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https://github.com/hosseinkarimi128/zed-one
An AI-powered assistant that analyzes CSV data using natural language queries to generate pandas code and visualizations.
https://github.com/hosseinkarimi128/zed-one
ai-data-analysis automated-pandas automated-pandas-queries csv data-analysis fastapi langchain machine-learning matplotlib nlp openai pandas restful-api summarization visualization-tools
Last synced: 5 days ago
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An AI-powered assistant that analyzes CSV data using natural language queries to generate pandas code and visualizations.
- Host: GitHub
- URL: https://github.com/hosseinkarimi128/zed-one
- Owner: HosseinKarimi128
- License: mit
- Created: 2024-11-27T09:57:21.000Z (2 months ago)
- Default Branch: main
- Last Pushed: 2024-12-06T16:32:55.000Z (2 months ago)
- Last Synced: 2024-12-22T15:16:28.328Z (about 2 months ago)
- Topics: ai-data-analysis, automated-pandas, automated-pandas-queries, csv, data-analysis, fastapi, langchain, machine-learning, matplotlib, nlp, openai, pandas, restful-api, summarization, visualization-tools
- Language: Python
- Homepage:
- Size: 1.87 MB
- Stars: 0
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Zed-One
## 🚀 Introduction
Welcome to **Zed-One**, an intelligent data analysis application that leverages the power of OpenAI's GPT-4 to provide insightful data queries and visualizations. Whether you're a data analyst, business professional, or enthusiast, Zed-One simplifies data exploration by allowing you to interact with your datasets using natural language.
## 🛠 Features
- **CSV Upload:** Easily upload your CSV files for analysis.
- **Natural Language Queries:** Ask questions about your data and receive precise answers generated by AI.
- **Dynamic Visualizations:** Request visual representations of your data and get interactive Plotly charts.
- **Automated Schema Extraction:** Automatically extracts and summarizes the schema of your datasets.
- **Interactive Frontend:** User-friendly interface built with Streamlit for seamless interaction.
- **Robust Backend:** Powered by FastAPI to handle requests efficiently.
## 📈 Demo
## 🧰 Technologies Used
- **Backend:**
- [FastAPI](https://fastapi.tiangolo.com/) - A modern, fast web framework for building APIs with Python.
- [LangChain](https://langchain.com/) - Framework for developing applications powered by language models.
- [OpenAI GPT-4](https://openai.com/product/gpt-4) - Advanced language model for generating intelligent responses.
- [Plotly](https://plotly.com/python/) - Interactive graphing library for Python.
- **Frontend:**
- [Streamlit](https://streamlit.io/) - An open-source app framework for Machine Learning and Data Science teams.
- [Rich](https://rich.readthedocs.io/en/stable/) - Python library for rich text and beautiful formatting in the terminal.
- **Utilities:**
- [Uvicorn](https://www.uvicorn.org/) - A lightning-fast ASGI server.
- [dotenv](https://github.com/theskumar/python-dotenv) - Loads environment variables from a `.env` file.
- [Logging](https://docs.python.org/3/library/logging.html) - Configurable logging for Python applications.
## 📦 Installation
### Prerequisites
- Python 3.8 or higher
- pip (Python package installer)
### Steps
1. **Clone the Repository**
```bash
git clone https://github.com/your-username/smartdata-ai.git
cd smartdata-ai
```
2. **Create a Virtual Environment**
```bash
python -m venv venv
```
3. **Activate the Virtual Environment**
- **Windows:**
```bash
venv\Scripts\activate
```
- **macOS/Linux:**
```bash
source venv/bin/activate
```
4. **Install Dependencies**
```bash
pip install -r requirements.txt
```
5. **Configure Environment Variables**
Create a `.env` file in the root directory and add your OpenAI API key:
```env
OPENAI_API_KEY=your_openai_api_key_here
```
## 🏃♂️ Usage
### Running the Application
To start both the FastAPI backend and the Streamlit frontend simultaneously, use the `run_all.py` script:
```bash
python run_all.py
```
- **FastAPI Backend:** Runs on `http://localhost:8000`
- **Streamlit Frontend:** Runs on `http://localhost:8501`
### Endpoints
#### 1. **Upload CSV**
- **URL:** `/upload_csv/`
- **Method:** `POST`
- **Description:** Upload a CSV file for analysis.
- **Parameters:**
- `file`: The CSV file to upload.
#### 2. **Ask Question**
- **URL:** `/ask_question/`
- **Method:** `POST`
- **Description:** Ask a question about the uploaded CSV data.
- **Parameters:**
- `question`: The question you want to ask.
- `filename`: The name of the uploaded CSV file.
#### 3. **Visualize Data**
- **URL:** `/visualize/`
- **Method:** `POST`
- **Description:** Generate a Plotly visualization based on your question.
- **Parameters:**
- `question`: The visualization request.
- `filename`: The name of the uploaded CSV file.
### Streamlit Frontend
Access the Streamlit frontend at [http://localhost:8501](http://localhost:8501) after running the application. The interface allows you to:
- **Upload CSV Files:** Easily upload and manage your datasets.
- **Ask Questions:** Interact with your data using natural language queries.
- **Generate Visualizations:** Create insightful plots and charts based on your requests.
## 🗂 Project Structure
```
smartdata-ai/
├── agents/
│ ├── query_generator.py
│ ├── response_generator.py
│ └── visualizer.py
├── utils/
│ ├── data_loader.py
│ ├── schema_extractor.py
│ └── summary_generator.py
├── app.py
├── run_all.py
├── streamlit_app.py
├── requirements.txt
├── .env
├── README.md
└── banner.png
```
## 📝 Contributing
Contributions are welcome! Please follow these steps:
1. **Fork the Repository**
2. **Create a Feature Branch**
```bash
git checkout -b feature/YourFeature
```
3. **Commit Your Changes**
```bash
git commit -m "Add your message"
```
4. **Push to the Branch**
```bash
git push origin feature/YourFeature
```
5. **Open a Pull Request**
## 🛡 License
This project is licensed under the [MIT License](LICENSE).
## 📞 Contact
For any inquiries or support, please contact [[email protected]](mailto:[email protected]).