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import gradio as gr
import pandas as pd
from widgets.widget_base import Widget
from data_measurements.dataset_statistics import DatasetStatisticsCacheClass as dmt_cls
import utils
logs = utils.prepare_logging(__file__)
class GeneralStats(Widget):
def __init__(self):
self.general_stats = gr.Markdown(render=False)
self.general_stats_top_vocab = gr.DataFrame(render=False)
self.general_stats_missing = gr.Markdown(render=False)
self.general_stats_duplicates = gr.Markdown(render=False)
def render(self):
with gr.TabItem(f"General Text Statistics"):
self.general_stats.render()
self.general_stats_missing.render()
self.general_stats_duplicates.render()
self.general_stats_top_vocab.render()
def update(self, dstats: dmt_cls):
general_stats_text = f"""
Use this widget to check whether the terms you see most represented in the dataset make sense for the goals of the dataset.
There are {str(dstats.total_words)} total words.
There are {dstats.total_open_words} after removing closed class words.
The most common [open class words](https://dictionary.apa.org/open-class-words) and their counts are:
"""
top_vocab = pd.DataFrame(dstats.sorted_top_vocab_df).round(4)
missing_text = (
f"There are {dstats.text_nan_count} missing values in the dataset"
)
if dstats.dups_frac > 0:
dupes_text = f"The dataset is {round(dstats.dups_frac * 100, 2)}% duplicates, For more information about the duplicates, click the 'Duplicates' tab."
else:
dupes_text = "There are 0 duplicate items in the dataset"
return {
self.general_stats: general_stats_text,
self.general_stats_top_vocab: top_vocab,
self.general_stats_missing: missing_text,
self.general_stats_duplicates: dupes_text,
}
@property
def output_components(self):
return [
self.general_stats,
self.general_stats_top_vocab,
self.general_stats_missing,
self.general_stats_duplicates,
]
def add_events(self, state: gr.State):
pass
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