| ---
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| license: mit
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| tags:
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| - health
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| - fitness
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| - calorie-prediction
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| - tensorflow
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| - wellness
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| datasets:
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| - synthetic
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| metrics:
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| - mae
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| - rmse
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| library_name: tensorflow
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| pipeline_tag: tabular-regression
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| ---
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|
|
| # 🏃♂️ VitaMind AI - Calorie Goal Predictor
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|
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| **Personalized daily calorie recommendations using AI**
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|
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| ## Model Description
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|
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| VitaMind AI predicts optimal daily calorie intake based on:
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| - Demographics (age, gender, height, weight)
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| - Activity metrics (steps, heart rate)
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| - Lifestyle factors (sleep, stress, mood)
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| - Activity level (sedentary to athlete)
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|
|
| ## Performance
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|
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| - **MAE**: 75 kcal
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| - **RMSE**: 95 kcal
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| - **MAPE**: 3.2%
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| - **R² Score**: 0.89
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|
|
| ## Quick Start
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|
|
| ```python
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| from huggingface_hub import hf_hub_download
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| import tensorflow as tf
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| import joblib
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| import numpy as np
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|
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| # Download model
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| model = tf.keras.models.load_model(
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| hf_hub_download(repo_id="developerPratik/vitamind-calorie-predictor", filename="model.keras")
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| )
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| scaler = joblib.load(
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| hf_hub_download(repo_id="developerPratik/vitamind-calorie-predictor", filename="scaler.joblib")
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| )
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| encoders = joblib.load(
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| hf_hub_download(repo_id="developerPratik/vitamind-calorie-predictor", filename="encoders.joblib")
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| )
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|
|
| # Example prediction
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| user_data = {
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| 'age': 30, 'weight': 75, 'height': 175, 'steps': 8000,
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| 'heart_rate': 72, 'sleep_hours': 7.5, 'stress_level': 4,
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| 'activity_level': 'Active', 'gender': 'M', 'mood': 'happy'
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| }
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|
|
| # Feature engineering
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| bmi = user_data['weight'] / ((user_data['height'] / 100) ** 2)
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| good_sleep = 1 if user_data['sleep_hours'] >= 7 else 0
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| high_stress = 1 if user_data['stress_level'] >= 7 else 0
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| activity_scores = {'Sedentary': 1, 'Lightly Active': 2, 'Active': 3, 'Very Active': 4, 'Athlete': 5}
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|
|
| # Encode
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| activity_encoded = encoders['activity_level'].transform([user_data['activity_level']])[0]
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| gender_encoded = encoders['gender'].transform([user_data['gender']])[0]
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| mood_encoded = encoders['mood'].transform([user_data['mood']])[0]
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|
|
| # Create feature vector (14 features)
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| features = np.array([[
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| user_data['age'], user_data['weight'], user_data['height'],
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| user_data['steps'], user_data['heart_rate'], user_data['sleep_hours'],
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| user_data['stress_level'], bmi, activity_encoded, gender_encoded,
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| mood_encoded, good_sleep, high_stress, activity_scores[user_data['activity_level']]
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| ]])
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|
|
| # Predict
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| features_scaled = scaler.transform(features)
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| calories = model.predict(features_scaled, verbose=0)[0][0]
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| print(f"Recommended daily calories: {calories:.0f} kcal")
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| ```
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|
|
| ## Model Architecture
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|
|
| ```
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| Input (14 features)
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| ↓
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| Dense(256) + BatchNorm + Dropout(0.3)
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| ↓
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| Dense(128) + BatchNorm + Dropout(0.3) [Residual Connection]
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| ↓
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| Dense(128) + BatchNorm + Dropout(0.3)
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| ↓
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| Dense(64) + BatchNorm + Dropout(0.2)
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| ↓
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| Output (1 - calories)
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| ```
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|
|
| **Total Parameters**: ~85,000
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|
|
| ## Features
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|
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| | Feature | Type | Description |
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| |---------|------|-------------|
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| | age | int | Age in years (18-100) |
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| | weight | float | Weight in kg (40-150) |
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| | height | float | Height in cm (140-220) |
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| | steps | int | Daily steps (0-30000) |
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| | heart_rate | int | Resting heart rate (50-120) |
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| | sleep_hours | float | Hours of sleep (3-12) |
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| | stress_level | int | Stress rating (1-10) |
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| | bmi | float | Calculated BMI |
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| | activity_level | str | Sedentary/Lightly Active/Active/Very Active/Athlete |
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| | gender | str | M/F |
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| | mood | str | happy/neutral/sad/anxious |
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| | good_sleep | binary | 1 if sleep >= 7 hours |
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| | high_stress | binary | 1 if stress >= 7 |
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| | activity_score | int | 1-5 based on activity level |
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|
|
| ## Limitations
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|
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| ⚠️ **Important Disclaimers**:
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| - For educational/wellness purposes only
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| - NOT a substitute for professional medical advice
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| - Individual metabolism varies significantly
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| - Does not account for medical conditions
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| - Consult healthcare providers for medical decisions
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|
|
| ## Training Details
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|
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| - **Framework**: TensorFlow 2.15
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| - **Training samples**: 5,000 synthetic
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| - **Validation split**: 15%
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| - **Test split**: 15%
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| - **Optimizer**: Adam (lr=0.001 with ReduceLROnPlateau)
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| - **Loss**: MSE
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| - **Regularization**: L2 (0.001) + Dropout + BatchNorm
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| - **Early stopping**: Patience=30
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|
|
| ## License
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|
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| MIT License - Free for commercial and personal use
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|
|
| ## Citation
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|
|
| ```bibtex
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| @software{vitamind_ai_2025,
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| author = {{Your Name}},
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| title = {{VitaMind AI Calorie Predictor}},
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| year = {2025},
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| publisher = {Hugging Face},
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| url = {{https://huggingface.co/developerPratik/vitamind-calorie-predictor}}
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| }
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| ```
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|
|
| ## Contact
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|
|
| - **Issues**: Open an issue on this model's discussion page
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| - **Email**: your.email@example.com
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|
|
| ---
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|
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| Built with ❤️ using TensorFlow and scikit-learn
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| |
