bio-performx/app/services/graph_generator.py

"""
Graph Generator Service

This service generates all the charts and visualizations required for the medical report.
Based on the analysis notebooks in services_dfdf/.
"""

import base64
from pathlib import Path

import matplotlib

matplotlib.use("Agg")  # Use non-interactive backend
import matplotlib.pyplot as plt
import matplotlib.transforms as mtransforms
import numpy as np
import pandas as pd
import seaborn as sns
from matplotlib.patches import FancyBboxPatch


class GraphGenerator:
    """Generate all charts for medical reports"""

    def __init__(self, charts_dir: str = "graphs"):
        """
        Initialize the graph generator.

        Args:
            charts_dir: Directory to save generated charts
        """
        self.charts_dir = Path(charts_dir)
        self.charts_dir.mkdir(exist_ok=True)

    def _image_to_base64(self, image_path: Path) -> str:
        """
        Convert image file to base64 string.

        Args:
            image_path: Path to image file

        Returns:
            Base64 encoded string
        """
        try:
            with open(image_path, "rb") as image_file:
                return base64.b64encode(image_file.read()).decode("utf-8")
        except FileNotFoundError:
            return ""

    def generate_respiratory_chart(
        self, df: pd.DataFrame, save_as_base64: bool = True
    ) -> str:
        """
        Generate respiratory chart (VT and Speed over time).

        Args:
            df: Processed DataFrame with smoothed columns
            save_as_base64: If True, return base64 string, else return file path

        Returns:
            Base64 string or file path
        """
        first_unique_phase = df.drop_duplicates(subset="PHASE")
        phase_times = first_unique_phase["T(sec)"].tolist()

        plt.figure(figsize=(18, 5))
        ax1 = plt.subplot()

        # Plot VT
        sns.lineplot(data=df, x="T(sec)", y="VT(l)_smoothed", label="VT (L)")
        ax1.set_xlabel("Time (sec)")
        ax1.set_ylabel("VT (L)")
        ax1.grid(True, alpha=0.1)
        ax1.set_ylim(0, min(8, df["VT(l)_smoothed"].max()))

        # Plot speed on secondary y-axis
        ax2 = ax1.twinx()
        ax1.set_xticks(np.arange(0, df["T(sec)"].max() + 200, 200))
        sns.lineplot(
            data=df,
            x="T(sec)",
            y="Speed",
            color="green",
            ax=ax2,
            drawstyle="steps-post",
            linewidth=2,
            label="Speed",
        )
        ax2.set_ylabel("Speed")
        ax2.set_ylim(0, min(30, df["Speed"].max()) + 1)

        # Combine legends
        ax1.get_legend().remove()
        ax2.get_legend().remove()
        lines1, labels1 = ax1.get_legend_handles_labels()
        lines2, labels2 = ax2.get_legend_handles_labels()
        ax1.legend(lines1 + lines2, labels1 + labels2, loc="upper left")

        # Add colored background regions
        if len(phase_times) >= 4:
            ax1.axvspan(0, phase_times[1], alpha=0.2, color="lightblue")
            ax1.axvspan(phase_times[1], phase_times[2], alpha=0.2, color="purple")
            ax1.axvspan(phase_times[2], phase_times[3], alpha=0.2, color="lightgreen")
            ax1.axvspan(phase_times[3], df["T(sec)"].max(), alpha=0.2, color="blue")

        chart_path = self.charts_dir / "respiratory.png"
        plt.savefig(chart_path, dpi=300, bbox_inches="tight")
        plt.close()

        return self._image_to_base64(chart_path) if save_as_base64 else str(chart_path)

    def generate_fuel_utilization_chart(
        self, df: pd.DataFrame, save_as_base64: bool = True
    ) -> str:
        """
        Generate fuel utilization chart (CHO vs FAT by stage).

        Args:
            df: Processed DataFrame with smoothed columns
            save_as_base64: If True, return base64 string, else return file path

        Returns:
            Base64 string or file path
        """
        # Group by speed and calculate mean
        speed_groups = df.groupby("Speed").mean(numeric_only=True).round(1)
        speed_groups = speed_groups.iloc[1:-1]

        # Filter data
        filtered_data = speed_groups[
            (speed_groups.index >= 3.5) & (speed_groups.index <= 7.5)
        ]

        plt.figure(figsize=(15, 8))
        plt.style.use("default")

        stage_labels = [f"Stage {i}" for i in range(1, len(filtered_data) + 1)]
        x_positions = np.arange(len(filtered_data))

        # Calculate fat and carbs energy expenditure
        fat_ee = filtered_data["EE(kcal/min)"] * filtered_data["FAT(%)"] / 100
        carbs_ee = filtered_data["EE(kcal/min)"] * filtered_data["CARBS(%)"] / 100

        ax1 = plt.gca()

        # Create stacked bar chart
        ax1.bar(
            x_positions,
            fat_ee,
            color="#1f77b4",
            alpha=0.8,
            width=0.6,
            label="Fat",
        )
        ax1.bar(
            x_positions,
            carbs_ee,
            bottom=fat_ee,
            color="#ff7f0e",
            alpha=0.8,
            width=0.6,
            label="Carbs",
        )

        ax1.set_xlabel("", fontsize=12)
        ax1.set_ylabel("Fuel (kcal/min)", fontsize=12)
        ax1.set_ylim(0, 20)

        # Add values on bars
        for i, (fat_val, carb_val, total_val) in enumerate(
            zip(fat_ee, carbs_ee, filtered_data["EE(kcal/min)"])
        ):
            if fat_val > 0.3:
                ax1.text(
                    i,
                    fat_val / 2,
                    f"{fat_val:.1f}",
                    ha="center",
                    va="center",
                    fontsize=9,
                    fontweight="bold",
                    color="white",
                )
            if carb_val > 0.3:
                ax1.text(
                    i,
                    fat_val + carb_val / 2,
                    f"{carb_val:.1f}",
                    ha="center",
                    va="center",
                    fontsize=9,
                    fontweight="bold",
                    color="white",
                )
            ax1.text(
                i,
                total_val + 0.5,
                f"{total_val:.1f} kcal",
                ha="center",
                va="bottom",
                fontsize=10,
                fontweight="bold",
                color="black",
            )

        # Add speed labels
        for i, speed in enumerate(filtered_data.index):
            ax1.text(i, -1.5, f"{speed:.1f} mph", ha="center", va="top", fontsize=9)
            ax1.text(
                i,
                -2.8,
                f"{speed * 1.609:.1f} min/km",
                ha="center",
                va="top",
                fontsize=8,
                color="gray",
            )

        # Create secondary y-axis for heart rate
        ax2 = ax1.twinx()
        ax2.plot(
            x_positions,
            filtered_data["HR(bpm)"],
            marker="o",
            linewidth=3,
            markersize=8,
            color="red",
            label="Heart Rate",
        )

        ax2.set_ylabel("Heart Rate (bpm)", fontsize=12, color="red")
        ax2.tick_params(axis="y", labelcolor="red")
        ax2.set_ylim(0, 220)

        # Add HR values
        for i, hr in enumerate(filtered_data["HR(bpm)"]):
            ax2.text(
                i,
                hr + 10,
                f"{int(hr)}bpm",
                ha="center",
                va="bottom",
                fontsize=10,
                fontweight="bold",
                color="red",
            )

        ax1.set_xticks(x_positions)
        ax1.set_xticklabels(stage_labels, fontsize=11)

        # Create legend
        lines1, labels1 = ax1.get_legend_handles_labels()
        lines2, labels2 = ax2.get_legend_handles_labels()
        ax1.legend(
            lines1 + lines2,
            labels1 + labels2,
            loc="upper left",
            frameon=True,
            fancybox=True,
            shadow=True,
        )

        ax1.grid(True, alpha=0.3, linestyle="-", linewidth=0.5)
        ax1.set_axisbelow(True)

        plt.tight_layout()
        plt.subplots_adjust(bottom=0.1, top=0.9)

        chart_path = self.charts_dir / "fuel_utilization_chart.png"
        plt.savefig(chart_path, dpi=300)
        plt.close()

        return self._image_to_base64(chart_path) if save_as_base64 else str(chart_path)

    def generate_vo2_pulse_chart(
        self, df: pd.DataFrame, save_as_base64: bool = True
    ) -> str:
        """
        Generate VO2 Pulse chart with HR and Speed.

        Args:
            df: Processed DataFrame with smoothed columns
            save_as_base64: If True, return base64 string, else return file path

        Returns:
            Base64 string or file path
        """
        first_unique_phase = df.drop_duplicates(subset="PHASE")
        phase_times = first_unique_phase["T(sec)"].tolist()

        plt.figure(figsize=(18, 5))
        ax1 = plt.subplot()

        # Plot VO2 Pulse
        sns.lineplot(
            data=df,
            x="T(sec)",
            y="VO2 Pulse_smoothed",
            label="VO2 Pulse (mL/beat)",
            color="blue",
        )
        ax1.set_xlabel("Time (sec)")
        ax1.set_ylabel("VO2 Pulse (mL/beat)")
        ax1.set_ylim(0, df["VO2 Pulse_smoothed"].max())
        ax1.grid(True, alpha=0.1)

        # Create second y-axis for heart rate
        ax2 = ax1.twinx()
        sns.lineplot(
            data=df,
            x="T(sec)",
            y="HR(bpm)_smoothed",
            color="red",
            ax=ax2,
            linewidth=2,
            label="Heart Rate (bpm)",
        )
        ax2.set_ylabel("Heart Rate (bpm)", color="red")
        ax2.tick_params(axis="y", labelcolor="red")
        ax2.set_ylim(0, df["HR(bpm)_smoothed"].max() + 1)

        # Create third y-axis for speed
        ax3 = ax1.twinx()
        ax3.spines["right"].set_position(("outward", 60))
        sns.lineplot(
            data=df,
            x="T(sec)",
            y="Speed",
            color="green",
            ax=ax3,
            drawstyle="steps-post",
            linewidth=2,
            label="Speed",
        )
        ax3.set_ylabel("Speed", color="green")
        ax3.tick_params(axis="y", labelcolor="green")
        ax3.set_ylim(0, df["Speed"].max() + 1)

        ax1.set_xticks(np.arange(0, df["T(sec)"].max() + 200, 200))

        # Combine legends
        if ax1.get_legend():
            ax1.get_legend().remove()
        if ax2.get_legend():
            ax2.get_legend().remove()
        if ax3.get_legend():
            ax3.get_legend().remove()

        lines1, labels1 = ax1.get_legend_handles_labels()
        lines2, labels2 = ax2.get_legend_handles_labels()
        lines3, labels3 = ax3.get_legend_handles_labels()
        ax1.legend(
            lines1 + lines2 + lines3, labels1 + labels2 + labels3, loc="upper left"
        )

        # Add colored background regions
        if len(phase_times) >= 4:
            ax1.axvspan(0, phase_times[1], alpha=0.2, color="lightblue")
            ax1.axvspan(phase_times[1], phase_times[2], alpha=0.2, color="purple")
            ax1.axvspan(phase_times[2], phase_times[3], alpha=0.2, color="lightgreen")
            ax1.axvspan(phase_times[3], df["T(sec)"].max(), alpha=0.2, color="blue")

        chart_path = self.charts_dir / "vo2_pulse_chart.png"
        plt.savefig(chart_path, bbox_inches="tight", dpi=300)
        plt.close()

        return self._image_to_base64(chart_path) if save_as_base64 else str(chart_path)

    def generate_vo2_breath_chart(
        self, df: pd.DataFrame, save_as_base64: bool = True
    ) -> str:
        """
        Generate VO2 per Breath chart.

        Args:
            df: Processed DataFrame with smoothed columns
            save_as_base64: If True, return base64 string, else return file path

        Returns:
            Base64 string or file path
        """
        first_unique_phase = df.drop_duplicates(subset="PHASE")
        phase_times = first_unique_phase["T(sec)"].tolist()

        plt.figure(figsize=(18, 5))
        ax1 = plt.subplot()

        sns.lineplot(
            data=df,
            x="T(sec)",
            y="VO2 Breath_smoothed",
            label="VO2 per Breath (mL/breath)",
        )
        ax1.set_xlabel("Time (sec)")
        ax1.set_ylabel("VO2 per Breath (mL/breath)")
        ax1.set_ylim(0, df["VO2 Breath_smoothed"].max() + 1)
        ax1.grid(True, alpha=0.1)

        # Plot speed on secondary y-axis
        ax2 = ax1.twinx()
        ax1.set_xticks(np.arange(0, df["T(sec)"].max() + 200, 200))
        sns.lineplot(
            data=df,
            x="T(sec)",
            y="Speed",
            color="green",
            ax=ax2,
            drawstyle="steps-post",
            linewidth=2,
            label="Speed",
        )
        ax2.set_ylim(0, df["Speed"].max() + 1)
        ax2.set_ylabel("Speed")

        # Combine legends
        ax1.get_legend().remove()
        ax2.get_legend().remove()
        lines1, labels1 = ax1.get_legend_handles_labels()
        lines2, labels2 = ax2.get_legend_handles_labels()
        ax1.legend(lines1 + lines2, labels1 + labels2, loc="upper left")

        # Add colored background regions
        if len(phase_times) >= 4:
            ax1.axvspan(0, phase_times[1], alpha=0.2, color="lightblue")
            ax1.axvspan(phase_times[1], phase_times[2], alpha=0.2, color="purple")
            ax1.axvspan(phase_times[2], phase_times[3], alpha=0.2, color="lightgreen")
            ax1.axvspan(phase_times[3], df["T(sec)"].max(), alpha=0.2, color="blue")

        chart_path = self.charts_dir / "vo2_breath_chart.png"
        plt.savefig(chart_path, bbox_inches="tight", dpi=300)
        plt.close()

        return self._image_to_base64(chart_path) if save_as_base64 else str(chart_path)

    def generate_fat_metabolism_chart(
        self, df: pd.DataFrame, save_as_base64: bool = True
    ) -> str:
        """
        Generate fat metabolism chart (CHO vs FAT over time).

        Args:
            df: Processed DataFrame with smoothed columns
            save_as_base64: If True, return base64 string, else return file path

        Returns:
            Base64 string or file path
        """
        first_unique_phase = df.drop_duplicates(subset="PHASE")
        phase_times = first_unique_phase["T(sec)"].tolist()

        plt.figure(figsize=(18, 5))
        ax1 = plt.subplot()

        # Plot CHO
        sns.lineplot(data=df, x="T(sec)", y="CHO_smoothed", label="CHO (kcal/min)")
        ax1.set_xlabel("Time (sec)")
        ax1.set_ylabel("CHO (g/min)")
        ax1.grid(True, alpha=0.1)

        # Plot FAT on secondary y-axis
        ax2 = ax1.twinx()
        ax1.set_xticks(np.arange(0, df["T(sec)"].max() + 200, 200))
        sns.lineplot(
            data=df,
            x="T(sec)",
            y="FAT_smoothed",
            color="green",
            ax=ax2,
            label="FAT (kcal/min)",
        )
        ax2.set_ylabel("FAT (kcal/min)")
        ax2.set_ylim(0, 15)

        # Combine legends
        ax1.get_legend().remove()
        ax2.get_legend().remove()
        lines1, labels1 = ax1.get_legend_handles_labels()
        lines2, labels2 = ax2.get_legend_handles_labels()
        ax1.legend(lines1 + lines2, labels1 + labels2, loc="upper left")

        # Add colored background regions
        if len(phase_times) >= 4:
            ax1.axvspan(0, phase_times[1], alpha=0.2, color="lightblue")
            ax1.axvspan(phase_times[1], phase_times[2], alpha=0.2, color="purple")
            ax1.axvspan(phase_times[2], phase_times[3], alpha=0.2, color="lightgreen")
            ax1.axvspan(phase_times[3], df["T(sec)"].max(), alpha=0.2, color="blue")

        chart_path = self.charts_dir / "fat_metabolism_chart.png"
        plt.savefig(chart_path, bbox_inches="tight", dpi=300)
        plt.close()

        return self._image_to_base64(chart_path) if save_as_base64 else str(chart_path)

    def generate_recovery_chart(
        self, df: pd.DataFrame, save_as_base64: bool = True
    ) -> str:
        """
        Generate recovery chart (VCO2, HR, and BF).

        Args:
            df: Processed DataFrame with smoothed columns
            save_as_base64: If True, return base64 string, else return file path

        Returns:
            Base64 string or file path
        """
        first_unique_phase = df.drop_duplicates(subset="PHASE")
        phase_times = first_unique_phase["T(sec)"].tolist()

        plt.figure(figsize=(18, 5))
        ax1 = plt.subplot()

        # Plot VCO2
        sns.lineplot(
            data=df,
            x="T(sec)",
            y="VCO2(ml/min)_smoothed",
            label="VCO2 (ml/min)",
            color="blue",
        )
        ax1.set_xlabel("Time (sec)")
        ax1.set_ylabel("VO2 Pulse (mL/beat)")
        ax1.set_ylim(0, df["VCO2(ml/min)"].max())
        ax1.grid(True, alpha=0.1)

        # Create second y-axis for heart rate
        ax2 = ax1.twinx()
        sns.lineplot(
            data=df,
            x="T(sec)",
            y="HR(bpm)_smoothed",
            color="red",
            ax=ax2,
            linewidth=2,
            label="Heart Rate (bpm)",
        )
        ax2.set_ylabel("Heart Rate (bpm)", color="red")
        ax2.set_ylim(df["HR(bpm)_smoothed"].min(), df["HR(bpm)_smoothed"].max() + 1)
        ax2.tick_params(axis="y", labelcolor="red")

        # Create third y-axis for BF
        ax3 = ax1.twinx()
        ax3.spines["right"].set_position(("outward", 60))
        sns.lineplot(
            data=df,
            x="T(sec)",
            y="BF(bpm)_smoothed",
            color="green",
            ax=ax3,
            linewidth=2,
            label="BF (bpm)",
        )
        ax3.set_ylabel("BF (bpm)", color="green")
        ax3.tick_params(axis="y", labelcolor="green")
        ax3.set_ylim(0, df["BF(bpm)_smoothed"].max() + 1)
        ax1.set_xticks(np.arange(0, df["T(sec)"].max() + 200, 200))

        # Combine legends
        if ax1.get_legend():
            ax1.get_legend().remove()
        if ax2.get_legend():
            ax2.get_legend().remove()
        if ax3.get_legend():
            ax3.get_legend().remove()

        lines1, labels1 = ax1.get_legend_handles_labels()
        lines2, labels2 = ax2.get_legend_handles_labels()
        lines3, labels3 = ax3.get_legend_handles_labels()
        ax1.legend(
            lines1 + lines2 + lines3, labels1 + labels2 + labels3, loc="upper left"
        )

        # Add colored background regions
        if len(phase_times) >= 4:
            ax1.axvspan(0, phase_times[1], alpha=0.2, color="lightblue")
            ax1.axvspan(phase_times[1], phase_times[2], alpha=0.2, color="purple")
            ax1.axvspan(phase_times[2], phase_times[3], alpha=0.2, color="lightgreen")
            ax1.axvspan(phase_times[3], df["T(sec)"].max(), alpha=0.2, color="blue")

        chart_path = self.charts_dir / "recovery_chart.png"
        plt.savefig(chart_path, bbox_inches="tight", dpi=300)
        plt.close()

        return self._image_to_base64(chart_path) if save_as_base64 else str(chart_path)

    def generate_tsi_chart(
        self, oxygenation_df: pd.DataFrame, save_as_base64: bool = True
    ) -> str:
        """
        Generate TSI (Tissue Saturation Index) chart with trend lines per stage.

        Args:
            oxygenation_df: DataFrame with Time, TSI, and TSI-second columns
            save_as_base64: If True, return base64 string, else return file path

        Returns:
            Base64 string or file path
        """
        from numpy.polynomial.polynomial import Polynomial

        plt.figure(figsize=(12, 5.5))

        # Plot TSI (Left Leg)
        plt.plot(
            oxygenation_df["Time"],
            oxygenation_df["TSI"],
            label="TSI (Left Leg)",
            color="steelblue",
            linewidth=2,
        )

        # Plot TSI2 (Right Leg)
        plt.plot(
            oxygenation_df["Time"],
            oxygenation_df["TSI-second"],
            label="TSI2 (Right Leg)",
            color="orange",
            linewidth=2,
        )

        # Define time intervals for stages (adjust these based on your test protocol)
        max_time = oxygenation_df["Time"].max()
        intervals = [
            (0, 250),
            (250, 500),
            (500, 750),
            (750, 1000),
            (1000, 1250),
            (1250, 1500),
            (1500, max_time),
        ]

        # Calculate and plot trend lines for each interval
        for start_time, end_time in intervals:
            # Filter data for this interval
            mask_interval = (oxygenation_df["Time"] >= start_time) & (
                oxygenation_df["Time"] <= end_time
            )

            # TSI (Left Leg) trend for this interval
            mask_left = mask_interval & ~oxygenation_df["TSI"].isna()
            if mask_left.sum() > 1:  # Need at least 2 points for a line
                x_left = oxygenation_df.loc[mask_left, "Time"]
                y_left = oxygenation_df.loc[mask_left, "TSI"]
                coefs_left = Polynomial.fit(x_left, y_left, 1).convert().coef
                trend_left = coefs_left[0] + coefs_left[1] * x_left
                plt.plot(
                    x_left,
                    trend_left,
                    color="black",
                    linestyle="--",
                    linewidth=2,
                    alpha=0.8,
                )

            # TSI-second (Right Leg) trend for this interval
            mask_right = mask_interval & ~oxygenation_df["TSI-second"].isna()
            if mask_right.sum() > 1:  # Need at least 2 points for a line
                x_right = oxygenation_df.loc[mask_right, "Time"]
                y_right = oxygenation_df.loc[mask_right, "TSI-second"]
                coefs_right = Polynomial.fit(x_right, y_right, 1).convert().coef
                trend_right = coefs_right[0] + coefs_right[1] * x_right
                plt.plot(
                    x_right,
                    trend_right,
                    color="black",
                    linestyle="--",
                    linewidth=2,
                    alpha=0.8,
                )

        plt.xlabel("Time (s)")
        plt.ylabel("TSI (%)")
        plt.title("TSI (Left) and TSI2 (Right) with Black Slope Lines per Stage")
        plt.legend(fontsize=10, loc="upper right")
        plt.grid(alpha=0.25)
        plt.tight_layout()

        chart_path = self.charts_dir / "tsi_chart.png"
        plt.savefig(chart_path, bbox_inches="tight", dpi=160)
        plt.close()

        return self._image_to_base64(chart_path) if save_as_base64 else str(chart_path)

    def generate_body_composition_chart(
        self, fat_mass_lbs: float, lean_mass_lbs: float, save_as_base64: bool = True
    ) -> str:
        """
        Generate body composition donut chart.

        Args:
            fat_mass_lbs: Fat mass in pounds
            lean_mass_lbs: Lean mass in pounds
            save_as_base64: If True, return base64 string, else return file path

        Returns:
            Base64 string or file path
        """
        # Calculate percentages
        total_weight = fat_mass_lbs + lean_mass_lbs
        fat_percentage = (fat_mass_lbs / total_weight) * 100
        lean_percentage = (lean_mass_lbs / total_weight) * 100

        sizes = [fat_percentage, lean_percentage]
        colors = ["#fde3ac", "#ff9966"]

        plt.figure(figsize=(8, 8))

        # Create donut chart
        plt.pie(
            sizes,
            autopct="",
            startangle=90,
            wedgeprops=dict(width=0.5, edgecolor="w"),
            colors=colors,
            labels=["", ""],
        )

        # Add custom text annotations
        plt.text(
            -1,
            1,
            f"Fat Mass ({fat_mass_lbs:.1f}lbs)\n{fat_percentage:.1f}%",
            fontsize=14,
            fontweight="bold",
            ha="center",
            va="center",
            bbox=dict(boxstyle="round,pad=0.3", facecolor="white", alpha=0.8),
        )

        plt.text(
            1,
            -1,
            f"Lean Mass ({lean_mass_lbs:.1f}lbs)\n{lean_percentage:.1f}%",
            fontsize=14,
            fontweight="bold",
            ha="center",
            va="center",
            bbox=dict(boxstyle="round,pad=0.3", facecolor="white", alpha=0.8),
        )

        plt.axis("equal")

        chart_path = self.charts_dir / "body_composition_chart.png"
        plt.savefig(chart_path, bbox_inches="tight", dpi=600)
        plt.close()

        return self._image_to_base64(chart_path) if save_as_base64 else str(chart_path)

    def generate_body_fat_percent_chart(
        self,
        fat_percentage: float,
        age: int,
        gender: str,
        save_as_base64: bool = True,
    ) -> str:
        """
        Generate body fat percentage chart.

        Args:
            fat_percentage: Body fat percentage
            age: Patient age
            gender: Patient gender ('male' or 'female')
            save_as_base64: If True, return base64 string, else return file path

        Returns:
            Base64 string or file path
        """
        # Determine age group
        if 20 <= age <= 39:
            age_group = "20-39"
        elif 40 <= age <= 59:
            age_group = "40-59"
        elif 60 <= age <= 79:
            age_group = "60-79"
        else:
            age_group = "20-39"  # Default

        gender_abbrev = "M" if gender.lower() == "male" else "F"
        demographic = f"{age_group}\n({gender_abbrev})"

        # Define segments based on gender and age group
        if gender.lower() == "female":
            if age_group == "20-39":
                segments = [
                    ("#F8A8A8", 0, 15),  # Bad: 0-15%
                    ("#FFEECC", 15, 5),  # Okay: 15-20%
                    ("#D0F0C0", 20, 15),  # Good: 20-35%
                    ("#FFEECC", 35, 5),  # Okay: 35-40%
                    ("#F8A8A8", 40, 10),  # Bad: 40-50%
                ]
            else:  # 40-59 and 60-79 have same ranges for females
                segments = [
                    ("#F8A8A8", 0, 20),  # Bad: 0-20%
                    ("#FFEECC", 20, 5),  # Okay: 20-25%
                    ("#D0F0C0", 25, 10),  # Good: 25-35%
                    ("#FFEECC", 35, 5),  # Okay: 35-40%
                    ("#F8A8A8", 40, 10),  # Bad: 40-50%
                ]
        else:  # male
            if age_group == "20-39":
                segments = [
                    ("#F8A8A8", 0, 5),  # Bad: 0-5%
                    ("#FFEECC", 5, 5),  # Okay: 5-10%
                    ("#D0F0C0", 10, 10),  # Good: 10-20%
                    ("#FFEECC", 20, 5),  # Okay: 20-25%
                    ("#F8A8A8", 25, 25),  # Bad: 25-50%
                ]
            elif age_group == "40-59":
                segments = [
                    ("#F8A8A8", 0, 5),  # Bad: 0-5%
                    ("#FFEECC", 5, 5),  # Okay: 5-10%
                    ("#D0F0C0", 10, 10),  # Good: 10-20%
                    ("#FFEECC", 20, 10),  # Okay: 20-30%
                    ("#F8A8A8", 30, 20),  # Bad: 30-50%
                ]
            else:  # 60-79
                segments = [
                    ("#F8A8A8", 0, 5),  # Bad: 0-5%
                    ("#FFEECC", 5, 5),  # Okay: 5-10%
                    ("#D0F0C0", 10, 15),  # Good: 10-25%
                    ("#FFEECC", 25, 5),  # Okay: 25-30%
                    ("#F8A8A8", 30, 20),  # Bad: 30-50%
                ]

        fig, ax = plt.subplots(figsize=(10, 2))

        # Create the segmented bar
        for color, start, length in segments:
            ax.barh(
                y=0,
                width=length,
                left=start,
                height=1,
                color=color,
                edgecolor="black",
                linewidth=0.5,
            )

        # Add the indicator (triangle)
        ax.plot(
            fat_percentage,
            1.05,
            marker="v",
            color="black",
            markersize=10,
            clip_on=False,
            transform=ax.get_xaxis_transform(),
        )

        # Set axis properties
        ax.set_xlim(0, 50)
        ax.set_xticks(range(0, 51, 5))
        ax.set_yticks([])
        ax.text(
            -0.05,
            0,
            demographic,
            transform=ax.get_yaxis_transform(),
            va="center",
            ha="right",
            fontsize=12,
        )

        ticks = range(0, 51, 5)
        ax.set_xticks(ticks)
        labels = [f"{t}%" for t in ticks]
        ax.set_xticklabels(labels)

        # Clean up spines
        ax.spines["right"].set_visible(False)
        ax.spines["top"].set_visible(False)
        ax.spines["left"].set_visible(False)
        ax.spines["bottom"].set_visible(True)

        # Add tick marks
        for x in range(0, 51, 5):
            ax.plot(
                [x, x],
                [-0.05, -0.01],
                color="black",
                transform=ax.get_xaxis_transform(),
                clip_on=False,
            )

        plt.tight_layout()

        chart_path = self.charts_dir / "body_fat_percent_chart.png"
        plt.savefig(chart_path, bbox_inches="tight", dpi=300)
        plt.close()

        return self._image_to_base64(chart_path) if save_as_base64 else str(chart_path)

    def generate_spirometry_chart(
        self, spirometry_df: pd.DataFrame, save_as_base64: bool = True
    ) -> str:
        """
        Generate spirometry chart with Z-scores.

        Args:
            spirometry_df: Spirometry DataFrame with parameters
            save_as_base64: If True, return base64 string, else return file path

        Returns:
            Base64 string or file path
        """
        # Coerce numeric columns - handle various column name formats
        # Map standard column names to possible variations
        column_aliases = {
            "Best": ["Best", "best", "BEST"],
            "LLN": ["LLN", "lln"],
            "Pred.": ["Pred.", "Pred", "pred", "Predicted", "predicted"],
            "%Pred.": [
                "%Pred.",
                "%Pred",
                "%pred",
                "% Pred.",
                "% Pred",
                "Pred %",
                "Pred%",
            ],
            "ZScore": ["ZScore", "Z-Score", "z-score", "Zscore", "zscore", "Z Score"],
        }

        # Find and normalize column names
        column_mapping = {}
        for target_col, possible_names in column_aliases.items():
            for col_name in possible_names:
                if col_name in spirometry_df.columns:
                    column_mapping[target_col] = col_name
                    # Convert to numeric
                    spirometry_df[col_name] = pd.to_numeric(
                        spirometry_df[col_name], errors="coerce"
                    )
                    break

        # If standard columns don't exist, create aliases
        for target_col, source_col in column_mapping.items():
            if target_col not in spirometry_df.columns and source_col != target_col:
                spirometry_df[target_col] = spirometry_df[source_col]

        # Select rows of interest
        rows_map = {
            "Lung Volume": "FVC",
            "Lung Power": "FEV1",
            "Power/Volume": "FEV1/FVC%",
        }

        records = []
        for label, param in rows_map.items():
            # Try exact match first
            row = spirometry_df.loc[spirometry_df["Parameters"].str.strip() == param]
            if row.empty:
                # Try case-insensitive match
                row = spirometry_df.loc[
                    spirometry_df["Parameters"].str.strip().str.upper() == param.upper()
                ]
            if row.empty:
                # Try matching without % sign
                if "%" in param:
                    param_no_pct = param.replace("%", "")
                    row = spirometry_df.loc[
                        spirometry_df["Parameters"].str.strip() == param_no_pct
                    ]
            if row.empty:
                print(f"Warning: Could not find parameter '{param}' in spirometry data")
                print(f"Available parameters: {spirometry_df['Parameters'].tolist()}")
                continue
            row = row.iloc[0]
            # Get values with fallbacks for column name variations
            best_val = row.get("Best", row.get("best", pd.NA))
            pct_val = row.get(
                "%Pred.", row.get("%Pred", row.get("Pred %", row.get("Pred%", pd.NA)))
            )
            z_val = row.get("ZScore", row.get("Z-Score", row.get("Zscore", pd.NA)))

            records.append(
                {
                    "label": label,
                    "param": param,
                    "best": best_val,
                    "pct": pct_val,
                    "z": z_val,
                }
            )

        # Validate we have exactly 3 records
        if len(records) != 3:
            raise ValueError(
                f"Expected 3 spirometry parameters (FVC, FEV1, FEV1/FVC%), "
                f"but found {len(records)}. Found: {[r['param'] for r in records]}"
            )

        # Figure setup
        fig, axes = plt.subplots(
            nrows=3,
            ncols=1,
            figsize=(11.5, 3.6),
            sharex=True,
            gridspec_kw={"hspace": 0.65},
        )

        x_min, x_max = -5, 3
        # Segment colors
        segments = [
            (-5, -4, "#f4a7a7"),  # red-ish
            (-4, -3, "#f7c49a"),  # orange-ish
            (-3, -1.7, "#f6e3a3"),  # yellow-ish
            (-1.7, 3, "#c9f0cc"),  # green-ish
        ]

        ticks = np.arange(x_min, x_max + 1, 1)
        labels = [str(i) for i in ticks]

        # Plot each row
        for ax, rec in zip(axes, records):
            # Background segments
            for a, b, color in segments:
                ax.barh(
                    0, width=b - a, left=a, height=0.6, color=color, edgecolor="none"
                )

            # LLN and Predicted markers
            ax.axvline(0, color="black", lw=1)

            # Z-score pointer
            if pd.notna(rec["z"]):
                trans = mtransforms.blended_transform_factory(
                    ax.transData, ax.transAxes
                )
                ax.plot(
                    float(rec["z"]),
                    1.2,
                    marker="v",
                    markersize=12,
                    color="dimgray",
                    transform=trans,
                    clip_on=False,
                )

            # Labels and styling
            ax.set_title(
                rec["label"], loc="left", fontsize=11, fontweight="bold", pad=2
            )
            ax.set_xlim(x_min, x_max)
            ax.set_yticks([])
            ax.set_xticks(ticks)
            ax.set_xticklabels(labels, fontsize=8)
            ax.set_xlabel("")

        # Top annotations
        axes[0].text(-1.7, 0.45, "LLN", ha="center", va="bottom", fontsize=9)
        axes[0].text(0, 0.45, "Predicted", ha="center", va="bottom", fontsize=9)

        # Right-side summary boxes
        fig.subplots_adjust(right=0.78)
        box_ax = fig.add_axes([0.805, 0.06, 0.18, 0.90])
        box_ax.axis("off")

        def pill(ax, xy, text):
            x, y = xy
            bbox = FancyBboxPatch(
                (x - 0.48, y - 0.09),
                0.96,
                0.18,
                boxstyle="round,pad=0.02,rounding_size=0.08",
                ec="#dddddd",
                fc="#f3f3f3",
                linewidth=1.0,
            )
            ax.add_patch(bbox)
            ax.text(
                x,
                y + 0.025,
                text,
                ha="center",
                va="center",
                fontsize=11,
                fontweight="bold",
            )
            ax.text(
                x,
                y - 0.055,
                "of predicted",
                ha="center",
                va="center",
                fontsize=9,
                color="#555555",
            )

        box_ax.set_xlim(0, 1)
        box_ax.set_ylim(0, 1)

        # Prepare display strings
        right_items = []
        for rec in records:
            name = (
                "FVC"
                if rec["param"] == "FVC"
                else ("FEV1" if rec["param"] == "FEV1" else "FEV1/FVC")
            )
            unit = "L" if rec["param"] in ("FVC", "FEV1") else "%"
            value_fmt = f"{rec['best']:.2f}{unit}"
            pct_fmt = f"{rec['pct']:.1f}%"
            right_items.append((name, value_fmt, pct_fmt))

        # Sort to match order
        order = ["FVC", "FEV1", "FEV1/FVC"]
        right_items_sorted = [
            next(item for item in right_items if item[0] == k) for k in order
        ]

        ys = [0.82, 0.48, 0.15]
        for (name, value_fmt, pct_fmt), y in zip(right_items_sorted, ys):
            main_line = f"{name}\n{value_fmt} → {pct_fmt}"
            pill(box_ax, (0.5, y), main_line)

        chart_path = self.charts_dir / "spirometry_chart.png"
        plt.savefig(chart_path, dpi=300, bbox_inches="tight")
        plt.close()

        return self._image_to_base64(chart_path) if save_as_base64 else str(chart_path)

    def generate_metabolism_chart(
        self, rmr_kcal: float, save_as_base64: bool = True
    ) -> str:
        """
        Generate metabolism chart (Slow vs Fast Metabolism).

        Args:
            rmr_kcal: Resting metabolic rate in kcal/day
            save_as_base64: If True, return base64 string, else return file path

        Returns:
            Base64 string or file path
        """
        from matplotlib.patches import FancyBboxPatch

        fig, ax = plt.subplots(figsize=(10, 2.5))

        # Chart data and positions
        categories = ["Very Slow", "Slow", "Average", "Fast", "Very Fast"]
        positions = [1500, 3000, 4500, 6000, 7500]
        indicator_pos = rmr_kcal
        highlight_end = rmr_kcal

        # Main Bar (Background)
        main_bar = FancyBboxPatch(
            (0, 0.4),
            9000,
            0.2,
            boxstyle="round,pad=0,rounding_size=0.1",
            ec="none",
            fc="#E0E0E0",
        )
        ax.add_patch(main_bar)

        # Highlighted Bar
        highlight_bar = FancyBboxPatch(
            (0, 0.4),
            highlight_end,
            0.2,
            boxstyle="round,pad=0,rounding_size=0.1",
            ec="none",
            fc="#B2FFC8",
        )
        ax.add_patch(highlight_bar)

        # Text and Labels
        ax.text(
            highlight_end / 2,
            0.5,
            f"{rmr_kcal:.0f}kCals",
            ha="center",
            va="center",
            color="#006400",
            fontsize=14,
            weight="bold",
        )

        # Indicator Triangle
        ax.plot(indicator_pos, 0.65, "v", markersize=15, color="#606060", clip_on=False)

        # Ticks and Labels
        for pos, label in zip(positions, categories):
            ax.text(
                pos, 0.15, label, ha="center", va="center", fontsize=12, color="#333333"
            )
            ax.plot([pos, pos], [0.35, 0.39], color="grey", lw=5)

        # Chart Styling
        ax.set_title("Slow vs Fast Metabolism", fontsize=18, weight="bold", loc="left")
        ax.set_xlim(0, 9000)
        ax.set_ylim(0, 1)
        ax.axis("off")

        plt.tight_layout()

        chart_path = self.charts_dir / "metabolism_chart.png"
        plt.savefig(chart_path, bbox_inches="tight", dpi=300)
        plt.close()

        return self._image_to_base64(chart_path) if save_as_base64 else str(chart_path)

    def generate_fuel_source_chart(
        self, fat_percentage: float, save_as_base64: bool = True
    ) -> str:
        """
        Generate fuel source chart (Fats vs Carbs).

        Args:
            fat_percentage: Fat percentage at rest
            save_as_base64: If True, return base64 string, else return file path

        Returns:
            Base64 string or file path
        """
        from matplotlib.patches import FancyBboxPatch

        fig, ax = plt.subplots(figsize=(10, 2.5))

        carb_percentage = 100 - fat_percentage
        optimal_point = 75

        # Main Bars (Fats and Carbs)
        # Fats bar (yellow)
        fats_bar = FancyBboxPatch(
            (0, 0.4),
            fat_percentage,
            0.2,
            boxstyle="round,pad=0,rounding_size=0.1",
            ec="none",
            fc="#FEEAAB",
        )
        ax.add_patch(fats_bar)

        # Carbs bar (blue) - starts where the fats bar ends
        carbs_bar = FancyBboxPatch(
            (fat_percentage, 0.4),
            carb_percentage,
            0.2,
            boxstyle="round,pad=0,rounding_size=0.1",
            ec="none",
            fc="#A7F5FF",
        )
        ax.add_patch(carbs_bar)

        # Text and Labels
        ax.text(
            fat_percentage / 2,
            0.5,
            f"Fats\n{fat_percentage:.1f}%",
            ha="center",
            va="center",
            color="#333333",
            fontsize=12,
            weight="bold",
        )
        ax.text(
            fat_percentage + carb_percentage / 2,
            0.5,
            f"Carbs\n{carb_percentage:.1f}%",
            ha="center",
            va="center",
            color="#333333",
            fontsize=12,
            weight="bold",
        )

        # Add 'Optimal' label
        ax.text(optimal_point, 0.75, "Optimal", ha="center", va="center", fontsize=12)

        # Indicator Triangle
        ax.plot(
            fat_percentage, 0.65, "v", markersize=15, color="#606060", clip_on=False
        )

        # Ticks and Labels
        positions = [0, 25, 50, 75, 100]
        for pos in positions:
            ax.text(
                pos,
                0.15,
                str(pos),
                ha="center",
                va="center",
                fontsize=12,
                color="#333333",
            )
            ax.plot([pos, pos], [0.35, 0.39], color="grey", lw=5)

        # Add a special tick for the 'Optimal' point
        ax.plot([optimal_point, optimal_point], [0.6, 0.7], color="black", lw=2)

        # Chart Styling
        ax.set_title("Fuel Source", fontsize=18, weight="bold", loc="left")
        ax.set_ylim(0, 1)
        ax.axis("off")

        plt.tight_layout()

        chart_path = self.charts_dir / "fuel_source_chart.png"
        plt.savefig(chart_path, bbox_inches="tight", dpi=300)
        plt.close()

        return self._image_to_base64(chart_path) if save_as_base64 else str(chart_path)

    def generate_vo2_max_table(
        self,
        data: list[list],
        columns: list[str],
        vo2_max_value: float = None,
        category: str = None,
        cell_colors: list[list[str]] = None,
        save_as_base64: bool = True,
    ) -> str:
        """
        Generate VO2 Max table as an image with optimized sizing, highlighting the patient's category.

        Args:
            data: List of rows (each row is a list of values)
            columns: List of column headers
            vo2_max_value: Patient's VO2 max value (for title and arrow)
            category: Category that the patient falls into (e.g., 'Good', 'Excellent')
            cell_colors: Optional matrix of cell colors
            save_as_base64: If True, return base64 string

        Returns:
            Base64 string or file path
        """
        import io

        from matplotlib.patches import FancyArrowPatch, RegularPolygon

        # Fixed optimal sizing for VO2 Max table (7 columns, 1 data row)
        fig, ax = plt.subplots(figsize=(14, 2.2))
        ax.axis("off")

        # Create table
        table = ax.table(
            cellText=data,
            colLabels=columns,
            cellLoc="center",
            loc="center",
            bbox=[0, 0, 1, 1],
        )

        # Style the table
        table.auto_set_font_size(False)
        table.set_fontsize(11)
        table.scale(1, 1.8)

        # Header row styling (cyan background)
        for i in range(len(columns)):
            cell = table[(0, i)]
            cell.set_facecolor("#7dd3fc")  # cyan-300 equivalent
            cell.set_text_props(weight="bold", color="black", fontsize=12)
            cell.set_edgecolor("#9ca3af")  # gray-400
            cell.set_linewidth(1)

        # Find the column index for the category (if provided)
        category_index = None
        if category and category in columns:
            category_index = columns.index(category)

        # Data row styling
        for i in range(len(data[0])):
            cell = table[(1, i)]
            if i == 0:  # Age column
                cell.set_facecolor("#a5f3fc")  # cyan-200
                cell.set_text_props(weight="semibold", color="black", fontsize=11)
            else:
                cell.set_facecolor("#f3f4f6")  # gray-100
                cell.set_text_props(color="black", fontsize=10)
                # Bold the cell that corresponds to the patient's category
                if category_index is not None and i == category_index:
                    cell.set_text_props(weight="bold", color="black", fontsize=11)
            cell.set_edgecolor("#9ca3af")  # gray-400
            cell.set_linewidth(1)

        # Add arrow indicator below the category column
        if category_index is not None:
            # Calculate position
            cell_width = 1.0 / len(columns)
            arrow_x = (category_index + 0.5) * cell_width

            # Draw arrow pointing up
            arrow = FancyArrowPatch(
                (arrow_x, -0.15),
                (arrow_x, -0.05),
                arrowstyle="->",
                mutation_scale=20,
                linewidth=2,
                color="black",
                transform=ax.transAxes,
            )
            ax.add_patch(arrow)

            # Add triangle at the top
            triangle = RegularPolygon(
                (arrow_x, -0.05),
                3,
                radius=0.02,
                orientation=np.pi / 2,
                color="black",
                transform=ax.transAxes,
            )
            ax.add_patch(triangle)

        # Set title - calculate approximate percentile
        if vo2_max_value is not None:
            if category == "Superior":
                percentile = "100th percentile"
            else:
                percentile_map = {
                    "Very Poor": "1st-10th percentile",
                    "Poor": "10th-20th percentile",
                    "Fair": "20th-40th percentile",
                    "Good": "40th-60th percentile",
                    "Excellent": "60th-80th percentile",
                }
                percentile = percentile_map.get(category, "N/A")

            title = f"VO2 Max - {vo2_max_value:.1f} ({percentile})"
            ax.set_title(title, fontsize=14, fontweight="bold", pad=10)

        if save_as_base64:
            buf = io.BytesIO()
            plt.savefig(
                buf,
                format="png",
                bbox_inches="tight",
                dpi=300,
                facecolor="white",
                pad_inches=0.05,
            )
            plt.close(fig)
            buf.seek(0)
            return base64.b64encode(buf.read()).decode("utf-8")
        else:
            output_path = (
                self.charts_dir / f"vo2_max_table_{pd.Timestamp.now().timestamp()}.png"
            )
            plt.savefig(
                output_path,
                bbox_inches="tight",
                dpi=300,
                facecolor="white",
                pad_inches=0.05,
            )
            plt.close(fig)
            return str(output_path)

    def generate_heart_rate_zones_table(
        self,
        data: list[list],
        columns: list[str],
        cell_colors: list[list[str]] = None,
        save_as_base64: bool = True,
    ) -> str:
        """
        Generate Heart Rate Zones table as an image with optimized sizing.

        Args:
            data: List of rows (each row is a list of values)
            columns: List of column headers (Zone 1-5)
            cell_colors: Optional matrix of cell colors (IGNORED - using notebook colors)
            save_as_base64: If True, return base64 string

        Returns:
            Base64 string or file path
        """
        import io

        # Optimal sizing for HR Zones table (5 columns, 8 rows) - match notebook exactly
        fig, ax = plt.subplots(figsize=(12, 8))
        ax.axis("off")

        # Data comes pre-formatted with newlines from context_generator - use as-is
        # No text wrapping needed

        # Create table without rowLabels - match notebook exactly
        table = ax.table(
            cellText=data,
            colLabels=columns,
            loc="center",
            cellLoc="center",
        )

        # Style the table - match notebook exactly
        table.auto_set_font_size(False)
        table.set_fontsize(10)
        table.scale(1, 3.5)  # Increased vertical scale for multi-line text

        # Header row styling
        for j, label in enumerate(columns):
            cell = table[(0, j)]
            cell.set_facecolor("#7dd3fc")  # cyan-300
            cell.set_text_props(weight="bold")

        # Row specific styling - match notebook colors exactly
        colors = ["#fecaca", "#fecaca", "#fef08a", "#bbf7d0", "#bbf7d0"]

        # HR BPM row is at index 2 (0-based in data) -> row 3 in table (0 is header)
        for j in range(len(columns)):
            cell = table[(3, j)]
            cell.set_facecolor(colors[j])
            cell.set_text_props(weight="bold")

        # Breathing row is at index 7 -> row 8 in table
        for j in range(len(columns)):
            cell = table[(8, j)]
            cell.set_facecolor(colors[j])
            cell.set_text_props(weight="bold")

        # Add title matching notebook
        plt.title(
            "Personalized Heart Rate Zones", fontsize=16, fontweight="bold", pad=5
        )
        plt.tight_layout()

        if save_as_base64:
            buf = io.BytesIO()
            plt.savefig(
                buf,
                format="png",
                bbox_inches="tight",
                dpi=300,
                facecolor="white",
            )
            plt.close(fig)
            buf.seek(0)
            return base64.b64encode(buf.read()).decode("utf-8")
        else:
            output_path = (
                self.charts_dir / f"hr_zones_table_{pd.Timestamp.now().timestamp()}.png"
            )
            plt.savefig(
                output_path,
                bbox_inches="tight",
                dpi=300,
                facecolor="white",
            )
            plt.close(fig)
            return str(output_path)

    def generate_resting_heart_rate_table(
        self,
        data: list[list],
        columns: list[str],
        rhr_value: float = None,
        category: str = None,
        cell_colors: list[list[str]] = None,
        save_as_base64: bool = True,
    ) -> str:
        """
        Generate Resting Heart Rate table as an image with optimized sizing, highlighting the patient's category.

        Args:
            data: List of rows (each row is a list of values)
            columns: List of column headers
            rhr_value: Patient's resting heart rate value in bpm (for title and arrow)
            category: Category that the patient falls into (e.g., 'Good', 'Excellent')
            cell_colors: Optional matrix of cell colors
            save_as_base64: If True, return base64 string

        Returns:
            Base64 string or file path
        """
        import io

        from matplotlib.patches import FancyArrowPatch, RegularPolygon

        # Optimal sizing for RHR table (8 columns, 1 data row)
        fig, ax = plt.subplots(figsize=(16, 2.2))
        ax.axis("off")

        # Create table
        table = ax.table(
            cellText=data,
            colLabels=columns,
            cellLoc="center",
            loc="center",
            bbox=[0, 0, 1, 1],
        )

        # Style the table
        table.auto_set_font_size(False)
        table.set_fontsize(11)
        table.scale(1, 1.8)

        # Header row styling (cyan background)
        for i in range(len(columns)):
            cell = table[(0, i)]
            cell.set_facecolor("#7dd3fc")  # cyan-300 equivalent
            cell.set_text_props(weight="bold", color="black", fontsize=12)
            cell.set_edgecolor("#9ca3af")  # gray-400
            cell.set_linewidth(1)

        # Find the column index for the category (if provided)
        category_index = None
        if category and category in columns:
            category_index = columns.index(category)

        # Data row styling
        for i in range(len(data[0])):
            cell = table[(1, i)]
            if i == 0:  # Age column
                cell.set_facecolor("#a5f3fc")  # cyan-200
                cell.set_text_props(weight="semibold", color="black", fontsize=11)
            else:
                # Highlight the category cell with light green background
                if category_index is not None and i == category_index:
                    cell.set_facecolor("#d1fae5")  # green-200 equivalent
                    cell.set_text_props(weight="bold", color="black", fontsize=11)
                else:
                    cell.set_facecolor("#f3f4f6")  # gray-100
                    cell.set_text_props(color="black", fontsize=10)
            cell.set_edgecolor("#9ca3af")  # gray-400
            cell.set_linewidth(1)

        # Add arrow indicator below the category column
        if category_index is not None:
            # Calculate position
            cell_width = 1.0 / len(columns)
            arrow_x = (category_index + 0.5) * cell_width

            # Draw arrow pointing up
            arrow = FancyArrowPatch(
                (arrow_x, -0.15),
                (arrow_x, -0.05),
                arrowstyle="->",
                mutation_scale=20,
                linewidth=2,
                color="black",
                transform=ax.transAxes,
            )
            ax.add_patch(arrow)

            # Add triangle at the top
            triangle = RegularPolygon(
                (arrow_x, -0.05),
                3,
                radius=0.02,
                orientation=np.pi / 2,
                color="black",
                transform=ax.transAxes,
            )
            ax.add_patch(triangle)

        # Set title
        if rhr_value is not None:
            title = f"Resting Heart Rate - {rhr_value:.0f}bpm"
            ax.set_title(title, fontsize=14, fontweight="bold", pad=10)

        if save_as_base64:
            buf = io.BytesIO()
            plt.savefig(
                buf,
                format="png",
                bbox_inches="tight",
                dpi=300,
                facecolor="white",
                pad_inches=0.05,
            )
            plt.close(fig)
            buf.seek(0)
            return base64.b64encode(buf.read()).decode("utf-8")
        else:
            output_path = (
                self.charts_dir / f"rhr_table_{pd.Timestamp.now().timestamp()}.png"
            )
            plt.savefig(
                output_path,
                bbox_inches="tight",
                dpi=300,
                facecolor="white",
                pad_inches=0.05,
            )
            plt.close(fig)
            return str(output_path)