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https://github.com/kaykeeb3/excel-spreadsheet-converter
Aplicação web que converte dados JSON em Excel 📊, utilizando Flask, Openpyxl, e TailwindCSS para uma experiência moderna e intuitiva 🌐
https://github.com/kaykeeb3/excel-spreadsheet-converter
css html javascript python
Last synced: about 1 month ago
JSON representation
Aplicação web que converte dados JSON em Excel 📊, utilizando Flask, Openpyxl, e TailwindCSS para uma experiência moderna e intuitiva 🌐
- Host: GitHub
- URL: https://github.com/kaykeeb3/excel-spreadsheet-converter
- Owner: kaykeeb3
- Created: 2024-05-17T18:28:37.000Z (9 months ago)
- Default Branch: main
- Last Pushed: 2024-12-23T15:39:51.000Z (about 2 months ago)
- Last Synced: 2024-12-23T16:36:07.419Z (about 2 months ago)
- Topics: css, html, javascript, python
- Language: Python
- Homepage:
- Size: 51.8 KB
- Stars: 3
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Excel Spreadsheet Converter
This project is a web application developed to convert **JSON** data into **Excel** spreadsheets automatically. Using **Flask** for the backend and **HTML**, **JavaScript**, and **TailwindCSS** for the frontend, the tool provides a simple and intuitive experience for data conversion, saving time and minimizing errors when generating Excel reports.
## Features
- **Report Generation**: Converts user-provided JSON data into custom Excel reports.
- **Automation**: Simplifies report creation from structured data, ensuring speed and accuracy in the process.
- **Intuitive and Responsive Interface**: Developed with modern technologies to ensure an optimized user experience on any device.
## Technologies Used
- **Backend**: Flask (Python framework for APIs)
- **Frontend**: HTML, TailwindCSS, JavaScript
- **Data Manipulation**: Pandas (for data processing)
- **Excel Generation**: Openpyxl (for creating and manipulating Excel files)
## Prerequisites
Before starting, you need to have the following prerequisites installed:
- **Python 3.x**
- **Pip** (Python package manager)
## Installation
1. Clone the repository:
```bash
git clone https://github.com/kaykeeb3/excel-spreadsheet-converter.git
cd excel-spreadsheet-converter
```
2. Create and activate a virtual environment:
For Linux/macOS:
```bash
python3 -m venv venv
source venv/bin/activate
```
For Windows:
```bash
python -m venv venv
venv\Scripts\activate
```
3. Install the project dependencies:
```bash
pip install -r requirements.txt
```
## Running the Application
1. Start the Flask server:
```bash
python app.py
```
2. Open your browser and go to `http://localhost:5000` to use the application.
## Usage
1. Paste the **JSON** data into the provided field on the interface.
2. Click the **"Generate Report"** button.
3. The Excel file will be automatically generated and downloaded to your device.
### Example JSON Data
Here is an example of how the JSON data should be structured to generate a report:
```json
[
{
"ID": 1,
"Customer Name": "Alice",
"Customer who called on WhatsApp": "João",
"Company Name": "Company A",
"Customer Query": "Product X",
"Call Time": "10:00",
"Average Response Time": "15 minutes",
"Date": "2024-05-20",
"Day: .. Service of the Day": "Monday"
},
{
"ID": 2,
"Customer Name": "Bob",
"Customer who called on WhatsApp": "Maria",
"Company Name": "Company B",
"Customer Query": "Service Y",
"Call Time": "11:30",
"Average Response Time": "20 minutes",
"Date": "2024-05-20",
"Day: .. Service of the Day": "Monday"
},
{
"ID": 3,
"Customer Name": "Charlie",
"Customer who called on WhatsApp": "Pedro",
"Company Name": "Company C",
"Customer Query": "Product Z",
"Call Time": "14:45",
"Average Response Time": "10 minutes",
"Date": "2024-05-21",
"Day: .. Service of the Day": "Tuesday"
}
]
```
## Future Improvements
- **Support for More Formats**: Implement support for importing and exporting other data formats (CSV, XML, etc.).
- **User Authentication**: Implement user authentication and authorization to control access to reports.
- **Data Analysis**: Add features for data analysis and visualization with charts and dynamic reports.
## Contributing
Contributions are always welcome! To contribute, follow these steps:
1. Fork the repository.
2. Create a branch for your feature (`git checkout -b feature/new-feature`).
3. Make your changes and commit (`git commit -am 'Added a new feature'`).
4. Push your branch to the remote repository (`git push origin feature/new-feature`).
5. Open a **Pull Request** with a clear description of the changes made.