{"id":23691324,"url":"https://github.com/devnamdev2003/sqlease","last_synced_at":"2026-01-16T16:30:17.622Z","repository":{"id":222936751,"uuid":"758785790","full_name":"devnamdev2003/SQLEase","owner":"devnamdev2003","description":null,"archived":false,"fork":false,"pushed_at":"2024-03-14T14:29:45.000Z","size":77,"stargazers_count":3,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2024-12-30T02:56:28.016Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":"https://devnamdev2003.github.io/SQLEase/","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/devnamdev2003.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}},"created_at":"2024-02-17T04:30:42.000Z","updated_at":"2024-07-02T04:05:55.000Z","dependencies_parsed_at":"2024-02-17T05:37:09.552Z","dependency_job_id":null,"html_url":"https://github.com/devnamdev2003/SQLEase","commit_stats":null,"previous_names":["devnamdev2003/sqlease"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/devnamdev2003%2FSQLEase","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/devnamdev2003%2FSQLEase/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/devnamdev2003%2FSQLEase/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/devnamdev2003%2FSQLEase/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/devnamdev2003","download_url":"https://codeload.github.com/devnamdev2003/SQLEase/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":239758610,"owners_count":19692031,"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":[],"created_at":"2024-12-30T02:56:33.295Z","updated_at":"2025-02-20T00:36:37.616Z","avatar_url":"https://github.com/devnamdev2003.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# [SQL Query Assistant: Simplify Your Data Analysis](https://drive.google.com/file/d/1Ce1sU9qES04qsOG-5x483j0A0jR0y9lA/view?usp=sharing)\n\n---\n\n## Introduction:\n\nThe SQL Query Assistant is a powerful desktop application designed to streamline the process of writing SQL queries for data analysis tasks. Whether you're a data scientist, analyst, or database administrator, this tool provides a user-friendly interface to interactively build and execute SQL queries on your datasets.\n\n## Key Features:\n\n1. **Upload CSV Files**: Easily upload your datasets in CSV format, making it convenient to work with various data sources.\n\n2. **Interactive Query Building**: The application allows you to input SQL queries directly or interactively build them within the interface, providing real-time feedback and suggestions.\n\n3. **AI-Powered Assistance**: Powered by OpenAI's GPT-3.5 model, the assistant provides intelligent suggestions and completions for your SQL queries, enhancing productivity and reducing errors.\n\n4. **Error Handling**: Robust error handling ensures that even complex queries are executed smoothly, with detailed error messages provided in case of any issues.\n\n5. **Data Visualization**: Integration with popular Python libraries like Pandas allows for seamless data visualization, enabling users to gain deeper insights from query results.\n\n6. **Cross-Platform Compatibility**: The application is built using Python and Tkinter, making it compatible with Windows, macOS, and Linux operating systems.\n\n\n## How to Use:\n\nClone the repository:\n\n```bash\ngit clone https://github.com/devnamdev2003/SQLEase.git\n```\nNavigate to the project directory:\n\n```bash\ncd SQLEase\n```\nInstall dependencies:\n\n```bash\npip install -r requirements.txt\n```\nRun the application:\n\n```bash\npython manage.py sql_gui_api.py\n```\n\n## How It Works:\n\n1. **Upload Dataset**: Start by uploading your dataset in CSV format using the provided button. The application supports a wide range of datasets, making it suitable for various use cases.\n\n2. **Write or Input Query**: Once the dataset is uploaded, you can either manually write your SQL query or input keywords and let the AI-powered assistant generate a query for you. The assistant provides helpful suggestions and completions, making query writing faster and more efficient.\n\n3. **Execute Query**: After finalizing your query, simply click the \"Run Query\" button to execute it. The application handles the execution process seamlessly, providing you with the query results in a clear and organized format.\n\n4. **Error Handling**: In case of any errors during query execution, the application provides detailed error messages, helping you quickly identify and resolve issues.\n\n5. **Visualize Results**: Utilize the integrated data visualization capabilities to create insightful charts and graphs based on your query results, facilitating easier data interpretation and analysis.\n6. Certainly! Here's an updated section including a sample data section:\nCertainly! Here's an updated section including a sample data section and a link to download the CSV file:\n\n\n## Sample Data:\n\nTo help you get started with the SQL Query Assistant, here's a sample dataset that you can use for testing and experimentation:\n\n### Description:\n\nThe sample dataset provided below includes simulated financial transactions over time. Each row represents a transaction with the following attributes:\n\n- **Date**: The date of the transaction.\n- **Withdrawals**: The amount withdrawn from the account.\n- **Deposits**: The amount deposited into the account.\n- **Balance**: The remaining balance after the transaction.\n\nYou can download the sample dataset from the link below:\n\n[Download Sample Dataset](https://raw.githubusercontent.com/devnamdev2003/SQLEase/main/data.csv)\n\n## Benefits:\n\n- **Time Efficiency**: By automating the query writing process and providing intelligent suggestions, the SQL Query Assistant helps save time and effort, allowing users to focus on analysis rather than syntax.\n\n- **Accuracy**: The AI-powered assistant reduces the risk of syntax errors and logical mistakes in SQL queries, ensuring accurate results and analysis.\n\n- **User-Friendly Interface**: With its intuitive interface and interactive features, the application caters to users of all skill levels, from beginners to experienced SQL developers.\n\n- **Enhanced Productivity**: By simplifying the data analysis workflow, the SQL Query Assistant enables users to complete tasks more efficiently, increasing overall productivity.\n\n## Future Enhancements:\n\n- **Support for Additional Data Formats**: Expand the application's compatibility to support other data formats such as Excel files, JSON, and databases like MySQL and PostgreSQL.\n\n- **Advanced Query Optimization**: Implement advanced query optimization techniques to further improve query performance and efficiency.\n\n- **Customization Options**: Introduce customization options for the AI assistant, allowing users to fine-tune its behavior and preferences according to their specific requirements.\n\n\n## User Interface:\n\n![SQL Query Assistant UI](./view.png)\n\n\n## Conclusion:\n\nThe SQL Query Assistant empowers users to perform data analysis tasks with ease and efficiency, thanks to its intuitive interface, AI-powered assistance, and robust features. Whether you're a data enthusiast exploring datasets or a professional analyst working with large-scale data, this tool is designed to simplify your workflow and elevate your data analysis experience.\n\nDownload the SQL Query Assistant today and revolutionize the way you interact with your data!\n\n[Download SQL Query Assistant](https://drive.google.com/file/d/1Ce1sU9qES04qsOG-5x483j0A0jR0y9lA/view?usp=sharing) \n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdevnamdev2003%2Fsqlease","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fdevnamdev2003%2Fsqlease","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdevnamdev2003%2Fsqlease/lists"}