Use Dash to Build to Web Apps on Google Translate Data

Jerod Johnson
Director, Technology Evangelism

Create Python applications that use pandas and Dash to build Google Translate-connected web apps.

The rich ecosystem of Python modules lets you get to work quickly and integrate your systems more effectively. With the CData API Driver for Python, the pandas module, and the Dash framework, you can build Google Translate-connected web applications for Google Translate data. This article shows how to connect to Google Translate with the CData Connector and use pandas and Dash to build a simple web app for visualizing Google Translate data.

With built-in, optimized data processing, the CData Python Connector offers unmatched performance for interacting with live Google Translate data in Python. When you issue complex SQL queries from Google Translate, the driver pushes supported SQL operations, like filters and aggregations, directly to Google Translate and utilizes the embedded SQL engine to process unsupported operations client-side (often SQL functions and JOIN operations).

Connecting to Google Translate Data

Connecting to Google Translate data looks just like connecting to any relational data source. Create a connection string using the required connection properties. For this article, you will pass the connection string as a parameter to the create_engine function.

Authentication

Google Cloud Translation API requires OAuth 2.0 authentication to ensure secure access to translation services, datasets, glossaries, and adaptive MT resources. This authentication method allows you to securely connect to your Google Cloud project and manage translation resources with proper authorization.

OAuth 2.0 Setup and Configuration

Step 1: Create Google Cloud Project and Enable API

To set up OAuth authentication:

Visit the Google Cloud Console
Create a new project or select an existing project
Note down your Project ID (required for all API calls)
Navigate to "APIs & Services" > "Library"
Search for and enable the "Cloud Translation API"
Go to "APIs & Services" > "Credentials"
Click "Create Credentials" and select "OAuth Client ID"
Configure the OAuth consent screen if prompted
Select "Desktop application" or "Web application" as appropriate
Set the authorized redirect URI (CallbackURL)
Copy the Client ID and Client Secret for use in your connection

Required Connection Properties

AuthScheme: Set this to OAuth (required)
OAuthClientId: Client ID from Google Cloud Console (required)
OAuthClientSecret: Client secret from Google Cloud Console (required)
CallbackURL: Redirect URI specified in your OAuth application (required)
InitiateOAuth: Set to GETANDREFRESH for automatic token management (recommended)
ProjectId: Your Google Cloud project ID or project number (required for queries)

Required OAuth Scopes

The Google Cloud Translation API Profile requires the following OAuth scope:

https://www.googleapis.com/auth/cloud-translation - Full access to Cloud Translation API resources including translation, datasets, glossaries, and adaptive MT

After installing the CData Google Translate Connector, follow the procedure below to install the other required modules and start accessing Google Translate through Python objects.

Install Required Modules

Use the pip utility to install the required modules and frameworks:

pip install pandas
pip install dash
pip install dash-daq

Visualize Google Translate Data in Python

Once the required modules and frameworks are installed, we are ready to build our web app. Code snippets follow, but the full source code is available at the end of the article.

First, be sure to import the modules (including the CData Connector) with the following:

import os
import dash
import dash_core_components as dcc
import dash_html_components as html
import pandas as pd
import cdata.api as mod
import plotly.graph_objs as go

You can now connect with a connection string. Use the connect function for the CData Google Translate Connector to create a connection for working with Google Translate data.

cnxn = mod.connect("Profile=C:\profiles\GoogleTranslate.apip;AuthScheme=OAuth;InitiateOAuth=GETANDREFRESH;OAuthClientId=your_client_id;OAuthClientSecret=your_client_secret;CallbackUrl=your_callback_url;")

Execute SQL to Google Translate

Use the read_sql function from pandas to execute any SQL statement and store the result set in a DataFrame.

df = pd.read_sql("SELECT LanguageCode, DisplayName FROM SupportedLanguages WHERE ProjectId = 'my-project-12345'", cnxn)

Configure the Web App

With the query results stored in a DataFrame, we can begin configuring the web app, assigning a name, stylesheet, and title.

app_name = 'dash-apiedataplot'

external_stylesheets = ['https://codepen.io/chriddyp/pen/bWLwgP.css']

app = dash.Dash(__name__, external_stylesheets=external_stylesheets)
app.title = 'CData + Dash'

Configure the Layout

The next step is to create a bar graph based on our Google Translate data and configure the app layout.

trace = go.Bar(x=df.LanguageCode, y=df.DisplayName, name='LanguageCode')

app.layout = html.Div(children=[html.H1("CData Extension + Dash", style={'textAlign': 'center'}),
	dcc.Graph(
		id='example-graph',
		figure={
			'data': [trace],
			'layout':
			go.Layout(title='Google Translate SupportedLanguages Data', barmode='stack')
		})
], className="container")

Set the App to Run

With the connection, app, and layout configured, we are ready to run the app. The last lines of Python code follow.

if __name__ == '__main__':
    app.run_server(debug=True)

Now, use Python to run the web app and a browser to view the Google Translate data.

python api-dash.py

Google Translate data in a Dash web app (Salesforce is shown).

Free Trial & More Information

Download a free, 30-day trial of the CData API Driver for Python to start building Python apps with connectivity to Google Translate data. Reach out to our Support Team if you have any questions.

Full Source Code

import os
import dash
import dash_core_components as dcc
import dash_html_components as html
import pandas as pd
import cdata.api as mod
import plotly.graph_objs as go

cnxn = mod.connect("Profile=C:\profiles\GoogleTranslate.apip;AuthScheme=OAuth;InitiateOAuth=GETANDREFRESH;OAuthClientId=your_client_id;OAuthClientSecret=your_client_secret;CallbackUrl=your_callback_url;")

df = pd.read_sql("SELECT LanguageCode, DisplayName FROM SupportedLanguages WHERE ProjectId = 'my-project-12345'", cnxn)
app_name = 'dash-apidataplot'

external_stylesheets = ['https://codepen.io/chriddyp/pen/bWLwgP.css']

app = dash.Dash(__name__, external_stylesheets=external_stylesheets)
app.title = 'CData + Dash'
trace = go.Bar(x=df.LanguageCode, y=df.DisplayName, name='LanguageCode')

app.layout = html.Div(children=[html.H1("CData Extension + Dash", style={'textAlign': 'center'}),
	dcc.Graph(
		id='example-graph',
		figure={
			'data': [trace],
			'layout':
			go.Layout(title='Google Translate SupportedLanguages Data', barmode='stack')
		})
], className="container")

if __name__ == '__main__':
    app.run_server(debug=True)

Ready to get started?

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