---
license: mit
tags:
- health
- fitness
- calorie-prediction
- tensorflow
- wellness
datasets:
- synthetic
metrics:
- mae
- rmse
library_name: tensorflow
pipeline_tag: tabular-regression
---

# 🏃‍♂️ VitaMind AI - Calorie Goal Predictor

**Personalized daily calorie recommendations using AI**

## Model Description

VitaMind AI predicts optimal daily calorie intake based on:
- Demographics (age, gender, height, weight)
- Activity metrics (steps, heart rate)
- Lifestyle factors (sleep, stress, mood)
- Activity level (sedentary to athlete)

## Performance

- **MAE**: 75 kcal
- **RMSE**: 95 kcal  
- **MAPE**: 3.2%
- **R² Score**: 0.89

## Quick Start

```python
from huggingface_hub import hf_hub_download
import tensorflow as tf
import joblib
import numpy as np

# Download model
model = tf.keras.models.load_model(
    hf_hub_download(repo_id="developerPratik/vitamind-calorie-predictor", filename="model.keras")
)
scaler = joblib.load(
    hf_hub_download(repo_id="developerPratik/vitamind-calorie-predictor", filename="scaler.joblib")
)
encoders = joblib.load(
    hf_hub_download(repo_id="developerPratik/vitamind-calorie-predictor", filename="encoders.joblib")
)

# Example prediction
user_data = {
    'age': 30, 'weight': 75, 'height': 175, 'steps': 8000,
    'heart_rate': 72, 'sleep_hours': 7.5, 'stress_level': 4,
    'activity_level': 'Active', 'gender': 'M', 'mood': 'happy'
}

# Feature engineering
bmi = user_data['weight'] / ((user_data['height'] / 100) ** 2)
good_sleep = 1 if user_data['sleep_hours'] >= 7 else 0
high_stress = 1 if user_data['stress_level'] >= 7 else 0
activity_scores = {'Sedentary': 1, 'Lightly Active': 2, 'Active': 3, 'Very Active': 4, 'Athlete': 5}

# Encode
activity_encoded = encoders['activity_level'].transform([user_data['activity_level']])[0]
gender_encoded = encoders['gender'].transform([user_data['gender']])[0]
mood_encoded = encoders['mood'].transform([user_data['mood']])[0]

# Create feature vector (14 features)
features = np.array([[
    user_data['age'], user_data['weight'], user_data['height'],
    user_data['steps'], user_data['heart_rate'], user_data['sleep_hours'],
    user_data['stress_level'], bmi, activity_encoded, gender_encoded,
    mood_encoded, good_sleep, high_stress, activity_scores[user_data['activity_level']]
]])

# Predict
features_scaled = scaler.transform(features)
calories = model.predict(features_scaled, verbose=0)[0][0]
print(f"Recommended daily calories: {calories:.0f} kcal")
```

## Model Architecture

```
Input (14 features)
    ↓
Dense(256) + BatchNorm + Dropout(0.3)
    ↓
Dense(128) + BatchNorm + Dropout(0.3)  [Residual Connection]
    ↓
Dense(128) + BatchNorm + Dropout(0.3)
    ↓
Dense(64) + BatchNorm + Dropout(0.2)
    ↓
Output (1 - calories)
```

**Total Parameters**: ~85,000

## Features

| Feature | Type | Description |
|---------|------|-------------|
| age | int | Age in years (18-100) |
| weight | float | Weight in kg (40-150) |
| height | float | Height in cm (140-220) |
| steps | int | Daily steps (0-30000) |
| heart_rate | int | Resting heart rate (50-120) |
| sleep_hours | float | Hours of sleep (3-12) |
| stress_level | int | Stress rating (1-10) |
| bmi | float | Calculated BMI |
| activity_level | str | Sedentary/Lightly Active/Active/Very Active/Athlete |
| gender | str | M/F |
| mood | str | happy/neutral/sad/anxious |
| good_sleep | binary | 1 if sleep >= 7 hours |
| high_stress | binary | 1 if stress >= 7 |
| activity_score | int | 1-5 based on activity level |

## Limitations

⚠️ **Important Disclaimers**:
- For educational/wellness purposes only
- NOT a substitute for professional medical advice
- Individual metabolism varies significantly
- Does not account for medical conditions
- Consult healthcare providers for medical decisions

## Training Details

- **Framework**: TensorFlow 2.15
- **Training samples**: 5,000 synthetic
- **Validation split**: 15%
- **Test split**: 15%
- **Optimizer**: Adam (lr=0.001 with ReduceLROnPlateau)
- **Loss**: MSE
- **Regularization**: L2 (0.001) + Dropout + BatchNorm
- **Early stopping**: Patience=30

## License

MIT License - Free for commercial and personal use

## Citation

```bibtex
@software{vitamind_ai_2025,
  author = {{Your Name}},
  title = {{VitaMind AI Calorie Predictor}},
  year = {2025},
  publisher = {Hugging Face},
  url = {{https://huggingface.co/developerPratik/vitamind-calorie-predictor}}
}
```

## Contact

- **Issues**: Open an issue on this model's discussion page
- **Email**: your.email@example.com

---

Built with ❤️ using TensorFlow and scikit-learn