diff --git a/plots/bokeh/custom/pie-basic/default.py b/plots/bokeh/custom/pie-basic/default.py
index bf784be68d..294b3671d5 100644
--- a/plots/bokeh/custom/pie-basic/default.py
+++ b/plots/bokeh/custom/pie-basic/default.py
@@ -4,18 +4,15 @@
 """
 
 import math
-from typing import TYPE_CHECKING
 
 import pandas as pd
+from bokeh.io import export_png
 from bokeh.models import ColumnDataSource, Label, Legend, LegendItem
 from bokeh.plotting import figure
 
 
-if TYPE_CHECKING:
-    from bokeh.plotting import figure as Figure
-
 # PyPlots.ai style colors
-PYPLOTS_COLORS = [
+COLORS = [
     "#306998",  # Python Blue
     "#FFD43B",  # Python Yellow
     "#DC2626",  # Signal Red
@@ -24,198 +21,103 @@
     "#F97316",  # Orange
 ]
 
-
-def create_plot(
-    data: pd.DataFrame,
-    category: str,
-    value: str,
-    title: str | None = None,
-    colors: list[str] | None = None,
-    startangle: float = 90,
-    legend: bool = True,
-    legend_loc: str = "right",
-    **kwargs,
-) -> "Figure":
-    """
-    Create a basic pie chart using Bokeh wedge glyphs.
-
-    Bokeh does not have a native pie chart method, so this implementation
-    uses wedge glyphs to construct the pie chart manually.
-
-    Args:
-        data: Input DataFrame containing category and value columns
-        category: Column name for category labels (slice names)
-        value: Column name for numeric values (slice sizes)
-        title: Plot title (optional)
-        colors: Custom color palette for slices (defaults to PyPlots colors)
-        startangle: Starting angle for first slice in degrees (default: 90)
-        legend: Whether to display legend (default: True)
-        legend_loc: Legend location - 'right', 'left', 'above', 'below' (default: 'right')
-        **kwargs: Additional parameters passed to figure
-
-    Returns:
-        Bokeh figure object
-
-    Raises:
-        ValueError: If data is empty or values are all zero/negative
-        KeyError: If required columns not found in data
-
-    Example:
-        >>> data = pd.DataFrame({
-        ...     'category': ['A', 'B', 'C'],
-        ...     'value': [30, 50, 20]
-        ... })
-        >>> fig = create_plot(data, 'category', 'value', title='Distribution')
-    """
-    # Input validation
-    if data.empty:
-        raise ValueError("Data cannot be empty")
-
-    for col in [category, value]:
-        if col not in data.columns:
-            available = ", ".join(data.columns)
-            raise KeyError(f"Column '{col}' not found. Available: {available}")
-
-    # Validate numeric values
-    if not pd.api.types.is_numeric_dtype(data[value]):
-        raise ValueError(f"Column '{value}' must contain numeric values")
-
-    if (data[value] < 0).any():
-        raise ValueError("Pie chart values must be non-negative")
-
-    total = data[value].sum()
-    if total == 0:
-        raise ValueError("Sum of values cannot be zero")
-
-    # Prepare data
-    plot_data = data.copy()
-    plot_data["angle"] = plot_data[value] / total * 2 * math.pi
-    plot_data["percentage"] = plot_data[value] / total * 100
-
-    # Calculate cumulative angles for wedge positioning
-    plot_data["end_angle"] = plot_data["angle"].cumsum()
-    plot_data["start_angle"] = plot_data["end_angle"] - plot_data["angle"]
-
-    # Apply start angle offset (convert degrees to radians, adjust for Bokeh's coordinate system)
-    start_rad = math.radians(startangle - 90)
-    plot_data["start_angle"] = plot_data["start_angle"] + start_rad
-    plot_data["end_angle"] = plot_data["end_angle"] + start_rad
-
-    # Assign colors
-    if colors is None:
-        colors = PYPLOTS_COLORS
-    # Cycle through colors if more categories than colors
-    num_categories = len(plot_data)
-    plot_data["color"] = [colors[i % len(colors)] for i in range(num_categories)]
-
-    # Create ColumnDataSource
-    source = ColumnDataSource(plot_data)
-
-    # Create figure - use range to ensure circular aspect ratio
-    # Set frame dimensions to maintain 16:9 overall but circular pie
-    fig_width = kwargs.get("width", 1600)
-    fig_height = kwargs.get("height", 900)
-
-    p = figure(
-        width=fig_width,
-        height=fig_height,
-        title=title,
-        tools="hover",
-        tooltips=[(category.capitalize(), f"@{category}"), ("Value", f"@{value}"), ("Percentage", "@percentage{0.1}%")],
-        x_range=(-1.2, 2.0 if legend else 1.2),
-        y_range=(-1.2, 1.2),
-    )
-
-    # Draw wedges (pie slices)
-    renderers = p.wedge(
-        x=0,
-        y=0,
-        radius=0.9,
-        start_angle="start_angle",
-        end_angle="end_angle",
-        line_color="white",
-        line_width=2,
-        fill_color="color",
-        source=source,
-    )
-
-    # Add percentage labels inside slices
-    for _, row in plot_data.iterrows():
-        # Calculate label position at middle of wedge, 60% from center
-        mid_angle = (row["start_angle"] + row["end_angle"]) / 2
-        label_radius = 0.55
-
-        x = label_radius * math.cos(mid_angle)
-        y = label_radius * math.sin(mid_angle)
-
-        # Only show percentage label if slice is large enough
-        if row["percentage"] >= 5:
-            label = Label(
-                x=x,
-                y=y,
-                text=f"{row['percentage']:.1f}%",
-                text_font_size="14pt",
-                text_align="center",
-                text_baseline="middle",
-                text_color="white" if row["percentage"] >= 10 else "black",
-            )
-            p.add_layout(label)
-
-    # Configure legend
-    if legend:
-        legend_items = []
-        for i, cat in enumerate(plot_data[category]):
-            legend_items.append(LegendItem(label=str(cat), renderers=[renderers], index=i))
-
-        leg = Legend(
-            items=legend_items,
-            location="center",
-            label_text_font_size="16pt",
-            background_fill_color="white",
-            background_fill_alpha=1.0,
-            border_line_color="black",
-            border_line_width=1,
+# Data
+data = pd.DataFrame(
+    {"category": ["Product A", "Product B", "Product C", "Product D", "Other"], "value": [35, 25, 20, 15, 5]}
+)
+
+# Calculate angles for pie slices
+total = data["value"].sum()
+data["angle"] = data["value"] / total * 2 * math.pi
+data["percentage"] = data["value"] / total * 100
+
+# Calculate cumulative angles for wedge positioning
+data["end_angle"] = data["angle"].cumsum()
+data["start_angle"] = data["end_angle"] - data["angle"]
+
+# Apply start angle offset (start from top, 90 degrees)
+start_rad = math.radians(90 - 90)  # Adjust for Bokeh coordinate system
+data["start_angle"] = data["start_angle"] + start_rad
+data["end_angle"] = data["end_angle"] + start_rad
+
+# Assign colors (cycle if more categories than colors)
+data["color"] = [COLORS[i % len(COLORS)] for i in range(len(data))]
+
+# Create ColumnDataSource
+source = ColumnDataSource(data)
+
+# Create figure - 4800 x 2700 px as per style guide
+p = figure(
+    width=4800,
+    height=2700,
+    title="Market Share Distribution",
+    tools="hover",
+    tooltips=[("Category", "@category"), ("Value", "@value"), ("Percentage", "@percentage{0.1}%")],
+    x_range=(-1.2, 2.0),
+    y_range=(-1.2, 1.2),
+)
+
+# Draw wedges (pie slices)
+renderers = p.wedge(
+    x=0,
+    y=0,
+    radius=0.9,
+    start_angle="start_angle",
+    end_angle="end_angle",
+    line_color="white",
+    line_width=2,
+    fill_color="color",
+    source=source,
+)
+
+# Add percentage labels inside slices
+for _, row in data.iterrows():
+    mid_angle = (row["start_angle"] + row["end_angle"]) / 2
+    label_radius = 0.55
+
+    x = label_radius * math.cos(mid_angle)
+    y = label_radius * math.sin(mid_angle)
+
+    # Only show label if slice is large enough
+    if row["percentage"] >= 5:
+        label = Label(
+            x=x,
+            y=y,
+            text=f"{row['percentage']:.1f}%",
+            text_font_size="48pt",
+            text_align="center",
+            text_baseline="middle",
+            text_color="white" if row["percentage"] >= 10 else "black",
         )
-
-        p.add_layout(leg, legend_loc)
-
-    # Style configuration
-    p.axis.visible = False
-    p.grid.visible = False
-    p.outline_line_color = None
-
-    # Title styling
-    if title:
-        p.title.text_font_size = "20pt"
-        p.title.align = "center"
-
-    # Background
-    p.background_fill_color = "white"
-
-    return p
-
-
-if __name__ == "__main__":
-    # Sample data for testing
-    sample_data = pd.DataFrame(
-        {"category": ["Product A", "Product B", "Product C", "Product D", "Other"], "value": [35, 25, 20, 15, 5]}
-    )
-
-    # Create plot
-    fig = create_plot(sample_data, "category", "value", title="Market Share Distribution")
-
-    # Save - try PNG first, fall back to HTML if selenium not available
-    try:
-        from bokeh.io import export_png
-
-        export_png(fig, filename="plot.png")
-        print("Plot saved to plot.png")
-    except RuntimeError as e:
-        if "selenium" in str(e).lower():
-            from bokeh.io import output_file, save
-
-            output_file("plot.html")
-            save(fig)
-            print("Plot saved to plot.html (selenium not available for PNG export)")
-        else:
-            raise
+        p.add_layout(label)
+
+# Create legend
+legend_items = []
+for i, cat in enumerate(data["category"]):
+    legend_items.append(LegendItem(label=str(cat), renderers=[renderers], index=i))
+
+leg = Legend(
+    items=legend_items,
+    location="center",
+    label_text_font_size="48pt",
+    background_fill_color="white",
+    background_fill_alpha=1.0,
+    border_line_color="black",
+    border_line_width=1,
+)
+p.add_layout(leg, "right")
+
+# Style configuration
+p.axis.visible = False
+p.grid.visible = False
+p.outline_line_color = None
+
+# Title styling
+p.title.text_font_size = "60pt"
+p.title.align = "center"
+
+# Background
+p.background_fill_color = "white"
+
+# Save
+export_png(p, filename="plot.png")