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import gradio as gr
from datasets import load_dataset
from nltk.util import ngrams
from collections import Counter
# Load the dataset and convert it to a Pandas dataframe
sotu_dataset = 'jsulz/state-of-the-union-addresses'
dataset = load_dataset(sotu_dataset)
df = dataset['train'].to_pandas()
df['word_count'] = df['speech_html'].apply(lambda x: len(x.split()))
written = df[df['categories'] == 'Written']
spoken = df[df['categories'] == 'Spoken']
# Create a Gradio interface with blocks
with gr.Blocks() as demo:
gr.Markdown(
"""
# A Dashboard to Analyze the State of the Union Addresses
""")
# get all unique president names
presidents = df['potus'].unique()
# convert presidents to a list
presidents = presidents.tolist()
# create a dropdown to select a president
president = gr.Dropdown(label="Select a President", choices=["All"] + presidents)
with gr.Row():
# if president is not of type string
@gr.render(inputs=president)
def show_text(potus):
if potus is not None:
gr.Markdown(f"{potus} was the first president of the United States.")
@gr.render(inputs=president)
def word_length_bar(potus):
# calculate the total number of words in the speech_html column and add it to a new column
# if the president is "All", show the word count for all presidents
if potus == "All":
gr.BarPlot(df, x="date", y="word_count", title="Total Number of Words in the Speeches")
else:
# if the president is not "All", show the word count for the selected president
gr.BarPlot(df[df['potus'] == potus], x="date", y="word_count", title="Total Number of Words in the Speeches")
with gr.Row():
@gr.render(inputs=president)
def ngram_bar(potus):
# create a Counter object from the trigrams
potus_df = df[df["potus"] == potus]
trigrams = (
potus_df["tokens-nostop"].apply(lambda x: list(ngrams(x, 3))).apply(Counter).sum()
)
# get the most common trigrams
common_trigrams = trigrams.most_common(20)
# unzip the list of tuples and plot the trigrams and counts as a bar chart
trigrams, counts = zip(*common_trigrams)
# join the trigrams into a single string
trigrams = [" ".join(trigram) for trigram in trigrams]
# create a dataframe from the trigrams and counts
trigrams_df = pd.DataFrame({"trigrams": trigrams, "counts": counts})
# plot the trigrams and counts as a bar chart
gr.BarPlot(trigrams_df, x="trigrams", y="counts", title="Most Common Trigrams")
demo.launch()
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