Use Dash to Build to Web Apps on Browse AI Data

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 Browse AI-connected web applications for Browse AI data. This article shows how to connect to Browse AI with the CData Connector and use pandas and Dash to build a simple web app for visualizing Browse AI data.

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

Connecting to Browse AI Data

Connecting to Browse AI 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.

Start by setting the Profile connection property to the location of the BrowseAI Profile on disk (e.g. C:\profiles\BrowseAI.apip). Next, set the ProfileSettings connection property to the connection string for BrowseAI (see below).

BrowseAI API Profile Settings

BrowseAI is a web scraping and automation platform that allows you to train robots to extract and monitor data from websites. Authentication is performed using an API key that is sent as a Bearer token in the Authorization header.

The BrowseAI API key has the format {userId}:{apiKey}, where both parts are UUIDs separated by a colon. This full string is provided as a single value in the BrowseAI dashboard.

To obtain your API key:

1. Log in to your BrowseAI account at https://app.browse.ai
2. Navigate to Settings in the dashboard
3. Locate the API section
4. Copy the full API key string displayed (format: userId:apiKey)

After obtaining your API key, set the following connection properties:

• AuthScheme: Set this to APIKey.

Set the following in the ProfileSettings connection property:

• APIKey: Set this to your BrowseAI API key (format: userId:apiKey).

After installing the CData Browse AI Connector, follow the procedure below to install the other required modules and start accessing Browse AI 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 Browse AI 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 Browse AI Connector to create a connection for working with Browse AI data.

cnxn = mod.connect("Profile=C:\profiles\BrowseAI.apip;AuthScheme=APIKey;ProfileSettings='APIKey=your_user_id:your_api_key';")

Execute SQL to Browse AI

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 Id, Status FROM RobotTasks WHERE RobotId = 'your-robot-id'", 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 Browse AI data and configure the app layout.

trace = go.Bar(x=df.Id, y=df.Status, name='Id')

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='Browse AI RobotTasks 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 Browse AI data.

python api-dash.py

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 Browse AI 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\BrowseAI.apip;AuthScheme=APIKey;ProfileSettings='APIKey=your_user_id:your_api_key';")

df = pd.read_sql("SELECT Id, Status FROM RobotTasks WHERE RobotId = 'your-robot-id'", 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.Id, y=df.Status, name='Id')

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='Browse AI RobotTasks Data', barmode='stack')
		})
], className="container")

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