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import gradio as gr
import pandas as pd
import plotly.graph_objects as go
def get_covered_languages():
#Load data
all_languages = pd.read_csv('data/merged_language_list_with_duplicates.csv')
with open("data/covered_languages.txt") as f:
covered_languages = f.read().splitlines()
# Split strings with commas and flatten the list
covered_languages = [lang.strip() for sublist in covered_languages for lang in sublist.split(',')]
covered_languages = list(set(covered_languages))
# Get language codes
covered_language_codes = [all_languages.loc[all_languages['Language'] == lang, 'Code'].values[0] for lang in covered_languages if lang in all_languages['Language'].values]
assert len(covered_language_codes) == len(covered_languages), "Mismatch between covered languages and their codes"
return covered_language_codes
def build_dataframes(covered_language_codes):
# Load data
clean_languages = pd.read_csv('data/merged_language_list_clean.csv')
# Create a dataframe for languages with a lead
languages_with_lead = clean_languages[clean_languages['Code'].isin(covered_language_codes)].sort_values(by='Code')
languages_with_lead.to_csv('data/languages_with_lead.csv', index=False)
# Create a dataframe for languages without a lead
languages_without_lead = clean_languages[~clean_languages['Code'].isin(covered_language_codes)].sort_values(by='Code')
return languages_with_lead, languages_without_lead
def create_piechart():
colors = ['#ffd21e', '#0b4a70']
fig = go.Figure(
go.Pie(
labels=["With lead", "Without lead"],
values=[len(languages_with_lead), len(languages_without_lead)],
marker=dict(colors=colors)
)
)
fig.update_traces(textposition='inside', textinfo='label+value')
return fig
def filter_dataframes(search_term=None):
if search_term:
search_terms = search_term.lower().split(" ")
filtered_with_lead = languages_with_lead[
languages_with_lead.apply(lambda row: any(term in str(row['Language']).lower() or term in str(row['Code']).lower() for term in search_terms), axis=1)
]
filtered_without_lead = languages_without_lead[
languages_without_lead.apply(lambda row: any(term in str(row['Language']).lower() or term in str(row['Code']).lower() for term in search_terms), axis=1)
]
return filtered_without_lead, filtered_with_lead
else:
return languages_without_lead, languages_with_lead
def load_demo():
languages_with_lead, languages_without_lead = build_dataframes(get_covered_languages())
piechart = create_piechart()
return languages_without_lead,languages_with_lead,piechart
with gr.Blocks() as demo:
gr.Markdown("## Language Leads Dashboard")
languages_with_lead, languages_without_lead = build_dataframes(get_covered_languages())
gr_piechart = gr.Plot(label="Language Leads")
search_box = gr.Textbox(type="text", label="Search your language:")
with gr.Row():
search_button = gr.Button("Search 🔎")
reset_button = gr.Button("Reset 🔁")
with gr.Tab("Looking for leads!"):
gr.Markdown("These languages don't have a lead yet! Would you like to lead one of them? Sign up using [this form](https://forms.gle/mFCMXNRjxvyFvW5q9).")
gr_languages_without_lead = gr.DataFrame()
with gr.Tab("Languages with leads"):
gr.Markdown("We found at least one lead for these languages:")
gr_languages_with_lead = gr.DataFrame()
demo.load(
load_demo,
outputs=[gr_languages_without_lead, gr_languages_with_lead, gr_piechart],
)
search_button.click(
fn=filter_dataframes,
inputs=search_box,
outputs=[gr_languages_without_lead,gr_languages_with_lead]
)
reset_button.click(
fn=filter_dataframes,
inputs=None,
outputs=[gr_languages_without_lead,gr_languages_with_lead]
)
demo.launch() |