Use Dash to Build to Web Apps on Mistral 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 Mistral AI-connected web applications for Mistral AI data. This article shows how to connect to Mistral AI with the CData Connector and use pandas and Dash to build a simple web app for visualizing Mistral AI data.

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

Connecting to Mistral AI Data

Connecting to Mistral 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.

The MistralAI API uses API key authentication.

Using API Key Authentication

Your MistralAI API Key is required to create a connection to MistralAI. API Keys can be obtained from your MistralAI account at console.mistral.ai by navigating to the API Keys section. Once you have obtained the API key, set it in the ProfileSettings connection property.

Example Connection string

Profile=C:\profiles\MistralAI.apip;ProfileSettings='APIKey=my_api_key;';AuthScheme=APIKey;

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

cnxn = mod.connect("Profile=C:\profiles\MistralAI.apip;ProfileSettings='APIKey=my_api_key;';AuthScheme=APIKey;")

Execute SQL to Mistral 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 ,  FROM AudioTranscriptions WHERE Model = 'voxtral-mini-latest'", 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 Mistral AI data and configure the app layout.

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

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='Mistral AI AudioTranscriptions 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 Mistral 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 Mistral 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\MistralAI.apip;ProfileSettings='APIKey=my_api_key;';AuthScheme=APIKey;")

df = pd.read_sql("SELECT ,  FROM AudioTranscriptions WHERE Model = 'voxtral-mini-latest'", 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., y=df., name='')

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='Mistral AI AudioTranscriptions Data', barmode='stack')
		})
], className="container")

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