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#                                                           #
#   This file was created by: Alberto Palomo Alonso         #
# Universidad de Alcalá - Escuela Politécnica Superior      #
#                                                           #
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"""
Gradio-based explorer for inspecting a segmented Wikipedia dataset.

Main features:
- Load a Hugging Face dataset from disk.
- Compute global statistics for paragraphs, words, and articles.
- Precompute histograms for dataset-level distributions.
- Provide an interactive Gradio UI to browse individual samples and
  visualize global statistics.

Expected dataset fields:
- id
- text (list of paragraphs/segments)
- paragraphs
- words
- articles
- title
"""

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#                       IMPORT STATEMENTS                   #
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import gradio as gr
import matplotlib.pyplot as plt
import numpy as np
from datasets import load_from_disk


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#                     STATISTICS UTILITIES                  #
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def compute_stats(arr: np.ndarray) -> dict:
    """
    Compute basic descriptive statistics for a numeric array.

    Args:
        arr (np.ndarray):
            Input array of numeric values.

    Returns:
        dict:
            Dictionary containing mean, median, standard deviation (sample),
            minimum, and maximum values.
    """
    return {
        'mean': float(np.mean(arr)),
        'median': float(np.median(arr)),
        'std': float(np.std(arr, ddof=1)),
        'min': int(np.min(arr)),
        'max': int(np.max(arr))
    }


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#                       PLOTTING UTILITIES                  #
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def make_histogram(arr: np.ndarray, title: str):
    """
    Create a histogram plot for a numeric array.

    Args:
        arr (np.ndarray):
            Input array of numeric values.
        title (str):
            Title label for the histogram (used in title and x-axis).

    Returns:
        matplotlib.figure.Figure:
            Matplotlib figure object containing the histogram.
    """
    fig, ax = plt.subplots()
    ax.hist(arr, bins=30)
    ax.set_title(f"Distribution of {title}")
    ax.set_xlabel(title)
    ax.set_ylabel("Count")
    fig.tight_layout()
    return fig


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#                           MAIN                            #
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if __name__ == '__main__':
    """
    Script entry point.

    Loads a dataset from disk, computes global statistics and histograms,
    and launches a Gradio UI to interactively explore dataset samples.
    """
    # Load dataset
    dataset_path = input('Enter dataset path: ')
    ds = load_from_disk(dataset_path)

    # Extract numeric arrays
    paragraphs_arr = np.array(ds['paragraphs'], dtype=int)
    words_arr = np.array(ds['words'], dtype=int)
    articles_arr = np.array(ds['articles'], dtype=int)

    # Compute global statistics
    stats = {
        'paragraphs': compute_stats(paragraphs_arr),
        'words': compute_stats(words_arr),
        'articles': compute_stats(articles_arr)
    }

    # Precompute histogram figures
    par_plot_obj = make_histogram(paragraphs_arr, 'Paragraphs')
    words_plot_obj = make_histogram(words_arr, 'Words')
    articles_plot_obj = make_histogram(articles_arr, 'Articles')

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    #                    GRADIO CALLBACK                      #
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    def show(idx: int):
        """
        Retrieve and format a single dataset sample for display.

        Args:
            idx (int):
                Index of the document in the dataset.

        Returns:
            tuple[str, str]:
                - Formatted sample text and metadata.
                - Formatted global statistics and current sample information.
        """
        sample = ds[int(idx)]
        texto = "\n\n".join(
            [f"{i}: {p}" for i, p in enumerate(sample["text"])]
        )

        sample_info = (
            f"Doc ID: {sample['id']}"
            f"\n\n{texto}"
        )

        stats_text = (
            "Global Dataset Statistics:\n"
            f"Paragraphs \t- mean: {stats['paragraphs']['mean']:.2f}, "
            f"std: {stats['paragraphs']['std']:.2f}, "
            f"min: {stats['paragraphs']['min']}, "
            f"max: {stats['paragraphs']['max']}\n"
            f"Words      \t- mean: {stats['words']['mean']:.2f}, "
            f"std: {stats['words']['std']:.2f}, "
            f"min: {stats['words']['min']}, "
            f"max: {stats['words']['max']}\n"
            f"Articles   \t- mean: {stats['articles']['mean']:.2f}, "
            f"std: {stats['articles']['std']:.2f}, "
            f"min: {stats['articles']['min']}, "
            f"max: {stats['articles']['max']}\n"
            f"\nCurrent Sample Information:\n"
            f"\t- Doc ID: {sample['id']}\n"
            f"\t- Paragraphs: {sample['paragraphs']}\n"
            f"\t- Words: {sample['words']}\n"
            f"\t- Articles: {sample['articles']}\n"
            f"\t- Titles: {sample['title']}"
        )
        return sample_info, stats_text

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    #                       GRADIO UI                          #
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    with gr.Blocks(title="Wikipedia Extractor Explorer") as demo:
        gr.Markdown("## Wikipedia Segmentation Explorer")

        idx_slider = gr.Slider(
            0, len(ds) - 1, step=1, label="Document Index"
        )

        with gr.Row():
            with gr.Column(scale=1):
                sample_output = gr.Textbox(
                    label="Sample Info", lines=20
                )
                stats_output = gr.Textbox(
                    label="Global Statistics", lines=6
                )
            with gr.Column(scale=1):
                gr.Plot(
                    label="Paragraphs Histogram",
                    value=par_plot_obj
                )
                gr.Plot(
                    label="Words Histogram",
                    value=words_plot_obj
                )
                gr.Plot(
                    label="Articles Histogram",
                    value=articles_plot_obj
                )

        idx_slider.change(
            fn=show,
            inputs=idx_slider,
            outputs=[sample_output, stats_output]
        )

    demo.launch()
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#                        END OF FILE                        #
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