|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
""" |
|
|
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 |
|
|
""" |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
import gradio as gr |
|
|
import matplotlib.pyplot as plt |
|
|
import numpy as np |
|
|
from datasets import load_from_disk |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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)) |
|
|
} |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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. |
|
|
""" |
|
|
|
|
|
dataset_path = input('Enter dataset path: ') |
|
|
ds = load_from_disk(dataset_path) |
|
|
|
|
|
|
|
|
paragraphs_arr = np.array(ds['paragraphs'], dtype=int) |
|
|
words_arr = np.array(ds['words'], dtype=int) |
|
|
articles_arr = np.array(ds['articles'], dtype=int) |
|
|
|
|
|
|
|
|
stats = { |
|
|
'paragraphs': compute_stats(paragraphs_arr), |
|
|
'words': compute_stats(words_arr), |
|
|
'articles': compute_stats(articles_arr) |
|
|
} |
|
|
|
|
|
|
|
|
par_plot_obj = make_histogram(paragraphs_arr, 'Paragraphs') |
|
|
words_plot_obj = make_histogram(words_arr, 'Words') |
|
|
articles_plot_obj = make_histogram(articles_arr, 'Articles') |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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() |
|
|
|
|
|
|
|
|
|
|
|
|
