# main.py

# A demonstration of interactive visualization using Bokeh.

#

# Before running, you may need to install bokeh, pandas, and scikit-learn:

# pip install bokeh pandas scikit-learn

import pandas as pd

from sklearn.datasets import load_iris

from bokeh.plotting import figure, show, output_file

from bokeh.models import HoverTool, ColumnDataSource

print("--- Starting Interactive Visualization Demonstration with Bokeh ---")

# --- Section 1: Load and Prepare the Dataset ---

# We will use the Iris dataset, which has multiple numerical features

# and a categorical target, making it great for visualization.

print("\n--- 1. Loading and Preparing the Iris Dataset ---")

iris = load_iris()

df = pd.DataFrame(data=iris.data, columns=iris.feature_names)

df['species'] = pd.Categorical.from_codes(iris.target, iris.target_names)

# Bokeh works best with its own ColumnDataSource for interactive features.

source = ColumnDataSource(df)

print("Dataset loaded and prepared for Bokeh.")

# --- Section 2: Create an Interactive Scatter Plot ---

# We will create a scatter plot of petal length vs. petal width.

# The plot will include interactive tools for panning, zooming, and hovering.

print("\n--- 2. Generating an Interactive Scatter Plot ---")

try:

    # Define the tools for the plot

    # HoverTool allows us to display information when the user hovers over a data point.

    # The tooltips are defined as a list of (label, @column_name) tuples.

    hover = HoverTool(

        tooltips=[

            ("Species", "@species"),

            ("Petal Length", "@{petal length (cm)}"),

            ("Petal Width", "@{petal width (cm)}")

        ]

    )

    # Create a figure object with tools.

    p = figure(

        width=800, height=500,

        title="Iris Dataset: Petal Length vs. Petal Width",

        x_axis_label='Petal Length (cm)',

        y_axis_label='Petal Width (cm)',

        tools=['pan', 'wheel_zoom', 'box_zoom', 'reset', 'save', hover]

    )

    # Add a scatter glyph to the plot.

    # We use the 'source' object we created earlier.

    p.scatter(

        x='petal length (cm)',

        y='petal width (cm)',

        source=source,

        legend_field='species', # Color the points by species

        size=10,

        alpha=0.7

    )

    # Customize the plot's appearance

    p.legend.location = "top_left"

    p.legend.title = "Species"

    p.legend.click_policy = "hide" # Allows hiding species by clicking the legend

    # Define the output file

    output_filename = 'interactive_iris_plot.html'

    output_file(output_filename, title="Iris Interactive Plot")

    print(f"Interactive plot will be saved to '{output_filename}'")

    # Show the results

    show(p)

except Exception as e:

    print(f"An error occurred during visualization: {e}")

print("\n--- End of Demonstration ---")