Update app.py

app.py

@@ -1,1360 +1,495 @@
 from __future__ import annotations
 
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 from datetime import datetime, timedelta
 
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 """Sundew Diabetes Commons – holistic, open Streamlit experience."""
 
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 import json
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 import logging
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 import math
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 import time
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 from dataclasses import dataclass
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 from typing import Any, Dict, List, Optional, Tuple
 
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 import numpy as np
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 import pandas as pd
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 import streamlit as st
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 from sklearn.linear_model import LogisticRegression
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 from sklearn.pipeline import Pipeline
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 from sklearn.preprocessing import StandardScaler
 
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 try:
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     from sundew import SundewAlgorithm  # type: ignore[attr-defined]
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     from sundew.config import SundewConfig
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     from sundew.config_presets import get_preset
 
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     _HAS_SUNDEW = True
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 except Exception:  # fallback when package is unavailable
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     SundewAlgorithm = None  # type: ignore
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     SundewConfig = object  # type: ignore
 
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     def get_preset(_: str) -> Any:  # type: ignore
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         return None
 
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     _HAS_SUNDEW = False
 
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 LOGGER = logging.getLogger("sundew.diabetes.commons")
 
 
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 @dataclass
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 class SundewGateConfig:
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     target_activation: float = 0.22
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     temperature: float = 0.08
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     mode: str = "tuned_v2"
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     use_native: bool = True
 
 
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 def _build_sundew_runtime(config: SundewGateConfig) -> Optional[SundewAlgorithm]:
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     if not (config.use_native and _HAS_SUNDEW and SundewAlgorithm is not None):
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         return None
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     try:
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         preset = get_preset(config.mode)
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     except Exception:
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         preset = SundewConfig()  # type: ignore
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     for attr, value in (
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         ("target_activation_rate", config.target_activation),
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         ("gate_temperature", config.temperature),
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     ):
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         try:
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             setattr(preset, attr, value)
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         except Exception:
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             pass
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     for constructor in (
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         lambda: SundewAlgorithm(preset),  # type: ignore[arg-type]
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         lambda: SundewAlgorithm(config=preset),  # type: ignore[arg-type]
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         lambda: SundewAlgorithm(),
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     ):
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         try:
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             return constructor()
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         except Exception:
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             continue
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     return None
 
 
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 class AdaptiveGate:
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     """Adapter that hides Sundew/Fallback branching."""
 
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     def __init__(self, config: SundewGateConfig) -> None:
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         self.config = config
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         self._ema = 0.0
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         self._tau = float(np.clip(config.target_activation, 0.05, 0.95))
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         self._alpha = 0.05
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         self.sundew: Optional[SundewAlgorithm] = _build_sundew_runtime(config)
 
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     def decide(self, score: float) -> bool:
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         if self.sundew is not None:
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             for attr in ("decide", "step", "open"):
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                 fn = getattr(self.sundew, attr, None)
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                 if callable(fn):
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                     try:
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                         return bool(fn(score))
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                     except Exception:
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                         continue
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         normalized = float(np.clip(score / 1.4, 0.0, 1.0))
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         temperature = max(self.config.temperature, 0.02)
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         probability = 1.0 / (1.0 + math.exp(-(normalized - self._tau) / temperature))
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         fired = bool(np.random.rand() < probability)
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-        self._ema = (1 - self._alpha) * self._ema + self._alpha * (
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-            1.0 if fired else 0.0
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-        )
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+        self._ema = (1 - self._alpha) * self._ema + self._alpha * (1.0 if fired else 0.0)
         self._tau += 0.05 * (self.config.target_activation - self._ema)
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         self._tau = float(np.clip(self._tau, 0.05, 0.95))
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         return fired
 
 
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 def load_example_dataset(n_rows: int = 720) -> pd.DataFrame:
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     rng = np.random.default_rng(17)
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     t0 = pd.Timestamp.utcnow().floor("5min") - pd.Timedelta(minutes=5 * n_rows)
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     timestamps = [t0 + pd.Timedelta(minutes=5 * i) for i in range(n_rows)]
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     base = 118 + 28 * np.sin(np.linspace(0, 7 * math.pi, n_rows))
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     noise = rng.normal(0, 12, n_rows)
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-    meals = (rng.random(n_rows) < 0.05).astype(float) * rng.normal(50, 18, n_rows).clip(
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-        0, 150
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-    )
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-    insulin = (rng.random(n_rows) < 0.03).astype(float) * rng.normal(
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-        4.2, 1.5, n_rows
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-    ).clip(0, 10)
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+    meals = (rng.random(n_rows) < 0.05).astype(float) * rng.normal(50, 18, n_rows).clip(0, 150)
+    insulin = (rng.random(n_rows) < 0.03).astype(float) * rng.normal(4.2, 1.5, n_rows).clip(0, 10)
     steps = rng.integers(0, 200, size=n_rows)
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     heart_rate = 68 + (steps > 90) * rng.integers(20, 45, size=n_rows)
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     sleep_flag = (rng.random(n_rows) < 0.12).astype(float)
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     stress_index = rng.uniform(0, 1, n_rows)
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     glucose = base + noise
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     for i in range(n_rows):
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         if i >= 6:
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             glucose[i] += 0.4 * meals[i - 6 : i].sum() / 6
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         if i >= 4:
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             glucose[i] -= 1.2 * insulin[i - 4 : i].sum() / 4
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         if steps[i] > 100:
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             glucose[i] -= 15
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     glucose[180:200] = rng.normal(62, 5, 20)
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     glucose[350:365] = rng.normal(210, 10, 15)
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     return pd.DataFrame(
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         {
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             "timestamp": timestamps,
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             "glucose_mgdl": np.round(np.clip(glucose, 40, 350), 1),
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             "carbs_g": np.round(meals, 1),
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             "insulin_units": np.round(insulin, 1),
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             "steps": steps.astype(int),
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             "hr": (heart_rate + rng.normal(0, 5, n_rows)).round().astype(int),
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             "sleep_flag": sleep_flag,
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             "stress_index": stress_index,
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         }
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     )
 
 
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 def compute_features(df: pd.DataFrame) -> pd.DataFrame:
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     df = df.copy().sort_values("timestamp").reset_index(drop=True)
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     df["timestamp"] = pd.to_datetime(df["timestamp"], utc=True)
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     df["glucose_prev"] = df["glucose_mgdl"].shift(1)
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-    dt = (
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-        df["timestamp"].astype("int64") - df["timestamp"].shift(1).astype("int64")
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-    ) / 60e9
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+    dt = (df["timestamp"].astype("int64") - df["timestamp"].shift(1).astype("int64")) / 60e9
     df["roc_mgdl_min"] = (df["glucose_mgdl"] - df["glucose_prev"]) / dt
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     df["roc_mgdl_min"] = df["roc_mgdl_min"].replace([np.inf, -np.inf], 0.0).fillna(0.0)
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     ema = df["glucose_mgdl"].ewm(span=48, adjust=False).mean()
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     df["deviation"] = (df["glucose_mgdl"] - ema).fillna(0.0)
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     df["iob_proxy"] = df["insulin_units"].rolling(12, min_periods=1).sum() / 12.0
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     df["cob_proxy"] = df["carbs_g"].rolling(12, min_periods=1).sum() / 12.0
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     df["variability"] = df["glucose_mgdl"].rolling(24, min_periods=2).std().fillna(0.0)
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     df["activity_factor"] = (df["steps"] / 200.0 + df["hr"] / 160.0).clip(0, 1)
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     df["sleep_flag"] = df["sleep_flag"].fillna(0.0) if "sleep_flag" in df else 0.0
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     df["stress_index"] = df["stress_index"].fillna(0.5) if "stress_index" in df else 0.5
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     return df[
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         [
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             "timestamp",
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             "glucose_mgdl",
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             "roc_mgdl_min",
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             "deviation",
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             "iob_proxy",
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             "cob_proxy",
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             "variability",
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             "activity_factor",
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             "sleep_flag",
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             "stress_index",
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         ]
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     ].copy()
 
 
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 def lightweight_score(row: pd.Series) -> float:
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     glucose = row["glucose_mgdl"]
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     roc = row["roc_mgdl_min"]
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     deviation = row["deviation"]
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     iob = row["iob_proxy"]
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     cob = row["cob_proxy"]
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     stress = row["stress_index"]
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     score = 0.0
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     score += max(0.0, (glucose - 180) / 80)
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     score += max(0.0, (70 - glucose) / 30)
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     score += abs(roc) / 6.0
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     score += abs(deviation) / 100.0
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     score += stress * 0.4
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     score += max(0.0, (cob - iob) * 0.04)
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     return float(np.clip(score, 0.0, 1.4))
 
 
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 def train_simple_model(df: pd.DataFrame):
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     features = df[
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         [
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             "glucose_mgdl",
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             "roc_mgdl_min",
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             "iob_proxy",
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             "cob_proxy",
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             "activity_factor",
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             "variability",
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         ]
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     ]
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     labels = (df["glucose_mgdl"] > 180).astype(int)
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     model = Pipeline(
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         [
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             ("scaler", StandardScaler()),
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             ("clf", LogisticRegression(max_iter=400, class_weight="balanced")),
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         ]
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     )
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     try:
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         model.fit(features, labels)
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         return model
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     except Exception:
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         return None
 
 
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 def render_overview(
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     results: pd.DataFrame,
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     alerts: List[Dict[str, Any]],
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     gate_config: SundewGateConfig,
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 ) -> None:
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     total = len(results)
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     activations = int(results["activated"].sum())
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     activation_rate = activations / max(total, 1)
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     energy_savings = max(0.0, 1.0 - activation_rate)
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     col_a, col_b, col_c, col_d = st.columns(4)
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     col_a.metric("Events", f"{total}")
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     col_b.metric("Heavy activations", f"{activations} ({activation_rate:.1%})")
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     col_c.metric("Estimated energy saved", f"{energy_savings:.1%}")
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     col_d.metric("Alerts", f"{len(alerts)}")
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     if gate_config.use_native and _HAS_SUNDEW:
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         st.caption(
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             "Energy savings follow 1 − activation rate. With native Sundew gating we target "
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             f"≈{gate_config.target_activation:.0%} activations, so savings approach "
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             f"{1 - gate_config.target_activation:.0%}."
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         )
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     else:
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         st.warning(
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             "Fallback gate active – heavy inference runs frequently, so savings mirror the observed activation rate."
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         )
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     with st.expander("Recent alerts", expanded=False):
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         if alerts:
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             st.table(pd.DataFrame(alerts).tail(10))
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         else:
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             st.info("No high-risk alerts in this window.")
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     st.area_chart(results.set_index("timestamp")["glucose_mgdl"], height=220)
 
 
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 def render_treatment_plan(medications: Dict[str, Any], next_visit: str) -> None:
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-        st.subheader("Full-cycle treatment support")
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+    st.subheader("Full-cycle treatment support")
     st.write(
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         "Upload or edit medication schedules, insulin titration guidance, and clinician notes."
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     )
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-        st.json(medications, expanded=False)
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-        st.caption(f"Next scheduled review: {next_visit}")
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+    st.json(medications, expanded=False)
+    st.caption(f"Next scheduled review: {next_visit}")
 
 
 def render_lifestyle_support(results: pd.DataFrame) -> None:
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     st.subheader("Lifestyle & wellbeing")
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     recent = results.tail(96).copy()
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     avg_glucose = recent["glucose_mgdl"].mean()
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     active_minutes = int((recent["activity_factor"] > 0.4).sum() * 5)
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     col1, col2 = st.columns(2)
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     col1.metric("Average glucose (8h)", f"{avg_glucose:.1f} mg/dL")
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     col2.metric("Active minutes", f"{active_minutes} min")
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     st.markdown(
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         """
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 - Aim for gentle movement every hour you are awake.
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 - Pair carbohydrates with protein/fiber to smooth spikes.
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 - Sleep flagged recently? Try 10-minute breathing before bed.
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 - Journal one gratitude moment—stress strongly shapes risk.
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 """
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     )
 
 
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 def render_community_actions() -> Dict[str, List[str]]:
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     st.subheader("Community impact")
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     st.write(
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         "Invite families, caregivers, and clinics to the commons. Set up alerts, shared logs, and outreach."
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     )
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     contact_list = [
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         "SMS: +233-200-000-111",
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         "WhatsApp: Care Circle Group",
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         "Clinic portal: sundew.health/community",
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     ]
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     st.table(pd.DataFrame({"Support channel": contact_list}))
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     return {
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         "Desired partners": ["Rural clinics", "Youth ambassadors", "Nutrition co-ops"],
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         "Needs": ["Smartphone grants", "Solar charging kits", "Translation volunteers"],
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     }
 
 
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 def render_telemetry(results: pd.DataFrame, telemetry: List[Dict[str, Any]]) -> None:
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     st.subheader("Telemetry & export")
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     st.write(
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         "Download event-level telemetry for validation, research, or regulatory reporting."
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     )
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-        st.caption(
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+    st.caption(
         "Energy savings are computed as 1 minus the observed activation rate. When the gate stays mostly open, savings naturally trend toward zero."
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     )
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     json_payload = json.dumps(telemetry, default=str, indent=2)
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     st.download_button(
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         label="Download telemetry (JSON)",
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         data=json_payload,
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         file_name="sundew_diabetes_telemetry.json",
+        mime="application/json",
+    )
+    st.dataframe(results.tail(100), use_container_width=True)
 
 
-        mime="application/json",
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-    )
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-    st.dataframe(results.tail(100), use_container_width=True)
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-def main() -> None:
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-    st.set_page_config(
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-        page_title="Sundew Diabetes Commons", layout="wide", page_icon="🕊"
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-    )
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-    st.title("Sundew Diabetes Commons")
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-        st.caption(
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+def main() -> None:
+    st.set_page_config(
+        page_title="Sundew Diabetes Commons", layout="wide", page_icon="🕊"
+    )
+    st.title("Sundew Diabetes Commons")
+    st.caption(
         "Open, compassionate diabetes care—monitoring, treatment, lifestyle, community."
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     )
 
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     st.sidebar.header("Load data")
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     uploaded = st.sidebar.file_uploader("CGM / diary CSV", type=["csv"])
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     use_example = st.sidebar.checkbox("Use synthetic example", True)
 
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     st.sidebar.header("Sundew configuration")
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     use_native = st.sidebar.checkbox(
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         "Use native Sundew gating",
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         value=_HAS_SUNDEW,
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         help="Disable to demo the lightweight fallback gate only.",
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     )
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     target_activation = st.sidebar.slider("Target activation", 0.05, 0.90, 0.22, 0.01)
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     temperature = st.sidebar.slider("Gate temperature", 0.02, 0.50, 0.08, 0.01)
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     mode = st.sidebar.selectbox(
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         "Preset", ["tuned_v2", "conservative", "aggressive", "auto_tuned"], index=0
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     )
 
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     if uploaded is not None:
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         df = pd.read_csv(uploaded)
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     elif use_example:
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         df = load_example_dataset()
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     else:
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         st.stop()
 
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     features = compute_features(df)
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     model = train_simple_model(features)
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     gate_config = SundewGateConfig(
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         target_activation=target_activation,
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         temperature=temperature,
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         mode=mode,
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         use_native=use_native,
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     )
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     gate = AdaptiveGate(gate_config)
 
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     telemetry: List[Dict[str, Any]] = []
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     records: List[Dict[str, Any]] = []
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     alerts: List[Dict[str, Any]] = []
 
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-
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     progress = st.progress(0)
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     status = st.empty()
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     for idx, row in enumerate(features.itertuples(index=False), start=1):
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         score = lightweight_score(pd.Series(row._asdict()))
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         should_run = gate.decide(score)
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         risk_proba = None
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         if should_run and model is not None:
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             sample = np.array(
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                 [
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                     [
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                         row.glucose_mgdl,
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                         row.roc_mgdl_min,
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                         row.iob_proxy,
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                         row.cob_proxy,
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                         row.activity_factor,
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                         row.variability,
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                     ]
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                 ]
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             )
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             try:
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                 risk_proba = float(model.predict_proba(sample)[0, 1])  # type: ignore[index]
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             except Exception:
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                 risk_proba = None
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         if risk_proba is not None and risk_proba >= 0.6:
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             alerts.append(
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                 {
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                     "timestamp": row.timestamp,
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                     "glucose": row.glucose_mgdl,
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                     "risk": risk_proba,
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                     "message": "Check CGM, hydrate, plan balanced snack/insulin",
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                 }
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             )
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         records.append(
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             {
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                 "timestamp": row.timestamp,
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                 "glucose_mgdl": row.glucose_mgdl,
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                 "roc_mgdl_min": row.roc_mgdl_min,
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                 "deviation": row.deviation,
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                 "iob_proxy": row.iob_proxy,
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                 "cob_proxy": row.cob_proxy,
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                 "variability": row.variability,
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                 "activity_factor": row.activity_factor,
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                 "score": score,
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                 "activated": should_run,
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                 "risk_proba": risk_proba,
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             }
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         )
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         telemetry.append(
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             {
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                 "timestamp": str(row.timestamp),
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                 "score": score,
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                 "activated": should_run,
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                 "risk_proba": risk_proba,
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             }
-
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         )
-
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         progress.progress(idx / len(features))
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         status.text(f"Processing event {idx}/{len(features)}")
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     progress.empty()
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     status.empty()
 
-
-
-
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-        results = pd.DataFrame(records)
-
-
-
-
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+    results = pd.DataFrame(records)
 
     tabs = st.tabs(["Overview", "Treatment", "Lifestyle", "Community", "Telemetry"])
+
     with tabs[0]:
-    render_overview(results, alerts, gate_config)
+        render_overview(results, alerts, gate_config)
+
     with tabs[1]:
-    st.subheader("Full-cycle treatment support")
-    default_plan = {
-        "Insulin": {
-            "Basal": "14u glargine at 21:00",
-            "Bolus": "1u per 10g carbs + correction 1u per 40 mg/dL over 140",
-        },
-        "Oral medications": {
-            "Metformin": "500mg breakfast + 500mg dinner",
-            "Empagliflozin": "10mg once daily (if eGFR > 45)",
-        },
-        "Monitoring": [
-            "CGM sensor change every 10 days",
-            "Morning fasted CGM calibration",
-            "Weekly telehealth coaching",
-            "Quarterly in-person clinician review",
-        ],
-        "Safety plan": [
-            "Carry glucose tabs + glucagon kit",
-            "Emergency contact: +233-200-000-888",
-        ],
-        "Lifestyle": [
-            "30 min brisk walk 5x/week",
-            "Bedtime snack if glucose < 110 mg/dL",
-            "Hydrate 2L water daily unless contraindicated",
-        ],
-    }
-    st.caption("Upload or edit schedules, medication titration guidance, and clinician notes.")
-    uploaded_plan = st.file_uploader(
-        "Optional plan JSON", type=["json"], key="plan_uploader"
-    )
-    plan_text = st.text_area(
-        "Edit plan JSON",
-        json.dumps(default_plan, indent=2),
-        height=240,
-    )
-    plan_data = default_plan
-    if uploaded_plan is not None:
-        try:
-            plan_data = json.load(uploaded_plan)
-        except Exception as exc:
-            st.error(f"Could not parse uploaded plan JSON: {exc}")
-            plan_data = default_plan
-    else:
-        try:
-            plan_data = json.loads(plan_text)
-        except Exception as exc:
-            st.warning(
-                f"Using default plan because text could not be parsed: {exc}"
-            )
-            plan_data = default_plan
-    next_visit = (datetime.utcnow() + timedelta(days=30)).strftime(
-        "%Y-%m-%d (telehealth)"
-    )
-    render_treatment_plan(plan_data, next_visit=next_visit)
+        st.subheader("Full-cycle treatment support")
+        default_plan = {
+            "Insulin": {
+                "Basal": "14u glargine at 21:00",
+                "Bolus": "1u per 10g carbs + correction 1u per 40 mg/dL over 140",
+            },
+            "Oral medications": {
+                "Metformin": "500mg breakfast + 500mg dinner",
+                "Empagliflozin": "10mg once daily (if eGFR > 45)",
+            },
+            "Monitoring": [
+                "CGM sensor change every 10 days",
+                "Morning fasted CGM calibration",
+                "Weekly telehealth coaching",
+                "Quarterly in-person clinician review",
+            ],
+            "Safety plan": [
+                "Carry glucose tabs + glucagon kit",
+                "Emergency contact: +233-200-000-888",
+            ],
+            "Lifestyle": [
+                "30 min brisk walk 5x/week",
+                "Bedtime snack if glucose < 110 mg/dL",
+                "Hydrate 2L water daily unless contraindicated",
+            ],
+        }
+        st.caption("Upload or edit schedules, medication titration guidance, and clinician notes.")
+        uploaded_plan = st.file_uploader(
+            "Optional plan JSON", type=["json"], key="plan_uploader"
+        )
+        plan_text = st.text_area(
+            "Edit plan JSON",
+            json.dumps(default_plan, indent=2),
+            height=240,
+        )
+        plan_data = default_plan
+        if uploaded_plan is not None:
+            try:
+                plan_data = json.load(uploaded_plan)
+            except Exception as exc:
+                st.error(f"Could not parse uploaded plan JSON: {exc}")
+                plan_data = default_plan
+        else:
+            try:
+                plan_data = json.loads(plan_text)
+            except Exception as exc:
+                st.warning(
+                    f"Using default plan because text could not be parsed: {exc}"
+                )
+                plan_data = default_plan
+        next_visit = (datetime.utcnow() + timedelta(days=30)).strftime(
+            "%Y-%m-%d (telehealth)"
+        )
+        render_treatment_plan(plan_data, next_visit=next_visit)
+
     with tabs[2]:
-    render_lifestyle_support(results)
+        render_lifestyle_support(results)
+
     with tabs[3]:
-    community_items = render_community_actions()
-    st.json(community_items, expanded=False)
-    with tabs[4]:
-    render_telemetry(results, telemetry)
-    st.sidebar.markdown("---")
+        community_items = render_community_actions()
+        st.json(community_items, expanded=False)
 
+    with tabs[4]:
+        render_telemetry(results, telemetry)
 
+    st.sidebar.markdown("---")
     status_text = (
-
-
         "native gating"
-
-
         if gate_config.use_native and gate.sundew is not None
-
-
         else "fallback gate"
-
-
     )
-
-
     st.sidebar.caption(f"Sundew status: {status_text}")
 
 
-
-
-
-
-
-
 if __name__ == "__main__":
-
-
     main()