Use Dash to Build Web Apps on Sage 300 Data via CData Connect AI

The rich ecosystem of Python modules lets you get to work quickly and integrate your systems more effectively. With the CData Connect AI Python SDK, the pandas module, and the Dash framework, you can build Sage 300-connected web applications for Sage 300 data. This article shows how to connect to Connect AI and use pandas and Dash to build a simple web app for visualizing Sage 300 data.

The Connect AI Python SDK (cdata-connect-ai) is a DB-API 2.0 (PEP 249) compliant client, so pandas can read query results directly from the SDK connection object. There is no driver to install per source: connect with a Personal Access Token and build your app.

Connect to Sage 300 in Connect AI

CData Connect AI uses a straightforward, point-and-click interface to connect to data sources.

1. Log into Connect AI, click Sources, and then click Add Connection

2. Select "Sage 300" from the Add Connection panel

3. Enter the necessary authentication properties to connect to Sage 300.

   Sage 300 requires some initial setup in order to communicate over the Sage 300 Web API.

   • Set up the security groups for the Sage 300 user. Give the Sage 300 user access to the option under Security Groups (per each module required).
   • Edit both web.config files in the /Online/Web and /Online/WebApi folders; change the key AllowWebApiAccessForAdmin to true. Restart the webAPI app-pool for the settings to take.
   • Once the user access is configured, click https://server/Sage300WebApi/ to ensure access to the web API.

   Authenticate to Sage 300 using Basic authentication.

   Connect Using Basic Authentication

   You must provide values for the following properties to successfully authenticate to Sage 300. Note that the provider reuses the session opened by Sage 300 using cookies. This means that your credentials are used only on the first request to open the session. After that, cookies returned from Sage 300 are used for authentication.

   • Url: Set this to the url of the server hosting Sage 300. Construct a URL for the Sage 300 Web API as follows: {protocol}://{host-application-path}/v{version}/{tenant}/ For example, http://localhost/Sage300WebApi/v1.0/-/.
   • User: Set this to the username of your account.
   • Password: Set this to the password of your account.
   
4. Click Save & Test
5. Navigate to the Permissions tab and update the user-based permissions.

Generate a Personal Access Token (PAT)

The Python SDK authenticates to Connect AI with your account email and a Personal Access Token (PAT). It is best practice to create a separate PAT for each application to maintain granularity of access.

1. Click the Gear icon () at the top right of the Connect AI app to open the Settings page.
2. On the Settings page, go to the Access Tokens section and click Create PAT.
3. Give the PAT a name and click Create.
4. The PAT is only visible at creation, so copy it and store it securely.

Install Required Modules

Install the SDK (with the pandas extra), Dash, and Plotly using the pip utility:

pip install "cdata-connect-ai[full]"
pip install dash
pip install plotly

Build a Web App on Sage 300 Data in Python

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

First, import the modules, then connect to Connect AI with your account email and PAT and read Sage 300 data into a DataFrame. Identifiers are three-part: <Connection>.<Schema>.<Table>, where the connection name defaults to the source name (for example, Sage3001).

import dash
from dash import dcc, html
import pandas as pd
import plotly.graph_objs as go
import cdata_connect_ai

conn = cdata_connect_ai.connect(
    username="[email protected]",
    password="<your_pat>",
)

df = pd.read_sql(
    "SELECT InvoiceUniquifier, ApprovedLimit "
    "FROM [Sage3001].[Sage300].[OEInvoices] "
    "WHERE AllowPartialShipments = 'Yes'",
    conn,
)

conn.close()

Configure the App and Layout

With the query results stored in a DataFrame, build a bar graph from the Sage 300 data and configure the app layout.

app = dash.Dash(__name__)
app.title = 'CData Connect AI + Dash'

trace = go.Bar(x=df['InvoiceUniquifier'], y=df['ApprovedLimit'], name='InvoiceUniquifier')

app.layout = html.Div(
    children=[
        html.H1("CData Connect AI + Dash", style={'textAlign': 'center'}),
        dcc.Graph(
            id='example-graph',
            figure={
                'data': [trace],
                'layout': go.Layout(title='Sage 300 OEInvoices Data', barmode='stack'),
            },
        ),
    ],
    className="container",
)

Set the App to Run

With the connection, app, and layout configured, you are ready to run the app.

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

Now, use Python to run the web app and a browser to view the Sage 300 data.

python sage300-dash.py

More Information and Free Trial

Now you can build interactive Dash web apps on live Sage 300 data using the CData Connect AI Python SDK. For more information on connecting to Sage 300 (and hundreds of other data sources), visit the Connect AI page. Sign up for a free trial and start building web apps on live Sage 300 data in Python.

Full Source Code

import dash
from dash import dcc, html
import pandas as pd
import plotly.graph_objs as go
import cdata_connect_ai

conn = cdata_connect_ai.connect(
    username="[email protected]",
    password="<your_pat>",
)

df = pd.read_sql(
    "SELECT InvoiceUniquifier, ApprovedLimit "
    "FROM [Sage3001].[Sage300].[OEInvoices] "
    "WHERE AllowPartialShipments = 'Yes'",
    conn,
)

conn.close()

app = dash.Dash(__name__)
app.title = 'CData Connect AI + Dash'

trace = go.Bar(x=df['InvoiceUniquifier'], y=df['ApprovedLimit'], name='InvoiceUniquifier')

app.layout = html.Div(
    children=[
        html.H1("CData Connect AI + Dash", style={'textAlign': 'center'}),
        dcc.Graph(
            id='example-graph',
            figure={
                'data': [trace],
                'layout': go.Layout(title='Sage 300 OEInvoices Data', barmode='stack'),
            },
        ),
    ],
    className="container",
)

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