---
title: Nutrition Regression
emoji: 🚀
colorFrom: blue
colorTo: green
sdk: gradio
sdk_version: 5.16.0
app_file: app.py
pinned: false
license: apache-2.0
---

Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

---
title: Nutrition Density Prediction App
emoji: 🍏
colorFrom: "#4CAF50"
colorTo: "#8BC34A"
sdk: gradio
sdk_version: "3.0.0"
app_file: app.py
pinned: false
---

# Nutrition Density Prediction App

This app predicts the **Nutrition Density** based on 10 selected nutrition features using either the **SVR** (Support Vector Regression) or **Linear Regression** model. The user can interact with the app by adjusting sliders for key nutrition features and selecting a model to predict the Nutrition Density.

## How It Works

1. **Model Selection**: Choose between two models - **SVR** or **Linear Regression**.
2. **Adjust Nutrition Features**: Adjust the following 10 key nutrition features using sliders:
   - **Caloric Value**
   - **Fat**
   - **Saturated Fats**
   - **Carbohydrates**
   - **Sugars**
   - **Protein**
   - **Cholesterol**
   - **Sodium**
   - **Calcium**
   - **Iron**
3. **Prediction**: Once the features are set, click on **"Predict"**. The selected model will calculate and display the Nutrition Density based on the inputs.
4. **Clear**: Reset the inputs to default values by clicking the **"Clear"** button.

## Deployment on Hugging Face Spaces

This app is deployed on [Hugging Face Spaces](https://huggingface.co/spaces), allowing users to interact with it easily via a web interface.

To use the app, follow these steps:

1. Visit the Hugging Face Spaces page for this app:  
   [Your Hugging Face Space Link](<Your Space URL>)

2. **Adjust the Sliders**: Choose your desired nutrition values for Caloric Value, Fat, Saturated Fats, Carbohydrates, Sugars, Protein, Cholesterol, Sodium, Calcium, and Iron.

3. **Select the Model**: Choose between **SVR** or **Linear Regression**.

4. **Click "Predict"**: The app will calculate the Nutrition Density based on your selections.

5. **Clear Inputs**: Reset all values to their initial state by clicking the **"Clear"** button.

## How to Run Locally (Optional)

If you'd like to run the app on your local machine, follow these instructions:

### Prerequisites

- Python 3.x

### Installation Steps

1. Clone the repository:
   ```bash
   git clone <repository_url>
   cd <project_directory>