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Dockerfile

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+FROM python:3.11-slim
+
+RUN apt-get update \
+    && apt-get install -y --no-install-recommends gcc libgomp1 \
+    && rm -rf /var/lib/apt/lists/*
+
+WORKDIR /app
+COPY . .
+RUN pip install --upgrade pip
+RUN pip install -r requirements.txt
+
+EXPOSE 7860
+CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "7860"]

README.md

@@ -1,10 +1,23 @@
----
-title: NeuroHealth
-emoji: 🏃
-colorFrom: blue
-colorTo: yellow
-sdk: docker
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# NeuroHealth Alzheimer MRI Space
+
+This space exposes Alzheimer's MRI models saved in `backend/saved_models`.
+
+## Endpoints
+
+- `GET /health` — model loading status
+- `GET /models` — available Alzheimer's MRI models
+- `POST /predict/image?model_key=<key>` — MRI image prediction
+
+## Supported model keys
+
+- `ad_dn121`
+- `ad_dn169`
+- `ad_dn201`
+- `ad_homogeneous`
+
+## Run locally with Docker
+
+```bash
+docker build -t neurohealth-ad-mri .
+docker run --rm -p 7860:7860 neurohealth-ad-mri
+```

app.py

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+import sys
+from pathlib import Path
+
+REPO_ROOT = Path(__file__).resolve().parent.parent
+BACKEND_PATH = str(REPO_ROOT / "backend")
+if BACKEND_PATH not in sys.path:
+    sys.path.insert(0, BACKEND_PATH)
+
+from fastapi import FastAPI, UploadFile, File, HTTPException, Query
+from fastapi.middleware.cors import CORSMiddleware
+from app.models.model_manager import get_model_manager, MODEL_CONFIGS, ENSEMBLE_CONFIGS
+
+app = FastAPI(
+    title="NeuroHealth Alzheimer MRI Space",
+    version="1.0.0",
+    description="FastAPI wrapper for Alzheimer's MRI models stored in backend/saved_models.",
+)
+
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+model_manager = get_model_manager()
+AD_KEYS = {"ad_dn121", "ad_dn169", "ad_dn201", "ad_homogeneous"}
+
+
+@app.get("/")
+async def root():
+    return {
+        "message": "NeuroHealth Alzheimer MRI Space",
+        "supported_endpoints": ["/health", "/models", "/predict/image"],
+    }
+
+
+@app.get("/health")
+async def health():
+    statuses = model_manager.get_model_status()
+    return {
+        "status": "ok",
+        "model_status": {k: v for k, v in statuses.items() if k in AD_KEYS},
+        "available_models": [m["key"] for m in model_manager.get_available_models("alzheimers")],
+    }
+
+
+@app.get("/models")
+async def get_models():
+    models = model_manager.get_available_models("alzheimers")
+    models.append({
+        "key": "ad_homogeneous",
+        "name": "Alzheimer's MRI Homogeneous Ensemble (DenseNet 121+169+201)",
+        "condition": "alzheimers",
+        "imaging_type": "mri",
+        "type": "ensemble",
+    })
+    return {"models": models}
+
+
+@app.post("/predict/image")
+async def predict_image(
+    model_key: str = Query(..., description="Model key such as ad_dn121 or ad_homogeneous"),
+    file: UploadFile = File(...),
+):
+    if model_key not in AD_KEYS:
+        raise HTTPException(status_code=400, detail=f"Unknown Alzheimer MRI model key: {model_key}")
+
+    image_bytes = await file.read()
+    filename = file.filename or ""
+    config = MODEL_CONFIGS.get(model_key, {})
+
+    if model_key == "ad_homogeneous":
+        result = model_manager.predict_ensemble(model_key, image_bytes, filename)
+    else:
+        result = model_manager.predict_image(model_key, image_bytes, filename)
+
+    if "error" in result:
+        raise HTTPException(status_code=400, detail=result["error"])
+
+    return result

requirements.txt

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+fastapi
+uvicorn[standard]
+torch
+torchvision
+pillow
+numpy
+pydantic