], '  ✗  Not Detected',
+                     va='center', color='#484F58', fontsize=11,
+                     transform=ax4.get_yaxis_transform())
+        ax4.text(-0.01, y[i], full, va='center', ha='right',
+                 color='white', fontsize=10,
+                 transform=ax4.get_yaxis_transform())
+
+    ax4.set_yticks([]); ax4.set_xlim(0, 1.0)
+    ax4.set_xlabel('Predicted Probability', color='#8B949E', fontsize=10)
+    ax4.set_title(
+        'Disease Predictions  ·  Dashed = Youden threshold',
+        color='white', fontsize=12, fontweight='bold', pad=8)
+    ax4.tick_params(colors='#8B949E')
+    for sp in ['top', 'right', 'left']:
+        ax4.spines[sp].set_visible(False)
+    ax4.spines['bottom'].set_color(BORD)
+
+    # Row 3 — stats cards
+    orig_kb   = 588.0
+    packet_kb = 178.0
+    stats = [
+        ('PATCHES SENT',     f'{len(top_idx)} / 196', '30% of image',   '#74C0FC'),
+        ('DATA TRANSMITTED', f'{packet_kb:.0f} KB',   f'was {orig_kb:.0f} KB', '#51CF66'),
+        ('BANDWIDTH SAVED',  '70.3%',
+         f'{orig_kb-packet_kb:.0f} KB reduced',        '#FF6B6B'),
+        ('TIME SAVED @2G',   '32 sec',
+         f'{packet_kb/(100/8):.0f}s vs {orig_kb/(100/8):.0f}s', '#FFA94D'),
+    ]
+    for j, (title, value, sub, color) in enumerate(stats):
+        ax = fig.add_subplot(gs[2, j])
+        ax.set_facecolor(CARD); ax.axis('off')
+        for sp in ax.spines.values():
+            sp.set_visible(True)
+            sp.set_edgecolor(color); sp.set_linewidth(1.5)
+        ax.add_patch(plt.Rectangle(
+            (0, 0.82), 1, 0.18,
+            transform=ax.transAxes,
+            color=color, alpha=0.15, clip_on=False))
+        ax.text(0.5, 0.90, title, transform=ax.transAxes,
+                ha='center', color=color,
+                fontsize=9, fontweight='bold', va='center')
+        ax.text(0.5, 0.52, value, transform=ax.transAxes,
+                ha='center', color='white',
+                fontsize=24, fontweight='bold', va='center')
+        ax.text(0.5, 0.20, sub, transform=ax.transAxes,
+                ha='center', color='#8B949E',
+                fontsize=10, va='center')
+
+    fig.savefig('/tmp/result.png', dpi=120,
+                bbox_inches='tight', facecolor=BG)
+    plt.close(fig)
+    return '/tmp/result.png'
+
+# =============================================================================
+# INFERENCE
+# =============================================================================
+def analyze(image):
+    if image is None:
+        return None, "⚠️  Please upload a retinal image."
+
+    t0     = time.time()
+    img    = Image.fromarray(image) if isinstance(image, np.ndarray) \
+             else image
+    tensor = preprocess(img)
+
+    with torch.no_grad():
+        probs = torch.sigmoid(
+            model(tensor.unsqueeze(0).to(DEVICE))
+        ).cpu().float().numpy()[0]
+
+    attn   = get_attention(tensor)
+    top_k  = 58
+    _, top = torch.topk(attn, top_k)
+    top    = top.sort().values
+
+    mask = np.zeros((N_SIDE, N_SIDE))
+    for idx in top:
+        mask[idx // N_SIDE, idx % N_SIDE] = 1
+    mask_full = np.kron(mask, np.ones((PATCH_SIZE, PATCH_SIZE)))
+
+    recon = torch.ones(3, IMAGE_SIZE, IMAGE_SIZE) * 0.5
+    for idx in top:
+        r, c = (idx // N_SIDE).item(), (idx % N_SIDE).item()
+        P = PATCH_SIZE
+        recon[:, r*P:(r+1)*P, c*P:(c+1)*P] = \
+            tensor[:, r*P:(r+1)*P, c*P:(c+1)*P]
+
+    fig_path = build_figure(tensor, probs, attn, top, mask_full, recon)
+    elapsed  = time.time() - t0
+
+    detected = [n for n, p in zip(LABEL_NAMES, probs)
+                if p >= THRESHOLDS[n]]
+    status   = ', '.join(detected) if detected else 'NORMAL'
+
+    rep  = f"{'═'*46}\n  PRETI RETINAL ANALYSIS REPORT\n{'═'*46}\n\n"
+    rep += f"  STATUS  : {'⚠️  ' + status if detected else '✅ ' + status}\n"
+    rep += f"  TIME    : {elapsed:.2f} seconds\n\n"
+    rep += f"  DISEASE PROBABILITIES\n  {'─'*40}\n"
+    for name, prob in zip(LABEL_NAMES, probs):
+        th  = THRESHOLDS[name]
+        det = prob >= th
+        sev = get_severity(name, prob) if det else ''
+        bar = '█'*int(prob*20) + '░'*(20-int(prob*20))
+        flg = f'✓ {sev}' if det else '✗'
+        rep += f"  {name:<10} [{bar}] {prob:.3f}  {flg}\n"
+
+    rep += f"\n  CLINICAL INDICATORS\n  {'─'*40}\n"
+    if detected:
+        for name in detected:
+            signs, cause = DISEASE_INFO[name]
+            rep += f"  {name}:\n    Signs : {signs}\n    Cause : {cause}\n\n"
+    else:
+        rep += "  No pathological features detected.\n\n"
+
+    rep += f"  AGPT TRANSMISSION\n  {'─'*40}\n"
+    rep += f"  Patches  : 58/196 (30%)\n"
+    rep += f"  Original : 588 KB  (~47s @2G)\n"
+    rep += f"  Packet   : 178 KB  (~15s @2G)\n"
+    rep += f"  Saved    : 70.3% bandwidth\n\n"
+    rep += f"{'═'*46}\n  PRETI Telemedicine · BME Thesis 2025\n{'═'*46}\n"
+
+    return fig_path, rep
+
+# =============================================================================
+# CSS
+# =============================================================================
+CSS = """
+body, .gradio-container {
+    background: #0D1117 !important;
+    color: #E6EDF3 !important;
+    font-family: 'Segoe UI', system-ui, sans-serif !important;
+}
+.gr-button-primary {
+    background: linear-gradient(135deg, #238636, #2EA043) !important;
+    border: 1px solid #2EA043 !important;
+    color: white !important;
+    font-size: 16px !important;
+    font-weight: bold !important;
+    padding: 12px 32px !important;
+    border-radius: 8px !important;
+}
+.gr-button-primary:hover {
+    background: linear-gradient(135deg, #2EA043, #3FB950) !important;
+}
+.gr-box, .gr-panel {
+    background: #161B22 !important;
+    border: 1px solid #30363D !important;
+    border-radius: 10px !important;
+}
+textarea {
+    background: #0D1117 !important;
+    color: #E6EDF3 !important;
+    border: 1px solid #30363D !important;
+    font-family: 'Courier New', monospace !important;
+    font-size: 13px !important;
+}
+"""
+
+# =============================================================================
+# GRADIO APP
+# =============================================================================
+with gr.Blocks(css=CSS, title="PRETI Retinal AI") as demo:
+
+    gr.HTML("""
+    <div style="text-align:center;padding:24px 0 12px">
+      <div style="font-size:13px;color:#58A6FF;font-weight:600;
+                  letter-spacing:2px;margin-bottom:6px">
+        UNDERGRADUATE THESIS · BIOMEDICAL ENGINEERING · 2025
+      </div>
+      <div style="font-size:28px;font-weight:700;color:#E6EDF3;
+                  margin-bottom:8px">
+        🔬 PRETI Retinal Disease Detection
+      </div>
+      <div style="font-size:15px;color:#8B949E;margin-bottom:16px">
+        Multi-label detection of
+        <span style="color:#FF6B6B">DR</span> ·
+        <span style="color:#51CF66">Glaucoma</span> ·
+        <span style="color:#74C0FC">HR</span> ·
+        <span style="color:#FFA94D">RVO</span>
+        with AGPT Bandwidth-Efficient Transmission
+      </div>
+      <div style="display:flex;justify-content:center;
+                  gap:8px;flex-wrap:wrap;margin-bottom:8px">
+        <span style="background:#21262D;color:#51CF66;padding:4px 12px;
+                     border-radius:20px;font-size:12px;font-weight:600;
+                     border:1px solid #238636">✓ Macro AUC 0.9903</span>
+        <span style="background:#21262D;color:#74C0FC;padding:4px 12px;
+                     border-radius:20px;font-size:12px;font-weight:600;
+                     border:1px solid #1F6FEB">✓ 70.3% Bandwidth Saved</span>
+        <span style="background:#21262D;color:#FFA94D;padding:4px 12px;
+                     border-radius:20px;font-size:12px;font-weight:600;
+                     border:1px solid #9E6A03">✓ 4 Diseases Simultaneously</span>
+        <span style="background:#21262D;color:#FF6B6B;padding:4px 12px;
+                     border-radius:20px;font-size:12px;font-weight:600;
+                     border:1px solid #8B1A1A">✓ PRETI Foundation Model</span>
+      </div>
+    </div>
+    """)
+
+    with gr.Row():
+        with gr.Column(scale=1, min_width=280):
+            gr.HTML("""
+            <div style="background:#161B22;border:1px solid #30363D;
+                        border-radius:10px;padding:16px;margin-bottom:8px">
+              <div style="color:#58A6FF;font-size:11px;font-weight:600;
+                          letter-spacing:1px;margin-bottom:10px">
+                UPLOAD RETINAL IMAGE
+              </div>
+              <div style="color:#8B949E;font-size:12px;line-height:1.8">
+                • Any fundus photograph<br>
+                • JPEG or PNG format<br>
+                • Any resolution supported<br>
+                • Auto CLAHE preprocessing
+              </div>
+            </div>""")
+
+            inp = gr.Image(label="Retinal Fundus Image",
+                          type="pil", height=260)
+            btn = gr.Button("🔍  Analyze Retina",
+                           variant="primary", size="lg")
+
+            gr.HTML("""
+            <div style="background:#161B22;border:1px solid #30363D;
+                        border-radius:10px;padding:14px;margin-top:10px">
+              <div style="color:#58A6FF;font-size:11px;font-weight:600;
+                          letter-spacing:1px;margin-bottom:8px">
+                PIPELINE
+              </div>
+              <div style="color:#8B949E;font-size:11px;line-height:1.8">
+                ① CLAHE contrast enhancement<br>
+                ② PRETI ViT-B/16 encoding<br>
+                ③ 4-disease classification<br>
+                ④ RAAM attention extraction<br>
+                ⑤ AGPT top-30% patch select<br>
+                ⑥ 70.3% bandwidth reduction
+              </div>
+            </div>""")
+
+        with gr.Column(scale=3):
+            out_img = gr.Image(label="Analysis Result", height=520)
+
+    with gr.Row():
+        out_txt = gr.Textbox(label="Clinical Report",
+                            lines=22, max_lines=28,
+                            show_copy_button=True)
+
+    gr.HTML("""
+    <div style="text-align:center;padding:12px 0;
+                color:#484F58;font-size:11px">
+      PRETI: Lee et al., arXiv:2505.12233 (2025) ·
+      Focal Loss: Lin et al., TPAMI 2020 ·
+      Class-Balanced Sampler: Cui et al., CVPR 2019 ·
+      AGPT: Novel Contribution
+    </div>""")
+
+    btn.click(fn=analyze,
+              inputs=[inp],
+              outputs=[out_img, out_txt])
+
+if __name__ == "__main__":
+    demo.launch()

requirements.txt

@@ -0,0 +1,9 @@
+gradio>=4.0.0
+torch>=2.0.0
+torchvision>=0.15.0
+timm>=0.9.0
+opencv-python-headless>=4.8.0
+Pillow>=9.0.0
+numpy>=1.24.0
+matplotlib>=3.7.0
+huggingface_hub>=0.19.0