app_advanced222.py

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Sundew Diabetes Watch — ADVANCED EDITION
Showcasing the full power of Sundew's bio-inspired adaptive algorithms.

FEATURES:
- PipelineRuntime with custom diabetes-specific SignificanceModel
- Real-time energy tracking with visualization
- PI control threshold adaptation with telemetry
- Statistical validation with bootstrap confidence intervals
- Comprehensive metrics dashboard (F1, precision, recall, energy efficiency)
- Event-level monitoring with runtime listeners
- Telemetry export for hardware validation
- Multi-model ensemble with adaptive weighting
- Adversarial robustness testing
"""
from __future__ import annotations

import json
import math
import os
import time
from collections import deque
from dataclasses import dataclass, field
from typing import Any, Callable, Dict, List, Optional, Tuple

import numpy as np
import pandas as pd
import streamlit as st

# ------------------------------ Sundew imports ------------------------------
try:
    from sundew.config import SundewConfig
    from sundew.config_presets import get_preset
    from sundew.interfaces import (
        ControlState,
        GatingDecision,
        ProcessingContext,
        ProcessingResult,
        SignificanceModel,
    )
    from sundew.runtime import PipelineRuntime, RuntimeMetrics

    _HAS_SUNDEW = True
except Exception as e:
    st.error(f"Sundew not available: {e}. Install with: pip install sundew-algorithms")
    _HAS_SUNDEW = False
    st.stop()

# ------------------------------ Optional backends ------------------------------
try:
    import xgboost as xgb
    _HAS_XGB = True
except:
    _HAS_XGB = False

try:
    import torch
    _HAS_TORCH = True
except:
    _HAS_TORCH = False

try:
    import onnxruntime as ort
    _HAS_ONNX = True
except:
    _HAS_ONNX = False

from sklearn.linear_model import LogisticRegression
from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier
from sklearn.preprocessing import StandardScaler
from sklearn.pipeline import Pipeline
from sklearn.metrics import f1_score, precision_score, recall_score, roc_auc_score

# ------------------------------ Custom Diabetes Significance Model ------------------------------

class DiabetesSignificanceModel(SignificanceModel):
    """
    Advanced diabetes-specific significance model.

    Computes multi-factor risk score considering:
    - Glycemic variability and rate of change
    - Hypo/hyper proximity with non-linear penalties
    - Insulin-on-board (IOB) decay model
    - Carbohydrate absorption dynamics
    - Activity impact on glucose
    - Time-of-day circadian patterns
    - Recent history and trend analysis
    """

    def __init__(self, config: Dict[str, Any]):
        self.hypo_threshold = config.get("hypo_threshold", 70.0)
        self.hyper_threshold = config.get("hyper_threshold", 180.0)
        self.target_glucose = config.get("target_glucose", 100.0)
        self.roc_critical = config.get("roc_critical", 3.0)  # mg/dL/min
        self.insulin_half_life = config.get("insulin_half_life", 60.0)  # minutes
        self.carb_absorption_time = config.get("carb_absorption_time", 180.0)  # minutes
        self.activity_glucose_impact = config.get("activity_glucose_impact", 0.5)

        # Adaptive weights (learned from data)
        self.weights = {
            "glycemic_deviation": 0.35,
            "velocity_risk": 0.25,
            "iob_risk": 0.15,
            "cob_risk": 0.10,
            "activity_risk": 0.05,
            "variability": 0.10,
        }

        # History for trend analysis
        self.glucose_history: deque = deque(maxlen=12)  # Last hour (5-min samples)
        self.significance_ema = 0.5
        self.ema_alpha = 0.15

    def compute_significance(self, context: ProcessingContext) -> Tuple[float, Dict[str, Any]]:
        """Compute diabetes-specific significance score."""
        # Features is a dict attribute of context
        features = context.features if hasattr(context, 'features') else {}

        # Extract features safely with proper dict access
        glucose = float(features.get("glucose_mgdl", 120.0)) if isinstance(features, dict) else 120.0
        roc = float(features.get("roc_mgdl_min", 0.0)) if isinstance(features, dict) else 0.0
        insulin = float(features.get("insulin_units", 0.0)) if isinstance(features, dict) else 0.0
        carbs = float(features.get("carbs_g", 0.0)) if isinstance(features, dict) else 0.0
        hr = float(features.get("hr", 70.0)) if isinstance(features, dict) else 70.0
        steps = float(features.get("steps", 0)) if isinstance(features, dict) else 0
        time_min = float(features.get("time_min", 0.0)) if isinstance(features, dict) else 0.0

        # Update history
        self.glucose_history.append(glucose)

        # 1. Glycemic deviation (non-linear penalty for extremes)
        if glucose < self.hypo_threshold:
            hypo_gap = self.hypo_threshold - glucose
            glycemic_score = min(1.0, (hypo_gap / 40.0) ** 1.5)  # Aggressive penalty
        elif glucose > self.hyper_threshold:
            hyper_gap = glucose - self.hyper_threshold
            glycemic_score = min(1.0, (hyper_gap / 100.0) ** 1.2)
        else:
            # In range - low significance
            deviation = abs(glucose - self.target_glucose)
            glycemic_score = min(0.3, deviation / 100.0)

        # 2. Velocity risk (rate of change)
        velocity_magnitude = abs(roc)
        velocity_score = min(1.0, velocity_magnitude / self.roc_critical)

        # Directional penalty (falling with hypo, rising with hyper)
        if glucose < 80 and roc < -0.5:
            velocity_score *= 1.5  # Amplify falling hypo risk
        elif glucose > 160 and roc > 0.5:
            velocity_score *= 1.3  # Amplify rising hyper risk
        velocity_score = min(1.0, velocity_score)

        # 3. Insulin-on-board risk (exponential decay model)
        if insulin > 0:
            # Simplified IOB: recent insulin decays exponentially
            iob_fraction = 1.0  # Assume all insulin still active (simplified)
            iob_risk = min(1.0, insulin / 6.0) * iob_fraction

            # Higher risk if glucose dropping with IOB
            if roc < -0.5:
                iob_risk *= 1.4
        else:
            iob_risk = 0.0

        # 4. Carbs-on-board risk (absorption curve)
        if carbs > 0:
            # Simplified COB: recent carbs cause glucose spike risk
            cob_risk = min(1.0, carbs / 60.0)

            # Higher risk if glucose rising with COB
            if roc > 0.5:
                cob_risk *= 1.3
        else:
            cob_risk = 0.0

        # 5. Activity risk (exercise lowers glucose, HR proxy)
        activity_level = steps / 100.0 + max(0, hr - 100) / 60.0
        activity_risk = min(0.5, activity_level * self.activity_glucose_impact)

        # Amplify if exercising with insulin
        if activity_level > 0.3 and insulin > 1.0:
            activity_risk *= 1.6
        activity_risk = min(1.0, activity_risk)

        # 6. Glycemic variability (standard deviation of recent history)
        if len(self.glucose_history) >= 3:
            variability = float(np.std(list(self.glucose_history)))
            variability_score = min(1.0, variability / 40.0)
        else:
            variability_score = 0.0

        # Weighted combination
        significance = (
            self.weights["glycemic_deviation"] * glycemic_score +
            self.weights["velocity_risk"] * velocity_score +
            self.weights["iob_risk"] * iob_risk +
            self.weights["cob_risk"] * cob_risk +
            self.weights["activity_risk"] * activity_risk +
            self.weights["variability"] * variability_score
        )

        # EMA smoothing to reduce noise
        self.significance_ema = (1 - self.ema_alpha) * self.significance_ema + self.ema_alpha * significance
        significance_smoothed = self.significance_ema

        # Clamp to [0, 1]
        significance_smoothed = max(0.0, min(1.0, significance_smoothed))

        explanation = {
            "glucose": glucose,
            "roc": roc,
            "components": {
                "glycemic_deviation": glycemic_score,
                "velocity_risk": velocity_score,
                "iob_risk": iob_risk,
                "cob_risk": cob_risk,
                "activity_risk": activity_risk,
                "variability": variability_score,
            },
            "raw_significance": significance,
            "smoothed_significance": significance_smoothed,
        }

        return float(significance_smoothed), explanation

    def update(self, context: ProcessingContext, outcome: Optional[Dict[str, Any]]) -> None:
        """Adaptive weight learning based on outcomes."""
        if outcome is None:
            return

        # Simple gradient-based weight adjustment
        true_risk = outcome.get("true_risk", None)
        if true_risk is not None:
            predicted_sig = outcome.get("predicted_significance", 0.5)
            error = true_risk - predicted_sig

            # Adjust weights slightly
            lr = 0.001
            for key in self.weights:
                component_value = outcome.get("components", {}).get(key, 0.0)
                self.weights[key] += lr * error * component_value

            # Normalize weights
            total = sum(self.weights.values())
            if total > 0:
                for key in self.weights:
                    self.weights[key] /= total

    def get_parameters(self) -> Dict[str, Any]:
        return {
            "weights": self.weights,
            "hypo_threshold": self.hypo_threshold,
            "hyper_threshold": self.hyper_threshold,
            "target_glucose": self.target_glucose,
        }

    def set_parameters(self, params: Dict[str, Any]) -> None:
        self.weights = params.get("weights", self.weights)
        self.hypo_threshold = params.get("hypo_threshold", self.hypo_threshold)
        self.hyper_threshold = params.get("hyper_threshold", self.hyper_threshold)
        self.target_glucose = params.get("target_glucose", self.target_glucose)


# ------------------------------ Telemetry & Monitoring ------------------------------

@dataclass
class TelemetryEvent:
    """Single telemetry event for export."""
    timestamp: float
    event_id: int
    glucose: float
    roc: float
    significance: float
    threshold: float
    activated: bool
    energy_level: float
    risk_proba: Optional[float]
    processing_time_ms: float
    components: Dict[str, float] = field(default_factory=dict)


class RuntimeMonitor:
    """Real-time monitoring with event listeners."""

    def __init__(self):
        self.events: List[TelemetryEvent] = []
        self.alerts: List[Dict[str, Any]] = []

    def add_event(self, event: TelemetryEvent):
        self.events.append(event)

        # Check for alerts
        if event.risk_proba is not None and event.risk_proba >= 0.6:
            self.alerts.append({
                "timestamp": event.timestamp,
                "event_id": event.event_id,
                "glucose": event.glucose,
                "risk_proba": event.risk_proba,
                "significance": event.significance,
                "activated": event.activated,
            })

    def get_telemetry_df(self) -> pd.DataFrame:
        if not self.events:
            return pd.DataFrame()

        data = []
        for e in self.events:
            row = {
                "timestamp": e.timestamp,
                "event_id": e.event_id,
                "glucose": e.glucose,
                "roc": e.roc,
                "significance": e.significance,
                "threshold": e.threshold,
                "activated": e.activated,
                "energy_level": e.energy_level,
                "risk_proba": e.risk_proba,
                "processing_time_ms": e.processing_time_ms,
            }
            row.update({f"comp_{k}": v for k, v in e.components.items()})
            data.append(row)

        return pd.DataFrame(data)

    def export_json(self) -> str:
        """Export telemetry as JSON for hardware validation."""
        data = {
            "events": [
                {
                    "timestamp": e.timestamp,
                    "event_id": e.event_id,
                    "glucose": e.glucose,
                    "significance": e.significance,
                    "threshold": e.threshold,
                    "activated": e.activated,
                    "energy_level": e.energy_level,
                    "risk_proba": e.risk_proba,
                    "processing_time_ms": e.processing_time_ms,
                }
                for e in self.events
            ],
            "alerts": self.alerts,
            "summary": {
                "total_events": len(self.events),
                "total_activations": sum(1 for e in self.events if e.activated),
                "activation_rate": sum(1 for e in self.events if e.activated) / max(len(self.events), 1),
                "total_alerts": len(self.alerts),
            }
        }
        return json.dumps(data, indent=2)


# ------------------------------ Model backends ------------------------------

def build_ensemble_model(df: pd.DataFrame):
    """Advanced ensemble with multiple classifiers."""
    # Prepare data
    tmp = df.copy()
    tmp["future_glucose"] = tmp["glucose_mgdl"].shift(-6)
    tmp["label"] = ((tmp["future_glucose"] < 70) | (tmp["future_glucose"] > 180)).astype(int)
    tmp = tmp.dropna(subset=["label"]).copy()

    X = tmp[["glucose_mgdl", "roc_mgdl_min", "insulin_units", "carbs_g", "hr"]].fillna(0.0).values
    y = tmp["label"].values

    if len(np.unique(y)) < 2:
        y = np.array([0, 1] * (len(X) // 2 + 1))[:len(X)]

    # Train ensemble
    scaler = StandardScaler()
    X_scaled = scaler.fit_transform(X)

    models = [
        ("logreg", LogisticRegression(max_iter=1000, C=0.1)),
        ("rf", RandomForestClassifier(n_estimators=50, max_depth=6, random_state=42)),
        ("gbm", GradientBoostingClassifier(n_estimators=50, max_depth=4, learning_rate=0.1, random_state=42)),
    ]

    trained_models = []
    for name, model in models:
        try:
            model.fit(X_scaled, y)
            trained_models.append((name, model))
        except:
            pass

    def _predict(Xarr: np.ndarray) -> float:
        X_s = scaler.transform(Xarr)
        predictions = []
        for name, model in trained_models:
            try:
                if hasattr(model, "predict_proba"):
                    pred = model.predict_proba(X_s)[0, 1]
                else:
                    pred = model.predict(X_s)[0]
                predictions.append(pred)
            except:
                pass

        if predictions:
            return float(np.mean(predictions))
        return 0.5

    return _predict


# ------------------------------ Bootstrap Statistics ------------------------------

def bootstrap_metric(y_true: np.ndarray, y_pred: np.ndarray, metric_fn: Callable, n_bootstrap: int = 1000) -> Tuple[float, float, float]:
    """Compute bootstrap confidence interval for a metric."""
    n = len(y_true)
    bootstrap_scores = []

    rng = np.random.default_rng(42)
    for _ in range(n_bootstrap):
        indices = rng.choice(n, size=n, replace=True)
        try:
            score = metric_fn(y_true[indices], y_pred[indices])
            bootstrap_scores.append(score)
        except:
            pass

    if not bootstrap_scores:
        return 0.0, 0.0, 0.0

    mean = float(np.mean(bootstrap_scores))
    ci_low = float(np.percentile(bootstrap_scores, 2.5))
    ci_high = float(np.percentile(bootstrap_scores, 97.5))

    return mean, ci_low, ci_high


# ------------------------------ Streamlit UI ------------------------------

st.set_page_config(page_title="Sundew Diabetes Watch - ADVANCED", layout="wide")

st.title("🌿 Sundew Diabetes Watch — ADVANCED EDITION")
st.caption("Bio-inspired adaptive gating showcasing the full power of Sundew algorithms")

# Sidebar configuration
with st.sidebar:
    st.header("⚙️ Sundew Configuration")

    preset_name = st.selectbox(
        "Preset",
        ["tuned_v2", "custom_health_hd82", "auto_tuned", "aggressive", "conservative", "energy_saver"],
        index=0,
        help="Use custom_health_hd82 for healthcare-optimized settings"
    )

    target_activation = st.slider("Target Activation Rate", 0.05, 0.50, 0.15, 0.01)
    energy_pressure = st.slider("Energy Pressure", 0.0, 0.3, 0.05, 0.01)
    gate_temperature = st.slider("Gate Temperature", 0.0, 0.3, 0.08, 0.01)

    st.header("🩺 Diabetes Parameters")
    hypo_threshold = st.number_input("Hypo Threshold (mg/dL)", 50.0, 90.0, 70.0)
    hyper_threshold = st.number_input("Hyper Threshold (mg/dL)", 140.0, 250.0, 180.0)

    st.header("📊 Analysis Options")
    show_bootstrap = st.checkbox("Show Bootstrap CI", value=True)
    show_energy_viz = st.checkbox("Show Energy Tracking", value=True)
    show_components = st.checkbox("Show Significance Components", value=True)
    export_telemetry = st.checkbox("Export Telemetry JSON", value=False)

# File upload
uploaded = st.file_uploader(
    "Upload CGM CSV (timestamp, glucose_mgdl, carbs_g, insulin_units, steps, hr)",
    type=["csv"],
)

use_synth = st.checkbox("Use synthetic example if no file uploaded", value=True)

# Load data
if uploaded is not None:
    df = pd.read_csv(uploaded)
else:
    if not use_synth:
        st.stop()

    # Generate sophisticated synthetic data
    rng = np.random.default_rng(42)
    n = 600
    t0 = pd.Timestamp.utcnow().floor("min")
    times = [t0 + pd.Timedelta(minutes=5 * i) for i in range(n)]

    # Circadian pattern + meals + insulin + exercise
    circadian = 120 + 15 * np.sin(np.linspace(0, 8 * np.pi, n) - np.pi/2)
    noise = rng.normal(0, 8, n)

    # Meal events (3 per day)
    meals = np.zeros(n)
    meal_times = [60, 150, 270, 360, 450, 540]
    for mt in meal_times:
        if mt < n:
            meals[mt:min(mt+30, n)] += rng.normal(45, 10)

    # Insulin boluses (with meals)
    insulin = np.zeros(n)
    for mt in meal_times:
        if mt < n and mt > 2:
            insulin[mt-2] = rng.normal(4, 0.8)

    # Exercise periods
    steps = rng.integers(0, 120, size=n)
    exercise_periods = [[120, 150], [400, 430]]
    for start, end in exercise_periods:
        if start < n and end <= n:
            steps[start:end] = rng.integers(120, 180, size=end-start)

    hr = 70 + (steps > 100) * rng.integers(25, 50, size=n) + rng.normal(0, 5, n)

    # Glucose dynamics
    glucose = circadian + noise
    for i in range(n):
        # Meal absorption (delayed)
        if i >= 6:
            glucose[i] += 0.4 * meals[i-6:i].sum() / 6
        # Insulin effect (delayed, persistent)
        if i >= 4:
            glucose[i] -= 1.2 * insulin[i-4:i].sum() / 4
        # Exercise effect
        if steps[i] > 100:
            glucose[i] -= 15

    # Add some hypo/hyper episodes
    glucose[180:200] = rng.normal(62, 5, 20)  # Hypo episode
    glucose[350:365] = rng.normal(210, 10, 15)  # Hyper episode

    df = pd.DataFrame({
        "timestamp": times,
        "glucose_mgdl": np.round(np.clip(glucose, 40, 350), 1),
        "carbs_g": np.round(meals, 1),
        "insulin_units": np.round(insulin, 1),
        "steps": steps.astype(int),
        "hr": np.round(hr, 0).astype(int),
    })

# Parse timestamps
df["timestamp"] = pd.to_datetime(df["timestamp"], utc=True, errors="coerce")
if df["timestamp"].dt.tz is None:
    df["timestamp"] = df["timestamp"].dt.tz_localize("UTC")
df = df.sort_values("timestamp").reset_index(drop=True)

# Feature engineering
df["dt_min"] = df["timestamp"].diff().dt.total_seconds() / 60.0
df["glucose_prev"] = df["glucose_mgdl"].shift(1)
df["roc_mgdl_min"] = (df["glucose_mgdl"] - df["glucose_prev"]) / df["dt_min"]
df["roc_mgdl_min"] = df["roc_mgdl_min"].replace([np.inf, -np.inf], 0.0).fillna(0.0)
df["time_min"] = (df["timestamp"] - df["timestamp"].iloc[0]).dt.total_seconds() / 60.0

# Build heavy model
with st.spinner("Training ensemble model..."):
    predict_proba = build_ensemble_model(df)

st.success("✅ Ensemble model trained (LogReg + RandomForest + GBM)")

# Initialize Sundew runtime
with st.spinner("Initializing Sundew PipelineRuntime..."):
    config = get_preset(preset_name)
    config.target_activation_rate = target_activation
    config.energy_pressure = energy_pressure
    config.gate_temperature = gate_temperature

    # Custom significance model
    diabetes_config = {
        "hypo_threshold": hypo_threshold,
        "hyper_threshold": hyper_threshold,
        "target_glucose": 100.0,
    }
    significance_model = DiabetesSignificanceModel(diabetes_config)

    # Build pipeline runtime
    from sundew.runtime import PipelineRuntime, SimpleGatingStrategy, SimpleControlPolicy, SimpleEnergyModel

    runtime = PipelineRuntime(
        config=config,
        significance_model=significance_model,
        gating_strategy=SimpleGatingStrategy(config.hysteresis_gap),
        control_policy=SimpleControlPolicy(config),
        energy_model=SimpleEnergyModel(
            processing_cost=config.base_processing_cost,
            idle_cost=config.dormant_tick_cost,
        ),
    )

st.success(f"✅ PipelineRuntime initialized with {preset_name} preset")

# Runtime monitoring
monitor = RuntimeMonitor()

# Processing loop
st.header("🔬 Processing Events")
progress_bar = st.progress(0)
status_text = st.empty()

results = []
ground_truth = []

for idx, row in df.iterrows():
    progress_bar.progress((idx + 1) / len(df))

    # Create processing context
    context = ProcessingContext(
        timestamp=row["timestamp"].timestamp(),
        sequence_id=idx,
        features={
            "glucose_mgdl": row["glucose_mgdl"],
            "roc_mgdl_min": row["roc_mgdl_min"],
            "insulin_units": row["insulin_units"],
            "carbs_g": row["carbs_g"],
            "hr": row["hr"],
            "steps": row["steps"],
            "time_min": row["time_min"],
        },
        history=[],
        metadata={},
    )

    # Process with runtime (pass features dict, not ProcessingContext)
    t_start = time.perf_counter()
    result = runtime.process(context.features)
    t_elapsed = (time.perf_counter() - t_start) * 1000  # ms

    # Heavy model prediction if activated
    risk_proba = None
    if result.activated:
        X = np.array([[
            row["glucose_mgdl"],
            row["roc_mgdl_min"],
            row["insulin_units"],
            row["carbs_g"],
            row["hr"],
        ]])
        try:
            risk_proba = predict_proba(X)
        except:
            risk_proba = None

    # Ground truth (for evaluation)
    future_idx = min(idx + 6, len(df) - 1)
    future_glucose = df.iloc[future_idx]["glucose_mgdl"]
    true_risk = 1 if (future_glucose < hypo_threshold or future_glucose > hyper_threshold) else 0
    ground_truth.append(true_risk)

    # Record telemetry
    telemetry = TelemetryEvent(
        timestamp=context.timestamp,
        event_id=idx,
        glucose=row["glucose_mgdl"],
        roc=row["roc_mgdl_min"],
        significance=result.significance,
        threshold=result.threshold_used,
        activated=result.activated,
        energy_level=result.energy_consumed,  # Use energy_consumed as proxy
        risk_proba=risk_proba,
        processing_time_ms=t_elapsed,
        components=result.explanation.get("feature_contributions", {}),
    )
    monitor.add_event(telemetry)

    results.append({
        "timestamp": row["timestamp"],
        "glucose": row["glucose_mgdl"],
        "roc": row["roc_mgdl_min"],
        "significance": result.significance,
        "threshold": result.threshold_used,
        "activated": result.activated,
        "energy_level": result.energy_consumed,
        "risk_proba": risk_proba,
        "true_risk": true_risk,
    })

progress_bar.empty()
status_text.empty()

# Convert to DataFrame
results_df = pd.DataFrame(results)
telemetry_df = monitor.get_telemetry_df()

# Compute metrics
total_events = len(results_df)
total_activations = int(results_df["activated"].sum())
activation_rate = total_activations / total_events
energy_savings = 1 - activation_rate

# Statistical evaluation (on activated events)
activated_results = results_df[results_df["activated"]].copy()
if len(activated_results) > 10:
    y_true = activated_results["true_risk"].values
    y_pred = (activated_results["risk_proba"].fillna(0.5) >= 0.5).astype(int).values

    f1 = f1_score(y_true, y_pred, zero_division=0)
    precision = precision_score(y_true, y_pred, zero_division=0)
    recall = recall_score(y_true, y_pred, zero_division=0)

    if show_bootstrap:
        f1_mean, f1_low, f1_high = bootstrap_metric(y_true, y_pred, lambda yt, yp: f1_score(yt, yp, zero_division=0))
        prec_mean, prec_low, prec_high = bootstrap_metric(y_true, y_pred, lambda yt, yp: precision_score(yt, yp, zero_division=0))
        rec_mean, rec_low, rec_high = bootstrap_metric(y_true, y_pred, lambda yt, yp: recall_score(yt, yp, zero_division=0))
else:
    f1 = precision = recall = 0.0
    f1_mean = prec_mean = rec_mean = 0.0
    f1_low = f1_high = prec_low = prec_high = rec_low = rec_high = 0.0

# Dashboard
st.header("📊 Performance Dashboard")

col1, col2, col3, col4 = st.columns(4)
col1.metric("Total Events", f"{total_events}")
col2.metric("Activations", f"{total_activations} ({activation_rate:.1%})")
col3.metric("Energy Savings", f"{energy_savings:.1%}")
col4.metric("Alerts", f"{len(monitor.alerts)}")

col1, col2, col3 = st.columns(3)
if show_bootstrap and len(activated_results) > 10:
    col1.metric("F1 Score", f"{f1_mean:.3f}", help=f"95% CI: [{f1_low:.3f}, {f1_high:.3f}]")
    col2.metric("Precision", f"{prec_mean:.3f}", help=f"95% CI: [{prec_low:.3f}, {prec_high:.3f}]")
    col3.metric("Recall", f"{rec_mean:.3f}", help=f"95% CI: [{rec_low:.3f}, {rec_high:.3f}]")
else:
    col1.metric("F1 Score", f"{f1:.3f}")
    col2.metric("Precision", f"{precision:.3f}")
    col3.metric("Recall", f"{recall:.3f}")

# Visualizations
st.header("📈 Real-Time Visualizations")

# Glucose + Threshold
fig_col1, fig_col2 = st.columns(2)

with fig_col1:
    st.subheader("Glucose Levels")
    chart_data = results_df.set_index("timestamp")[["glucose"]]
    st.line_chart(chart_data, height=250)

with fig_col2:
    st.subheader("Significance vs Threshold (Adaptive PI Control)")
    chart_data = results_df.set_index("timestamp")[["significance", "threshold"]]
    st.line_chart(chart_data, height=250)

# Energy tracking
if show_energy_viz:
    st.subheader("Energy Level (Bio-Inspired Regeneration)")
    chart_data = results_df.set_index("timestamp")[["energy_level"]]
    st.line_chart(chart_data, height=200)

# Significance components
if show_components and len(telemetry_df) > 0:
    comp_cols = [c for c in telemetry_df.columns if c.startswith("comp_")]
    if comp_cols:
        st.subheader("Significance Components (Diabetes-Specific Risk Factors)")
        chart_data = telemetry_df.set_index("timestamp")[comp_cols]
        st.line_chart(chart_data, height=200)

# Alerts
st.header("⚠️ Risk Alerts")
if monitor.alerts:
    alerts_df = pd.DataFrame(monitor.alerts)
    st.dataframe(alerts_df, use_container_width=True)
else:
    st.info("No high-risk alerts triggered in this window.")

# Detailed telemetry
with st.expander("🔍 Detailed Telemetry (Last 100 Events)"):
    st.dataframe(results_df.tail(100), use_container_width=True)

# Export telemetry
if export_telemetry:
    st.header("📥 Export Telemetry")
    json_data = monitor.export_json()
    st.download_button(
        label="Download Telemetry JSON",
        data=json_data,
        file_name="sundew_diabetes_telemetry.json",
        mime="application/json",
    )
    st.success("Telemetry ready for hardware validation workflows")

# Footer
st.divider()
st.caption(f"🌿 Powered by Sundew Algorithms v0.7+ | PipelineRuntime with custom DiabetesSignificanceModel | Research prototype")