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import gradio as gr
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
from sklearn.decomposition import LatentDirichletAllocation
from sklearn.feature_extraction.text import CountVectorizer
def concat_comments(sup_comment: list[str], comment: list[str]) -> list[str]:
format_s = "{s}\n{c}"
return [
format_s.format(s=s, c=c) for s, c in zip(sup_comment, comment)
]
def main(button, chose_context):
df = pd.read_csv('./data/results.csv', index_col=0)
print(chose_context)
data = concat_comments(df.sup_comment, df.comment)
subreddits = df.subreddit.value_counts().index[:22]
weight_counts = {
t: [
df[df.Topic_key_word == t].subreddit.value_counts()[subreddit] / df.subreddit.value_counts()[subreddit] for subreddit in subreddits
] for t in topics
}
irony_percs = {
t: [
len(
df[df.subreddit == subreddit][(df[df.subreddit == subreddit].Topic_key_word == t) & (df[df.subreddit == subreddit].label == 1)]
) /
len(
df[df.subreddit == subreddit]
) for subreddit in subreddits
] for t in topics
}
width = 0.9
fig, ax = plt.subplots(figsize = (10, 7))
plt.axhline(0.5, color = 'red', ls=":", alpha = .3)
bottom = np.zeros(len(subreddits))
for k, v in weight_counts.items():
p = ax.bar(subreddits, v, width, label=k, bottom=bottom)
ax.bar(subreddits, irony_percs[k], width - 0.01, bottom=bottom, color = 'black', edgecolor = 'white', alpha = .2, hatch = '\\')
bottom += v
ax.set_title("Perc of topics for each subreddit")
ax.legend(loc="upper right")
plt.xticks(rotation=70)
plt.show()
with gr.Blocks() as demo:
button = gr.Radio(
label="Plot type",
choices=['scatter_plot', 'heatmap', 'us_map', 'interactive_barplot', "radial", "multiline"], value='scatter_plot'
)
chose_context = gr.Radio(
label="Context LDA",
choices=['comment', 'sup comment', 'sup comment + comment'], value='scatter_plot'
)
plot = gr.Plot(label="Plot")
button.change(main, inputs=[button, chose_context], outputs=[plot])
demo.load(main, inputs=[button], outputs=[plot])
# iface = gr.Interface(fn=greet, inputs="text", outputs="text")
if __name__ == "__main__":
demo.launch()
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