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๐Ÿ“Š AI Data Visualization Agent

awesome-llm-appsai_data_visualisation_agent

๐Ÿ‘‰ Click here to follow our complete step-by-step tutorial and learn how to build this from scratch with detailed code walkthroughs, explanations, and best practices.

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๐Ÿ“Š AI Data Visualization Agent

๐ŸŽ“ FREE Step-by-Step Tutorial

๐Ÿ‘‰ Click here to follow our complete step-by-step tutorial and learn how to build this from scratch with detailed code walkthroughs, explanations, and best practices.

A Streamlit application that acts as your personal data visualization expert, powered by LLMs. Simply upload your dataset and ask questions in natural language - the AI agent will analyze your data, generate appropriate visualizations, and provide insights through a combination of charts, statistics, and explanations.

Features

Natural Language Data Analysis

  • Ask questions about your data in plain English
  • Get instant visualizations and statistical analysis
  • Receive explanations of findings and insights
  • Interactive follow-up questioning

Intelligent Visualization Selection

  • Automatic choice of appropriate chart types
  • Dynamic visualization generation
  • Statistical visualization support
  • Custom plot formatting and styling

Multi-Model AI Support

  • Meta-Llama 3.1 405B for complex analysis
  • DeepSeek V3 for detailed insights
  • Qwen 2.5 7B for quick analysis
  • Meta-Llama 3.3 70B for advanced queries

How to Run

Follow the steps below to set up and run the application:

  1. Clone the Repository
    git clone https://github.com/Shubhamsaboo/awesome-llm-apps.git
    cd starter_ai_agents/ai_data_visualisation_agent
    
  2. Install the dependencies
    pip install -r requirements.txt
    
  3. Run the Streamlit app
    streamlit run ai_data_visualisation_agent.py
    

Ingestion metadata

Source catalog
awesome-llm-apps
Repository
Shubhamsaboo/awesome-llm-apps ยท main
File path
starter_ai_agents/ai_data_visualisation_agent/README.md
Last refreshed
7/23/2026, 10:39:09 PM (4h ago)
Refresh schedule
Daily ยท 03:00 UTC
Dedupe status
Unique ยท deduped by (source, url)