AI/ML, Cloud Computing, Data Analytics

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Building Interactive Dashboards with ChatGPT

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Dashboards are visual representations of data that provide an at-a-glance view of key performance indicators (KPIs), metrics, and other important information. They consolidate and display data in a visually appealing and interactive format, allowing users to quickly monitor, analyze quickly, and understand complex datasets.

The growing trend of integrating conversational AI, like ChatGPT, into dashboards stems from the desire to enhance user experience and make data interaction more intuitive. Conversational AI adds a human-like interaction layer to dashboards, allowing users to communicate with the data in natural language rather than relying solely on static visualizations or predefined interactions.

Steps to Create a Dashboard

Creating a fully functional dashboard with ChatGPT involves several steps, and it’s important to note that the actual implementation can vary based on your preferred programming language, framework, and tools.

Steps Involved

Step 1: Install Required Libraries-Make sure you have Flask installed, and you may also need the openai library for interacting with the GPT-3 API.

Step 2: Create Flask App- Create a file named and set up your Flask app. Replace ‘your_openai_api_key’ with your actual OpenAI API key.

Step 3: Create HTML Templates- Create a folder named templates and add an HTML file named index.html.

Step 4: Obtain the OpenAI API Key. You must sign up for the OpenAI GPT-3 API and obtain an API key.

Step 5: Run the Application – Save your changes and run your Flask app.

This step-by-step guide provides a basic implementation of a Flask app integrated with the OpenAI GPT-3 API. Customize and enhance the code based on your specific requirements and handle your API key securely.

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Flow Diagram


Use Case


Create a dashboard that allows a sales manager to monitor and analyze key metrics related to sales performance, inventory, and customer feedback through a conversational interface.

Components of the Dashboard:

  1. Sales Performance Metrics:
  • Total Sales
  • Top-selling Products
  • Monthly Sales Trends
  1. Inventory Status:
  • Current Inventory Levels
  • Out-of-Stock Products
  • Reorder Recommendations
  1. Customer Feedback:
  • Overall Customer Satisfaction
  • Net Promoter Score (NPS)
  • Recent Customer Comments

Features and Interactions

  1. Conversational Interface:
  • Users can interact with the dashboard by typing natural language queries or commands.
  • Example: “What were the top-selling products last month?”

2. Dynamic Data Exploration:

  • Users can ask for real-time updates on metrics and trends.
  • Example: “Show me the sales trend for the last six months.”

3. Personalized Insights:

  • Conversational AI provides personalized insights based on user queries.
  • Example: “Which products are performing well in the North region?”

4. Inventory Management:

  • Users can inquire about inventory levels and receive recommendations for reordering.
  • Example: “What products are low in stock, and should we consider reordering?”

5. Customer Feedback Analysis:

  • Users can ask about customer satisfaction and recent comments.
  • Example: “How satisfied are customers with our service? Any recent feedback?”

6. Alerts and Notifications:

  • The system can proactively notify the user of critical updates or anomalies.
  • Example: “Alert me if any product is consistently out of stock.”


  • Dashboard Visualization: Use visualization tools like Plotly, Matplotlib, or Dash to create charts and graphs for sales performance, inventory, and customer feedback.
  • Flask Web Application: Develop a Flask web application to host the dashboard with interactive components.
  • Chat Integration (ChatGPT): Integrate ChatGPT or a similar conversational AI model to handle user queries and provide responses.
  • Data Integration: Connect the dashboard to relevant data sources, such as sales databases, inventory management systems, and customer feedback platforms.
  • User Authentication: Implement user authentication to ensure secure access to sensitive data.

User Interaction Flow

  • The sales manager logs into the dashboard and is greeted by the conversational interface.
  • The manager asks the chat interface questions.
  • The conversational AI processes these queries, retrieves data from the underlying datasets, and presents the information in a user-friendly format within the dashboard.
  • The dashboard displays visualizations, trends, and recommendations based on the manager’s inquiries.
  • The manager can refine their analysis by asking follow-up questions or issuing commands through the chat interface.

In this use case, integrating a conversational interface enhances the user experience by providing a natural and interactive way for the sales manager to explore and understand key metrics related to retail sales. The dashboard becomes a dynamic tool that adapts to user inquiries, facilitating quicker decision-making and analysis.


In conclusion, creating dashboards using ChatGPT introduces a transformative data exploration and analysis approach. Integrating a conversational interface powered by ChatGPT enhances user experience, making data interactions more natural, intuitive, and engaging.

Drop a query if you have any questions regarding ChatGPT and we will get back to you quickly.

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1. Why integrate ChatGPT into dashboards?

ANS: – Integrating ChatGPT into dashboards allows users to interact with data using natural language. This enhances the user experience by providing a conversational interface, enabling users to ask questions, request insights, and receive information more intuitively and interactively.

2. What are the benefits of using ChatGPT in dashboard creation?

ANS: – Benefits include enhanced user engagement, personalized interactions, dynamic data exploration, and receiving real-time insights through a natural language interface. ChatGPT adds a layer of interactivity and accessibility to dashboards.

3. How do I deploy a dashboard with ChatGPT integration in a production environment?

ANS: – Deploy the dashboard and ChatGPT integration using suitable hosting services. Ensure scalability, monitor system performance, and address security considerations before deploying in a production environment.

WRITTEN BY Neetika Gupta

Neetika Gupta works as a Senior Research Associate in CloudThat has the experience to deploy multiple Data Science Projects into multiple cloud frameworks. She has deployed end-to-end AI applications for Business Requirements on Cloud frameworks like AWS, AZURE, and GCP and Deployed Scalable applications using CI/CD Pipelines.



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