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feat: updated the README

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ShubhamSaboo 2025-01-07 20:07:20 -06:00
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ai_agent_tutorials/ai_data_visualisation_agent/README.md
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# AI Data Visualization Agent # 📊 AI Data Visualization Agent
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.
This Assistant is designed to help anyone create and visualize data using natural language commands, and it is built using Together AI and E2B Code Interpreter. User gets to upload a dataset and ask questions to the LLM to get the data visualized. This demo can be considered as a demo for the E2B Code Interpreter and Together AI, for anyone who's getting started with these libraries!
## Demo
https://github.com/user-attachments/assets/d8414c37-5edd-4e4d-a7b1-b9ab500bd8cd
## Features ## 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
- 🎨 Natural language-driven visualization creation #### Intelligent Visualization Selection
- 📊 Support for multiple chart types (line, bar, scatter, pie, bubble) - Automatic choice of appropriate chart types
- 📈 Automatic data preprocessing and cleaning - Dynamic visualization generation
- 🎯 Available Models: - Statistical visualization support
- Meta-Llama 3.1 405B - Custom plot formatting and styling
- DeepSeek V3
- Qwen 2.5 7B #### Multi-Model AI Support
- Meta-Llama 3.3 70B - Meta-Llama 3.1 405B for complex analysis
- 📱 The Code runs in the E2B Sandbox environment, so it is secure and fast - DeepSeek V3 for detailed insights
- Streamlit for clear and interactive user interface - Qwen 2.5 7B for quick analysis
- Meta-Llama 3.3 70B for advanced queries
## How to Run ## How to Run
Follow the steps below to set up and run the application: Follow the steps below to set up and run the application:
Before anything else, Please get a free Together AI API Key here: https://api.together.ai/signin - Before anything else, Please get a free Together AI API Key here: https://api.together.ai/signin
Get a free E2B API Key here: https://e2b.dev/ ; https://e2b.dev/docs/legacy/getting-started/api-key - Get a free E2B API Key here: https://e2b.dev/ ; https://e2b.dev/docs/legacy/getting-started/api-key
1. **Clone the Repository**: 1. **Clone the Repository**
```bash ```bash
git clone https://github.com/Shubhamsaboo/awesome-llm-apps.git git clone https://github.com/Shubhamsaboo/awesome-llm-apps.git
cd ai_agent_tutorials/ai_data_visualisation_agent cd ai_agent_tutorials/ai_data_visualisation_agent
``` ```
2. **Install the dependencies** 2. **Install the dependencies**
```bash ```bash
pip install -r requirements.txt pip install -r requirements.txt
``` ```
3. **Run the Streamlit app** 3. **Run the Streamlit app**
```bash ```bash
streamlit run ai_data_visualisation_agent.py streamlit run ai_data_visualisation_agent.py
``` ```

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ai_agent_tutorials/ai_data_visualisation_agent/ai_data_visualisation_agent.py
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@ -89,7 +89,7 @@ def upload_dataset(code_interpreter: Sandbox, uploaded_file) -> str:
def main(): def main():
"""Main Streamlit application.""" """Main Streamlit application."""
st.title("AI Data Visualization Agent") st.title("📊 AI Data Visualization Agent")
st.write("Upload your dataset and ask questions about it!") st.write("Upload your dataset and ask questions about it!")
# Initialize session state variables # Initialize session state variables