📊 Introduction

NTViz is a Streamlit application that performs various data analysis, visualization tasks and interacts with Large Language Models (LLMs) through the LIDA framework.

🛠️ Prerequisites & Installation

Option 1: Use the Web Application

Access NTViz directly in your browser.

Option 2: Run Locally

# Clone the repository
git clone https://github.com/tramphan748/lida_streamlit_app.git
cd lida_streamlit_app

# Install the required packages
pip install -r requirements.txt

# Run the application
streamlit run streamlit_app.py

Note: The requirements.txt file includes llmx-gemini, a customized version of the llmx library that supports the Gemini API. If you manually installed llmx in editable mode, ensure that the same code or git+... line is present in requirements.txt.

📑 Features

1. Dashboard

Introduction to the project, overview of capabilities, and user interface.

2. API Key Instructions

Step-by-step guide to obtain API keys from two main providers:

• Cohere
• Gemini (using our customized llmx-gemini library)

3. Data Reports

Generate comprehensive data overviews:

• Automatic statistics generation
• Charts and visualizations
• Missing value detection
• Correlation analysis
• Basic exploratory data analysis (EDA)

4. LIDA Tasks

4.1. Summarize & Goal

Leverage LLMs to automatically generate:

• Data summaries
• Top 5 analytical goals

4.2. User Query Based Graphs

Create visualizations based on natural language user queries.

4.3. Recommendations

Based on summaries, goals, code, and generated visualizations, the system automatically suggests 1-5 additional charts.

📚 References

• LIDA Github – Original LIDA framework
• llmx-gemini – Custom Gemini integration for llmx
• YData Profiling – Data profiling library for quick overviews
• Streamlit Documentation – Building interactive data apps in Python

📖 Documentation and Citation

A paper describing LIDA (Accepted at ACL 2023 Conference) is available here.

@inproceedings{dibia2023lida,
    title = "{LIDA}: A Tool for Automatic Generation of Grammar-Agnostic Visualizations and Infographics using Large Language Models",
    author = "Dibia, Victor",
    booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations)",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.acl-demo.11",
    doi = "10.18653/v1/2023.acl-demo.11",
    pages = "113--126",
}