{"id":22476880,"url":"https://github.com/hosseinkarimi128/zed-one","last_synced_at":"2026-04-07T09:31:40.845Z","repository":{"id":265110627,"uuid":"894955843","full_name":"HosseinKarimi128/zed-one","owner":"HosseinKarimi128","description":"An AI-powered assistant that analyzes CSV data using natural language queries to generate pandas code and visualizations.","archived":false,"fork":false,"pushed_at":"2024-12-23T12:35:02.000Z","size":1964,"stargazers_count":0,"open_issues_count":0,"forks_count":1,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-03-27T17:47:46.843Z","etag":null,"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"],"latest_commit_sha":null,"homepage":"","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/HosseinKarimi128.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2024-11-27T09:57:21.000Z","updated_at":"2024-12-23T12:35:05.000Z","dependencies_parsed_at":"2025-02-01T20:45:06.658Z","dependency_job_id":"843a5f2e-6be9-4598-9d2c-80c0b4d970cf","html_url":"https://github.com/HosseinKarimi128/zed-one","commit_stats":null,"previous_names":["hosseinkarimi128/zed-one"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/HosseinKarimi128/zed-one","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/HosseinKarimi128%2Fzed-one","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/HosseinKarimi128%2Fzed-one/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/HosseinKarimi128%2Fzed-one/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/HosseinKarimi128%2Fzed-one/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/HosseinKarimi128","download_url":"https://codeload.github.com/HosseinKarimi128/zed-one/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/HosseinKarimi128%2Fzed-one/sbom","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":263211343,"owners_count":23431294,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["ai-data-analysis","automated-pandas","automated-pandas-queries","csv","data-analysis","fastapi","langchain","machine-learning","matplotlib","nlp","openai","pandas","restful-api","summarization","visualization-tools"],"created_at":"2024-12-06T14:08:25.690Z","updated_at":"2025-12-30T22:19:25.610Z","avatar_url":"https://github.com/HosseinKarimi128.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"![Zed-One Banner](./logo.png)\n\n# Zed-One\n\n## 🚀 Introduction\n\nWelcome 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.\n\n## 🛠 Features\n\n- **CSV Upload:** Easily upload your CSV files for analysis.\n- **Natural Language Queries:** Ask questions about your data and receive precise answers generated by AI.\n- **Dynamic Visualizations:** Request visual representations of your data and get interactive Plotly charts.\n- **Automated Schema Extraction:** Automatically extracts and summarizes the schema of your datasets.\n- **Interactive Frontend:** User-friendly interface built with Streamlit for seamless interaction.\n- **Robust Backend:** Powered by FastAPI to handle requests efficiently.\n\n## 📈 Demo\n\n![Zed-One Demo](https://will-be-here-soon.gif)\n\n## 🧰 Technologies Used\n\n- **Backend:**\n  - [FastAPI](https://fastapi.tiangolo.com/) - A modern, fast web framework for building APIs with Python.\n  - [LangChain](https://langchain.com/) - Framework for developing applications powered by language models.\n  - [OpenAI GPT-4](https://openai.com/product/gpt-4) - Advanced language model for generating intelligent responses.\n  - [Plotly](https://plotly.com/python/) - Interactive graphing library for Python.\n\n- **Frontend:**\n  - [Streamlit](https://streamlit.io/) - An open-source app framework for Machine Learning and Data Science teams.\n  - [Rich](https://rich.readthedocs.io/en/stable/) - Python library for rich text and beautiful formatting in the terminal.\n\n- **Utilities:**\n  - [Uvicorn](https://www.uvicorn.org/) - A lightning-fast ASGI server.\n  - [dotenv](https://github.com/theskumar/python-dotenv) - Loads environment variables from a `.env` file.\n  - [Logging](https://docs.python.org/3/library/logging.html) - Configurable logging for Python applications.\n\n## 📦 Installation\n\n### Prerequisites\n\n- Python 3.8 or higher\n- pip (Python package installer)\n\n### Steps\n\n1. **Clone the Repository**\n\n   ```bash\n   git clone https://github.com/your-username/smartdata-ai.git\n   cd smartdata-ai\n   ```\n\n2. **Create a Virtual Environment**\n\n   ```bash\n   python -m venv venv\n   ```\n\n3. **Activate the Virtual Environment**\n\n   - **Windows:**\n     ```bash\n     venv\\Scripts\\activate\n     ```\n   - **macOS/Linux:**\n     ```bash\n     source venv/bin/activate\n     ```\n\n4. **Install Dependencies**\n\n   ```bash\n   pip install -r requirements.txt\n   ```\n\n5. **Configure Environment Variables**\n\n   Create a `.env` file in the root directory and add your OpenAI API key:\n\n   ```env\n   OPENAI_API_KEY=your_openai_api_key_here\n   ```\n\n## 🏃‍♂️ Usage\n\n### Running the Application\n\nTo start both the FastAPI backend and the Streamlit frontend simultaneously, use the `run_all.py` script:\n\n```bash\npython run_all.py\n```\n\n- **FastAPI Backend:** Runs on `http://localhost:8000`\n- **Streamlit Frontend:** Runs on `http://localhost:8501`\n\n### Endpoints\n\n#### 1. **Upload CSV**\n\n- **URL:** `/upload_csv/`\n- **Method:** `POST`\n- **Description:** Upload a CSV file for analysis.\n- **Parameters:**\n  - `file`: The CSV file to upload.\n\n#### 2. **Ask Question**\n\n- **URL:** `/ask_question/`\n- **Method:** `POST`\n- **Description:** Ask a question about the uploaded CSV data.\n- **Parameters:**\n  - `question`: The question you want to ask.\n  - `filename`: The name of the uploaded CSV file.\n\n#### 3. **Visualize Data**\n\n- **URL:** `/visualize/`\n- **Method:** `POST`\n- **Description:** Generate a Plotly visualization based on your question.\n- **Parameters:**\n  - `question`: The visualization request.\n  - `filename`: The name of the uploaded CSV file.\n\n### Streamlit Frontend\n\nAccess the Streamlit frontend at [http://localhost:8501](http://localhost:8501) after running the application. The interface allows you to:\n\n- **Upload CSV Files:** Easily upload and manage your datasets.\n- **Ask Questions:** Interact with your data using natural language queries.\n- **Generate Visualizations:** Create insightful plots and charts based on your requests.\n\n## 🗂 Project Structure\n\n```\nsmartdata-ai/\n├── agents/\n│   ├── query_generator.py\n│   ├── response_generator.py\n│   └── visualizer.py\n├── utils/\n│   ├── data_loader.py\n│   ├── schema_extractor.py\n│   └── summary_generator.py\n├── app.py\n├── run_all.py\n├── streamlit_app.py\n├── requirements.txt\n├── .env\n├── README.md\n└── banner.png\n```\n\n## 📝 Contributing\n\nContributions are welcome! Please follow these steps:\n\n1. **Fork the Repository**\n2. **Create a Feature Branch**\n\n   ```bash\n   git checkout -b feature/YourFeature\n   ```\n\n3. **Commit Your Changes**\n\n   ```bash\n   git commit -m \"Add your message\"\n   ```\n\n4. **Push to the Branch**\n\n   ```bash\n   git push origin feature/YourFeature\n   ```\n\n5. **Open a Pull Request**\n\n## 🛡 License\n\nThis project is licensed under the [MIT License](LICENSE).\n\n## 📞 Contact\n\nFor any inquiries or support, please contact [hossein.karimi.0128@gmail.com](mailto:hossein.karimi.0128@gmail.com).\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fhosseinkarimi128%2Fzed-one","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fhosseinkarimi128%2Fzed-one","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fhosseinkarimi128%2Fzed-one/lists"}