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https://github.com/luisgeralda/food-sales-webapp
Here is a simple project of a Food Sales webapp. While implementing HTML, CSS, Js, and Python I am filtering through a food sales report in Excel in order to filter sales within a specific time period and a specific location
https://github.com/luisgeralda/food-sales-webapp
Last synced: about 2 months ago
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Here is a simple project of a Food Sales webapp. While implementing HTML, CSS, Js, and Python I am filtering through a food sales report in Excel in order to filter sales within a specific time period and a specific location
- Host: GitHub
- URL: https://github.com/luisgeralda/food-sales-webapp
- Owner: LuisGeralda
- Created: 2024-11-11T21:25:28.000Z (3 months ago)
- Default Branch: main
- Last Pushed: 2024-12-07T02:09:20.000Z (2 months ago)
- Last Synced: 2024-12-07T02:17:44.352Z (2 months ago)
- Language: JavaScript
- Size: 109 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Food Sales Analysis Web App
This is a simple, responsive web application to analyze food sales data. Users can filter sales records by date, city, and category, with results displayed in a clean, tabulated format. The frontend is designed with an Apple-inspired aesthetic, featuring a modern gradient background and responsive design.
## Features
- Filter data by date range, city, and category.
- Display results in a tabulated, aesthetically pleasing format.
- Responsive design for compatibility with different screen sizes.
- Backend built with Flask for data handling and filtering.
- Frontend built with HTML, CSS, and JavaScript.
## Technologies Used
- **Backend**: Python, Flask, Pandas
- **Frontend**: HTML, CSS, JavaScript
- **Server**: Apache (via XAMPP or WAMP for local development)
## Setup Instructions
### Clone the Repository:
```
git clone https://github.com/LuisGeralda/Food-Sales-webapp.git
cd food-sales-analysis
```
### Install Dependencies:
Make sure you have Python and pip installed, then install the necessary Python packages:
```
pip install flask pandas flask-cors
```
### Start the Flask Backend:
Run the `app.py` file to start the backend server.
```
python backend/app.py
```
### Deploying on Render:
To deploy the application on Render:
1. **Prepare the Repository:**
- Ensure the project structure is as follows:
```
project/
├── backend/
│ ├── app.py
│ └── sampledatafoodsales_analysis.xlsx
├── frontend/
│ ├── index.html
│ ├── styles.css
│ ├── script.js
├── requirements.txt
```
- Push the repository to GitHub.
2. **Set Up a Render Account:**
- Log in to [Render](https://render.com/) and create a new web service.
3. **Connect the Repository:**
- Link your GitHub repository to Render.
4. **Configure the Service:**
- Use `python backend/app.py` as the start command.
- Ensure the `PORT` environment variable is set by Render.
5. **Deploy the Application:**
- Render will automatically detect the changes and deploy the app.
6. **Access the Application:**
- Visit the live Render URL (e.g., `https://food-sales-webapp.onrender.com`) to access the web app.
### Access the Application Locally:
For local development:
1. **Set Up the Frontend:**
- Install and start Apache via XAMPP or WAMP.
- Place the frontend files in the Apache `htdocs` folder for local access.
2. **Access the Frontend:**
- Open your browser and go to `http://localhost/foodsales/index.html`.
3. **Test the Backend:**
- Ensure the Flask backend is running.
- Use the filter options to view food sales data based on your criteria.
---
Enjoy exploring food sales data with this intuitive web application!