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AD-Stage-Net

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katelynhur commited on Sep 9

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README.md +14 -0
Results/BestSingles/desktop.ini +0 -0
Results/BestSingles/export_manifest.json +152 -0
Results/EnsembleEval/Combined/desktop.ini +0 -0
Results/EnsembleEval/Combined/ensemble_results.csv +253 -0
Results/EnsembleEval/desktop.ini +0 -0
Results/Model_Leaderboard/desktop.ini +0 -0
Results/Model_Leaderboard/leaderboard.csv +89 -0
app.py +459 -0
requirements.txt +6 -0

README.md ADDED Viewed

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+---
+title: AD-Stage-Net
+emoji: 📚
+colorFrom: blue
+colorTo: purple
+sdk: gradio
+sdk_version: 5.44.1
+app_file: app.py
+pinned: false
+license: apache-2.0
+short_description: Classifies 4 levels of AD from MRI
+---
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

Results/BestSingles/desktop.ini ADDED Viewed

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Results/BestSingles/export_manifest.json ADDED Viewed

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app.py ADDED Viewed

	@@ -0,0 +1,459 @@

+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+import os, json, re
+from pathlib import Path
+from typing import Dict, List
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from torchvision import models, transforms
+from torchvision.transforms import InterpolationMode
+from torchvision.transforms import functional as TF
+from PIL import Image
+import pandas as pd
+import gradio as gr
+# NEW: pull models from the Hub
+from huggingface_hub import hf_hub_download, list_repo_files
+# Optional: enable faster downloads on Spaces (uses aria2)
+os.environ.setdefault("HF_HUB_ENABLE_HF_TRANSFER", "1")
+MODEL_REPO = os.environ.get("MODEL_REPO", "katelynhur/AD-MRI-Classifier-Models")
+def warm_cache_all_pt_files():
+    try:
+        files = list_repo_files(MODEL_REPO)
+        pt_files = [f for f in files if f.lower().endswith(".pt")]
+        for f in pt_files:
+            try:
+                hf_hub_download(
+                    repo_id=MODEL_REPO,
+                    filename=f,
+                    local_dir="models_cache",
+                    local_dir_use_symlinks=False,
+                    local_files_only=False,
+                )
+                print(f"[warm] cached {f}")
+            except Exception as e:
+                print(f"[warm] skip {f}: {e}")
+    except Exception as e:
+        print(f"[warm] listing failed for {MODEL_REPO}: {e}")
+# call this once on startup
+warm_cache_all_pt_files()
+# ==============================
+# Project text (with links)
+# ==============================
+PROJECT_MD = r"""
+# 🧠 AD-Stage-Net: Four-Stage Alzheimer’s MRI Classification
+This demo runs strong single CNN backbones (and an optional 2-model ensemble) to classify brain MRI slices into four stages:
+- **No Impairment**
+- **Very Mild Impairment**
+- **Mild Impairment**
+- **Moderate Impairment**
+**Code:** <https://github.com/katelynhur/AD-Stage-Net>
+**Data sources:**
+- Kaggle (Luke Chugh): <https://www.kaggle.com/datasets/lchugh1/alzheimers-mri-dataset>
+- Kaggle (Marco Pinamonti): <https://www.kaggle.com/datasets/marcopeix/alzheimer-mri-dataset>
+- HuggingFace (Falah): <https://huggingface.co/datasets/fahadalhuzali/brain-MRI-ad-classification>
+> ⚠️ For research only; not a medical device.
+"""
+# ==============================
+# Config
+# ==============================
+# Class label order used during training
+CLASS_NAMES = ['Mild Impairment', 'Moderate Impairment', 'No Impairment', 'Very Mild Impairment']
+NUM_CLASSES = 4
+# Load checkpoints from this HF model repo
+MODEL_REPO = os.environ.get("MODEL_REPO", "katelynhur/AD-MRI-Classifier-Models")
+# Paths for performance tables (kept in the Space repo)
+LEADER_CSV    = Path("Results/Model_Leaderboard/leaderboard.csv")
+PAIR_COMBINED = Path("Results/EnsembleEval/Combined/ensemble_rankings.csv")
+PAIR_FALLBACK = Path("Results/EnsembleEval/Pairs_LukeTest_From_Luke/ensemble_results.csv")
+# Device
+DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+# ==============================
+# Transforms
+# ==============================
+def pad_to_square(img: Image.Image) -> Image.Image:
+    w, h = img.size
+    s = max(w, h)
+    pad_l = (s - w) // 2
+    pad_r = s - w - pad_l
+    pad_t = (s - h) // 2
+    pad_b = s - h - pad_t
+    return TF.pad(img, [pad_l, pad_t, pad_r, pad_b], fill=0)
+IMAGENET_MEAN = [0.485, 0.456, 0.406]
+IMAGENET_STD  = [0.229, 0.224, 0.225]
+def make_eval_tf(arch: str):
+    size = 299 if arch.lower().startswith("inception") else 224
+    return transforms.Compose([
+        transforms.Lambda(lambda im: im.convert("L").convert("RGB")),  # robust: grayscale -> 3ch
+        transforms.Lambda(pad_to_square),
+        transforms.Resize((size, size), interpolation=InterpolationMode.BICUBIC, antialias=True),
+        transforms.ToTensor(),
+        transforms.Normalize(IMAGENET_MEAN, IMAGENET_STD),
+    ])
+# ==============================
+# Model builders (match training)
+# ==============================
+def build_resnet(ctor, num):
+    m = ctor(weights=None)
+    in_dim = m.fc.in_features
+    m.fc = nn.Linear(in_dim, num)
+    return m
+def build_densenet(ctor, num):
+    m = ctor(weights=None)
+    in_dim = m.classifier.in_features
+    m.classifier = nn.Linear(in_dim, num)
+    return m
+def build_effnet(ctor, num):
+    m = ctor(weights=None)
+    in_dim = m.classifier[1].in_features
+    m.classifier[1] = nn.Linear(in_dim, num)
+    return m
+def build_mobilenet(ctor, num):
+    m = ctor(weights=None)
+    in_dim = m.classifier[-1].in_features
+    m.classifier[-1] = nn.Linear(in_dim, num)
+    return m
+def build_vgg(ctor, num):
+    m = ctor(weights=None)
+    in_dim = m.classifier[-1].in_features
+    m.classifier[-1] = nn.Linear(in_dim, num)
+    return m
+def build_inception(num_classes):
+    m = models.inception_v3(weights=None, aux_logits=True)
+    in_dim = m.fc.in_features
+    m.fc = nn.Linear(in_dim, num_classes)
+    return m
+class _SmallCNN(nn.Module):
+    def __init__(self, num_classes: int):
+        super().__init__()
+        self.features = nn.Sequential(
+            nn.Conv2d(3, 32, 3, padding=1), nn.ReLU(inplace=True), nn.MaxPool2d(2),
+            nn.Conv2d(32, 64, 3, padding=1), nn.ReLU(inplace=True), nn.MaxPool2d(2),
+            nn.Conv2d(64, 128, 3, padding=1), nn.ReLU(inplace=True), nn.AdaptiveAvgPool2d((1, 1))
+        )
+        self.classifier = nn.Linear(128, num_classes)
+    def forward(self, x):
+        z = self.features(x).view(x.size(0), -1)
+        return self.classifier(z)
+def _small_cnn(num_classes=NUM_CLASSES):
+    return _SmallCNN(num_classes)
+MODEL_BUILDERS: Dict[str, callable] = {
+    "CNN_Small":            lambda num: _small_cnn(num_classes=num),
+    "ResNet50":             lambda num: build_resnet(models.resnet50, num),
+    "ResNet101":            lambda num: build_resnet(models.resnet101, num),
+    "ResNet152":            lambda num: build_resnet(models.resnet152, num),
+    "DenseNet121":          lambda num: build_densenet(models.densenet121, num),
+    "DenseNet161":          lambda num: build_densenet(models.densenet161, num),
+    "DenseNet169":          lambda num: build_densenet(models.densenet169, num),
+    "DenseNet201":          lambda num: build_densenet(models.densenet201, num),
+    "EffNetB0":             lambda num: build_effnet(models.efficientnet_b0, num),
+    "MobileNetV2":          lambda num: build_mobilenet(models.mobilenet_v2, num),
+    "MobileNetV3_L":        lambda num: build_mobilenet(models.mobilenet_v3_large, num),
+    "ResNeXt50_32x4d":      lambda num: build_resnet(models.resnext50_32x4d, num),
+    "ResNeXt101_32x8d":     lambda num: build_resnet(models.resnext101_32x8d, num),
+    "VGG16":                lambda num: build_vgg(models.vgg16_bn, num),
+    "InceptionV3":          lambda num: build_inception(num),
+}
+# ==============================
+# Robust head adapter
+# (supports Linear vs Dropout+Linear heads)
+# ==============================
+def adapt_head_for_state_dict(model: nn.Module, arch: str, state: Dict, num_classes:int):
+    """If the checkpoint expects a Dropout+Linear head (keys like fc.1.weight or classifier.1.weight),
+    make sure the current model has the same structure."""
+    # ResNet/ResNeXt head
+    has_fc1 = any(k.startswith("fc.1.weight") for k in state.keys())
+    if hasattr(model, "fc"):
+        if has_fc1 and not isinstance(model.fc, nn.Sequential):
+            in_dim = model.fc.in_features
+            model.fc = nn.Sequential(nn.Dropout(0.0), nn.Linear(in_dim, num_classes))
+    # Common classifier head (DenseNet/MobileNet/VGG/EffNet)
+    has_clf1 = any(k.startswith("classifier.1.weight") for k in state.keys())
+    if hasattr(model, "classifier"):
+        if has_clf1 and not isinstance(model.classifier, nn.Sequential):
+            if isinstance(model.classifier, nn.Linear):
+                in_dim = model.classifier.in_features
+            elif isinstance(model.classifier, nn.Sequential):
+                in_dim = None
+            else:
+                in_dim = None
+            if in_dim is not None:
+                model.classifier = nn.Sequential(nn.Dropout(0.0), nn.Linear(in_dim, num_classes))
+    return model
+# ==============================
+# Hub checkpoint discovery & loading
+# ==============================
+def arch_from_filename(fname: str) -> str:
+    # Expect like "ResNet50_best.pt" -> "ResNet50"
+    base = Path(fname).stem
+    base = re.sub(r"_best$", "", base)
+    return base
+def list_available_checkpoints_from_hub(repo_id: str) -> Dict[str, str]:
+    """
+    Returns: {display_name (no _best): repo_filename}
+    """
+    files = list_repo_files(repo_id)
+    ckpts: Dict[str, str] = {}
+    for f in files:
+        if not f.lower().endswith(".pt"):
+            continue
+        name = Path(f).stem              # e.g., ResNet50_best
+        disp = re.sub(r"_best$", "", name)
+        # prefer *_best.pt if duplicates exist
+        if disp in ckpts:
+            if name.endswith("_best"):
+                ckpts[disp] = f
+        else:
+            ckpts[disp] = f
+    return ckpts
+_LOADED: Dict[str, nn.Module] = {}
+def load_model_from_hub(display_name: str, repo_filename: str) -> nn.Module:
+    """
+    display_name: e.g., "ResNet50"
+    repo_filename: path inside the repo, e.g., "ResNet50_best.pt"
+    """
+    # Cache key by repo file to avoid re-loading
+    key = f"{MODEL_REPO}::{repo_filename}"
+    if key in _LOADED:
+        return _LOADED[key]
+    # Download to cache
+    local_path = hf_hub_download(
+        repo_id=MODEL_REPO,
+        filename=repo_filename,
+        local_dir="models_cache",
+        local_dir_use_symlinks=False
+    )
+    arch = arch_from_filename(repo_filename)
+    if arch not in MODEL_BUILDERS:
+        raise RuntimeError(f"Unknown architecture inferred from filename: {arch}")
+    model = MODEL_BUILDERS[arch](NUM_CLASSES).to(DEVICE)
+    state = torch.load(local_path, map_location="cpu")
+    model = adapt_head_for_state_dict(model, arch, state, NUM_CLASSES)
+    model.load_state_dict(state, strict=True)
+    model.eval()
+    _LOADED[key] = model
+    return model
+# ==============================
+# Inference helpers
+# ==============================
+@torch.no_grad()
+def logits_for_model(model: nn.Module, arch: str, img: Image.Image) -> torch.Tensor:
+    tfm = make_eval_tf(arch)
+    xb = tfm(img).unsqueeze(0).to(DEVICE)
+    out = model(xb)
+    # Handle Inception aux tuple
+    if arch.lower().startswith("inception") and isinstance(out, tuple):
+        out = out[0]
+    return out  # [1, C]
+def ensemble_logits(logits_list: List[torch.Tensor]) -> torch.Tensor:
+    # Simple (unweighted) average of logits
+    return torch.mean(torch.stack(logits_list, dim=0), dim=0)
+def predict_files(files: List[str], model1_name: str, model2_name: str, ckpt_map_json: str) -> pd.DataFrame:
+    if not model1_name:
+        raise gr.Error("Please select Model 1.")
+    ckpt_map = json.loads(ckpt_map_json)  # {display_name: repo_filename}
+    # Load models from Hub
+    repo_file1 = ckpt_map[model1_name]
+    m1 = load_model_from_hub(model1_name, repo_file1)
+    arch1 = arch_from_filename(repo_file1)
+    m2, arch2 = None, None
+    if model2_name:
+        repo_file2 = ckpt_map[model2_name]
+        m2 = load_model_from_hub(model2_name, repo_file2)
+        arch2 = arch_from_filename(repo_file2)
+    rows = []
+    for f in files:
+        try:
+            img = Image.open(f)
+        except Exception:
+            # Gradio may pass dicts if drag-dropped—normalize to filepath string
+            if isinstance(f, dict) and "name" in f:
+                img = Image.open(f["name"])
+            else:
+                continue
+        # logits
+        L1 = logits_for_model(m1, arch1, img)         # [1, C]
+        L2 = logits_for_model(m2, arch2, img) if m2 else None
+        # softmax probs
+        P1 = F.softmax(L1, dim=1)[0].cpu().numpy()
+        if L2 is not None:
+            P2 = F.softmax(L2, dim=1)[0].cpu().numpy()
+            L_ens = ensemble_logits([L1, L2])
+            P_ens = F.softmax(L_ens, dim=1)[0].cpu().numpy()
+        else:
+            P2, P_ens = None, None
+        # top labels
+        top1 = CLASS_NAMES[int(P1.argmax())]
+        top2 = CLASS_NAMES[int(P2.argmax())] if P2 is not None else ""
+        topE = CLASS_NAMES[int(P_ens.argmax())] if P_ens is not None else ""
+        row = {
+            "filename": Path(f["name"] if isinstance(f, dict) else f).name,
+            "Model 1": model1_name,
+            **{f"M1:{cls}": float(P1[i]) for i, cls in enumerate(CLASS_NAMES)},
+            "M1:top": top1,
+        }
+        if P2 is not None:
+            row.update({f"M2:{cls}": float(P2[i]) for i, cls in enumerate(CLASS_NAMES)})
+            row["Model 2"] = model2_name
+            row["M2:top"] = top2
+            row.update({f"ENS:{cls}": float(P_ens[i]) for i, cls in enumerate(CLASS_NAMES)})
+            row["ENS:top"] = topE
+        rows.append(row)
+    return pd.DataFrame(rows)
+# ==============================
+# Metrics tables (right panel)
+# ==============================
+def load_best_singles_table(leader_csv: Path) -> pd.DataFrame:
+    if not leader_csv.exists():
+        return pd.DataFrame([{"info": "leaderboard.csv not found"}])
+    df = pd.read_csv(leader_csv)
+    if "source" in df.columns:
+        df = df[df["source"] == "single_arch"].copy()
+    sort_cols = [c for c in ["test_acc", "best_val_acc"] if c in df.columns]
+    if sort_cols:
+        df = df.sort_values(sort_cols, ascending=False)
+    if "arch" in df.columns:
+        df = df.drop_duplicates(subset=["arch"], keep="first")
+    keep = [c for c in ["arch","test_acc","best_val_acc","img_size","run_dir"] if c in df.columns]
+    return df[keep].reset_index(drop=True) if keep else df
+def load_best_pairs_table() -> pd.DataFrame:
+    # Prefer combined (avg/min) rankings if present
+    if PAIR_COMBINED.exists():
+        df = pd.read_csv(PAIR_COMBINED)
+        show = [c for c in ["members_names","avg_acc","min_acc","acc_LukeTest","acc_MarcoTest","acc_FalahTest"] if c in df.columns]
+        if show:
+            return df[show].head(20)
+        return df.head(20)
+    # else fallback to LukeTest pairs
+    if PAIR_FALLBACK.exists():
+        df = pd.read_csv(PAIR_FALLBACK)
+        show = [c for c in ["members_names","acc"] if c in df.columns]
+        return df[show].head(20) if show else df.head(20)
+    return pd.DataFrame([{"info": "No ensemble results CSV found"}])
+# ==============================
+# UI wiring
+# ==============================
+def update_model2_choices(model1, all_choices):
+    """Disable Model 2 until Model 1 is picked; exclude Model 1 from Model 2 list."""
+    if not model1:
+        return gr.update(choices=all_choices, value=None, interactive=False)
+    choices = [c for c in all_choices if c != model1]
+    return gr.update(choices=choices, value=None, interactive=True)
+def run_infer(files, model1, model2, ckpt_map_json):
+    if not files:
+        raise gr.Error("Please upload at least one image.")
+    df = predict_files(files, model1, model2, ckpt_map_json)
+    return df
+def build_app():
+    # Discover checkpoints from the Hub
+    ckpt_map = list_available_checkpoints_from_hub(MODEL_REPO)  # display_name -> repo_filename
+    model_choices = sorted(ckpt_map.keys())
+    ckpt_map_json = json.dumps(ckpt_map)
+    # Preload metrics tables
+    singles_tbl = load_best_singles_table(LEADER_CSV)
+    pairs_tbl   = load_best_pairs_table()
+    css = """
+    .gradio-container { background-color: #12192E !important; }
+    .gradio-container h1, .gradio-container p, .gradio-container .label, .gradio-container .gr-markdown, .gradio-container .gr-label-text {
+        color: white !important;
+    }
+    """
+    with gr.Blocks(theme=gr.themes.Soft(), css=css) as demo:
+        gr.Markdown(PROJECT_MD)
+        with gr.Row():
+            with gr.Column(scale=3):
+                with gr.Row():
+                    with gr.Column():
+                        m1 = gr.Dropdown(
+                            choices=model_choices,
+                            label="Model 1 (required)",
+                            value=None,
+                            allow_custom_value=False,
+                        )
+                        m2 = gr.Dropdown(
+                            choices=model_choices,
+                            label="Model 2 (optional, ensemble)",
+                            value=None,
+                            allow_custom_value=False,
+                            interactive=False,   # stays off until Model 1 chosen
+                        )
+                    with gr.Column():
+                        files = gr.Files(label="Upload one or more MRI images", file_count="multiple", type="filepath")
+                        run_btn = gr.Button("🔍 Run inference", variant="primary")
+                results = gr.Dataframe(label="Predictions", wrap=True, interactive=False)
+            with gr.Column(scale=2):
+                gr.Markdown("### 📊 Best Single-Model Performance (from `leaderboard.csv`)")
+                singles_df = gr.Dataframe(value=singles_tbl, interactive=False, wrap=True)
+                gr.Markdown("### 🤝 Top Pairwise Ensembles")
+                pairs_df   = gr.Dataframe(value=pairs_tbl,   interactive=False, wrap=True)
+        # wire events
+        m1.change(fn=update_model2_choices, inputs=[m1, gr.State(model_choices)], outputs=m2)
+        run_btn.click(fn=run_infer, inputs=[files, m1, m2, gr.State(ckpt_map_json)], outputs=results)
+    return demo
+# ========= Entry =========
+if __name__ == "__main__":
+    demo = build_app()
+    demo.launch()

requirements.txt ADDED Viewed

	@@ -0,0 +1,6 @@

+torch
+torchvision
+gradio
+Pillow
+huggingface_hub>=0.23
+hf_transfer