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import os
import gradio as gr
import pandas as pd
DB_USER = os.getenv("DB_USER")
DB_PASSWORD = os.getenv("DB_PASSWORD")
DB_HOST = os.getenv("DB_HOST")
PORT = 8080
DB_NAME = "bikeshare"
connection_string = (
f"postgresql://{DB_USER}:{DB_PASSWORD}@{DB_HOST}?port={PORT}&dbname={DB_NAME}"
)
def get_count_ride_type():
df = pd.read_sql(
"""
SELECT COUNT(ride_id) as n, rideable_type
FROM rides
GROUP BY rideable_type
ORDER BY n DESC
""",
con=connection_string,
)
return df
def get_most_popular_stations():
df = pd.read_sql(
"""
SELECT COUNT(ride_id) as n, MAX(start_station_name) as station
FROM RIDES
WHERE start_station_name is NOT NULL
GROUP BY start_station_id
ORDER BY n DESC
LIMIT 5
""",
con=connection_string,
)
return df
with gr.Blocks() as demo:
gr.Markdown(
"""
# Chicago Bike Share Dashboard
This demo pulls Chicago bike share data for March 2022 from a postgresql database hosted on AWS.
This demo uses psycopg2 but any postgresql client library (SQLAlchemy)
is compatible with gradio.
Connection credentials are handled by environment variables
defined as secrets in the Space.
If data were added to the database, the plots in this demo would update
whenever the webpage is reloaded.
This demo serves as a starting point for your database-connected apps!
"""
)
with gr.Row():
bike_type = gr.BarPlot(
x="rideable_type",
y='n',
title="Number of rides per bicycle type",
y_title="Number of Rides",
x_title="Bicycle Type",
vertical=False,
tooltip=['rideable_type', "n"],
height=300,
width=300,
)
station = gr.BarPlot(
x='station',
y='n',
title="Most Popular Stations",
y_title="Number of Rides",
x_title="Station Name",
vertical=False,
tooltip=['station', 'n'],
height=300,
width=300
)
demo.load(get_count_ride_type, inputs=None, outputs=bike_type)
demo.load(get_most_popular_stations, inputs=None, outputs=station)
if __name__ == "__main__":
demo.launch()
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