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README.md

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----
-title: SundewAIHealth
-emoji: 🐢
-colorFrom: purple
-colorTo: pink
-sdk: gradio
-sdk_version: 6.0.2
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# Sundew Health Backend
+
+Neurosymbolic, energy-aware ECG monitoring backend using FastAPI, PyTorch, and PostgreSQL.
+
+## Quickstart
+
+1. Create a `.env` from `.env.example` and set `DATABASE_URL`.
+2. Install dependencies (CPU PyTorch wheels): `pip install -e . --extra-index-url https://download.pytorch.org/whl/cpu`.
+3. Run the API: `uvicorn app.main:app --reload`.
+
+## Database (PostgreSQL + Alembic)
+
+- Set `DATABASE_URL` to your Postgres DSN (e.g., `postgresql+asyncpg://user:pass@localhost:5432/sundew_health`).
+- Run migrations: `alembic upgrade head`.
+- Create new migrations: `alembic revision -m "message" --autogenerate`.
+
+## Tests
+
+Run `pytest` to execute the test suite.
+
+## Notes
+
+- Gating uses `sundew-algorithms` (significance + hysteresis) ahead of model inference.
+- Adaptive Sparse Training (`adaptive-sparse-training`) is installed; torch shims are applied to load it, but training still uses the simpler loop until AST wiring is added.

app_space.py

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+import io
+import json
+from typing import Any, Dict, List
+
+import gradio as gr
+import matplotlib.pyplot as plt
+
+from app.ml.gating import gate_signal
+from app.ml.inference import infer_ecg, load_model
+from app.rules.engine import evaluate_ecg_rules
+
+
+# Preload model (uses ./checkpoints/ecg_classifier.pt if present)
+load_model()
+
+
+def parse_signal(text: str | List[float]) -> List[float]:
+    if isinstance(text, list):
+        return [float(x) for x in text]
+    try:
+        return [float(x) for x in json.loads(text)]
+    except Exception:
+        raise gr.Error("Provide ECG samples as a JSON list, e.g., [0.1, 0.2, 0.3]")
+
+
+def run_infer(signal_text: str) -> Dict[str, Any]:
+    sig = parse_signal(signal_text)
+    gated, gating_meta = gate_signal(sig, return_windows=True)
+    model_output: Dict[str, Any] = infer_ecg(
+        gated,
+        original_len=len(sig),
+        gating_meta=gating_meta,
+    )
+    patient_context = {"patient_id": "demo"}
+    rules_result = evaluate_ecg_rules(patient_context, model_output)
+    explanations = [*(model_output.get("gating", {}).get("explanations", []) if isinstance(model_output.get("gating"), dict) else []),
+                    *rules_result.get("explanations", [])]
+    return {
+        "label": model_output.get("label"),
+        "score": round(float(model_output.get("score", 0.0)), 3),
+        "hr": model_output.get("hr"),
+        "alert_level": rules_result.get("alert_level", "none"),
+        "gated_ratio": round(model_output.get("gated_ratio", 1.0), 3),
+        "gating": gating_meta,
+        "explanations": explanations,
+    }
+
+
+def plot_gating(signal_text: str):
+    sig = parse_signal(signal_text)
+    gated, meta = gate_signal(sig, return_windows=True)
+    fig, axes = plt.subplots(2, 1, figsize=(6, 4))
+    axes[0].plot(sig, color="#0066ff", linewidth=1)
+    axes[0].set_title("Raw signal")
+    axes[1].plot(gated, color="#ff6600", linewidth=1)
+    axes[1].set_title(f"Gated signal (ratio={meta['ratio']:.2f})")
+    fig.tight_layout()
+    buf = io.BytesIO()
+    fig.savefig(buf, format="png", dpi=120)
+    plt.close(fig)
+    buf.seek(0)
+    return buf
+
+
+demo_normal = [0.05 for _ in range(256)]
+demo_afib = [0.3 for _ in range(256)]
+
+with gr.Blocks(title="Sundew ECG Demo") as demo:
+    gr.Markdown("### Neurosymbolic ECG • Sundew Gating + Rules")
+    with gr.Tabs():
+        with gr.Tab("Upload/Infer"):
+            inp = gr.Textbox(
+                label="ECG samples (JSON list)",
+                value=json.dumps(demo_afib[:128]),
+            )
+            out = gr.JSON(label="Inference")
+            btn = gr.Button("Run")
+            btn.click(run_infer, inputs=inp, outputs=out)
+        with gr.Tab("Gating Preview"):
+            inp2 = gr.Textbox(
+                label="ECG samples (JSON list)",
+                value=json.dumps(demo_afib[:128]),
+            )
+            img = gr.Image(type="filepath", label="Raw vs Gated")
+            btn2 = gr.Button("Show gating")
+            btn2.click(plot_gating, inputs=inp2, outputs=img)
+        with gr.Tab("Demos"):
+            out_demo = gr.JSON()
+            btn_n = gr.Button("Normal")
+            btn_a = gr.Button("Arrhythmia-ish")
+            hidden_n = gr.Textbox(value=json.dumps(demo_normal), visible=False)
+            hidden_a = gr.Textbox(value=json.dumps(demo_afib), visible=False)
+            btn_n.click(run_infer, inputs=hidden_n, outputs=out_demo)
+            btn_a.click(run_infer, inputs=hidden_a, outputs=out_demo)
+
+
+if __name__ == "__main__":
+    demo.launch()

requirements.txt

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+gradio>=4.44
+torch==2.2.2+cpu
+torchvision==0.17.2+cpu
+--extra-index-url https://download.pytorch.org/whl/cpu
+pandas
+tqdm
+sundew-algorithms
+adaptive-sparse-training
+matplotlib