Connecting to a Database

Introduction

This guide explains how you can use Gradio to connect your app to a database. We will be connecting to a PostgreSQL database hosted on AWS but gradio is completely agnostic to the type of database you are connecting to and where it’s hosted. So as long as you can write python code to connect to your data, you can display it in a web UI with gradio 💪

Overview

We will be analyzing bike share data from Chicago. The data is hosted on kaggle here. Our goal is to create a dashboard that will enable our business stakeholders to answer the following questions:

1. Are electric bikes more popular than regular bikes?
2. What are the top 5 most popular departure bike stations?

At the end of this guide, we will have a functioning application that looks like this:

Step 1 - Creating your database

We will be storing our data on a PostgreSQL hosted on Amazon’s RDS service. Create an AWS account if you don’t already have one and create a PostgreSQL database on the free tier.

Important: If you plan to host this demo on HuggingFace Spaces, make sure database is on port 8080. Spaces will block all outgoing connections unless they are made to port 80, 443, or 8080 as noted here. RDS will not let you create a postgreSQL instance on ports 80 or 443.

Once your database is created, download the dataset from Kaggle and upload it to your database. For the sake of this demo, we will only upload March 2022 data.

Step 2.a - Write your ETL code

We will be querying our database for the total count of rides split by the type of bicycle (electric, standard, or docked). We will also query for the total count of rides that depart from each station and take the top 5.

We will then take the result of our queries and visualize them in with matplotlib.

We will use the pandas read_sql method to connect to the database. This requires the psycopg2 library to be installed.

In order to connect to our database, we will specify the database username, password, and host as environment variables. This will make our app more secure by avoiding storing sensitive information as plain text in our application files.

import os
import pandas as pd
import matplotlib.pyplot as plt

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
    )
    fig_m, ax = plt.subplots()
    ax.bar(x=df['rideable_type'], height=df['n'])
    ax.set_title("Number of rides by bycycle type")
    ax.set_ylabel("Number of Rides")
    ax.set_xlabel("Bicycle Type")
    return fig_m


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
    )
    fig_m, ax = plt.subplots()
    ax.bar(x=df['station'], height=df['n'])
    ax.set_title("Most popular stations")
    ax.set_ylabel("Number of Rides")
    ax.set_xlabel("Station Name")
    ax.set_xticklabels(
        df['station'], rotation=45, ha="right", rotation_mode="anchor"
    )
    ax.tick_params(axis="x", labelsize=8)
    fig_m.tight_layout()
    return fig_m

If you were to run our script locally, you could pass in your credentials as environment variables like so

DB_USER='username' DB_PASSWORD='password' DB_HOST='host' python app.py

Step 2.c - Write your gradio app

We will display or matplotlib plots in two separate gr.Plot components displayed side by side using gr.Row(). Because we have wrapped our function to fetch the data in a demo.load() event trigger, our demo will fetch the latest data dynamically from the database each time the web page loads. 🪄

import gradio as gr

with gr.Blocks() as demo:
    with gr.Row():
        bike_type = gr.Plot()
        station = gr.Plot()

    demo.load(get_count_ride_type, inputs=None, outputs=bike_type)
    demo.load(get_most_popular_stations, inputs=None, outputs=station)

demo.launch()

Step 3 - Deployment

If you run the code above, your app will start running locally. You can even get a temporary shareable link by passing the share=True parameter to launch.

But what if you want to a permanent deployment solution? Let’s deploy our Gradio app to the free HuggingFace Spaces platform.

If you haven’t used Spaces before, follow the previous guide here. You will have to add the DB_USER, DB_PASSWORD, and DB_HOST variables as “Repo Secrets”. You can do this in the “Settings” tab.

Conclusion

Congratulations! You know how to connect your gradio app to a database hosted on the cloud! ☁️

Our dashboard is now running on Spaces. The complete code is here

As you can see, gradio gives you the power to connect to your data wherever it lives and display however you want! 🔥