### Start WebSocket Server (Python)

Source: https://github.com/agk4444/aditya369/blob/master/README.md

Demonstrates how to start the WebSocket server for real-time communication within the system. It shows the instantiation of the server and the method to initiate its operation.

```Python
from src.emotion_inference import EQMWebSocketServer

# Start WebSocket server
ws_server = EQMWebSocketServer(eqm_pipeline, host="localhost", port=8080)
await ws_server.start_server()
```

--------------------------------

### Install Dependencies

Source: https://github.com/agk4444/aditya369/blob/master/README.md

Installs project dependencies using pip by referencing the requirements.txt file. This command ensures all necessary Python packages are installed in the environment.

```Bash
pip install -r requirements.txt
```

--------------------------------

### Set Up Virtual Environment

Source: https://github.com/agk4444/aditya369/blob/master/README.md

Creates and activates a Python virtual environment for the project. This isolates project dependencies and includes installing core libraries like TensorFlow, pandas, and scikit-learn.

```Bash
# Create virtual environment
python -m venv eqm_env
source eqm_env/bin/activate  # On Windows: eqm_env\Scripts\activate

# Install dependencies
pip install tensorflow pandas numpy scipy scikit-learn aiohttp aiofiles
```

--------------------------------

### Quick Start EQM Inference Pipeline

Source: https://github.com/agk4444/aditya369/blob/master/README.md

Initializes and starts the EQM inference pipeline for real-time emotion detection. It requires a configured model path and allows for real-time processing with a specified prediction interval. The pipeline processes sensor data and outputs detected emotion and confidence.

```Python
import asyncio
import numpy as np
from src.emotion_inference import EQMInferencePipeline, EQMConfig

async def main():
    # Configure EQM system
    config = EQMConfig(
        model_path="./models/emotion_model.h5",
        enable_real_time=True,
        prediction_interval=1.0,
        sampling_rate=10
    )

    # Initialize and start EQM
    eqm = EQMInferencePipeline(config)
    await eqm.initialize()
    await eqm.start()

    # Process sensor data (example)
    sensor_data = np.array([72, 40, 2.1, 36.5, 98])  # HR, HRV, GSR, Temp, SpO2
    result = await eqm.process_sensor_data(sensor_data)

    print(f"Detected emotion: {result['emotion']} (confidence: {result['confidence']:.2f})")

    # Keep running for real-time processing
    await asyncio.sleep(3600)  # Run for 1 hour

if __name__ == "__main__":
    asyncio.run(main())
```

--------------------------------

### Clone Repository

Source: https://github.com/agk4444/aditya369/blob/master/README.md

Clones the EQM Aditya369 project repository from GitHub using Git. This is the first step in the installation process to obtain the project's source code.

```Bash
git clone https://github.com/your-repo/eqm-aditya369.git
cd eqm-aditya369
```

--------------------------------

### EQMInferencePipeline API Reference (Python)

Source: https://github.com/agk4444/aditya369/blob/master/README.md

Defines the main interface for the EQM system, including initialization, starting, stopping, and processing sensor data. It outlines the methods available for interacting with the core inference pipeline.

```Python
class EQMInferencePipeline:
    def __init__(self, config: EQMConfig)
    async def initialize() -> bool
    async def start() -> bool
    async def stop() -> bool
    async def process_sensor_data(sensor_data: np.ndarray) -> Dict[str, Any]
```

--------------------------------

### Python Webhook Integration for Oura Ring

Source: https://github.com/agk4444/aditya369/blob/master/docs/DATA_COLLECTION_PLAN.md

Provides an example of handling incoming webhook data from the Oura Ring. This asynchronous function is designed to be an endpoint that receives POST requests, parses the JSON payload, and validates and stores the incoming data. It's crucial for event-driven data updates.

```python
# Handle incoming webhooks from devices
@app.post("/webhook/oura")
async def oura_webhook(request: Request):
    payload = await request.json()
    await validate_and_store(payload)
```

--------------------------------

### Optional: API Documentation Dependencies (Python)

Source: https://github.com/agk4444/aditya369/blob/master/requirements.txt

Lists optional dependencies for building API documentation, specifically FastAPI for creating APIs and Uvicorn as an ASGI server.

```Python
# fastapi>=0.68.0
# uvicorn>=0.15.0
```

--------------------------------

### Contributing to Aditya369 Project

Source: https://github.com/agk4444/aditya369/blob/master/README.md

This section provides a standard workflow for contributing to the Aditya369 project using Git. It outlines the steps for forking the repository, creating a feature branch, committing changes, and opening a pull request.

```git
git checkout -b feature/AmazingFeature
git commit -m 'Add some AmazingFeature'
git push origin feature/AmazingFeature
```

--------------------------------

### Optional: Hyperparameter Tuning Dependencies (Python)

Source: https://github.com/agk4444/aditya369/blob/master/requirements.txt

Includes an optional dependency for hyperparameter optimization using Optuna.

```Python
# optuna>=2.10.0
```

--------------------------------

### EQMConfig Configuration (Python)

Source: https://github.com/agk4444/aditya369/blob/master/README.md

Shows the configuration options for the EQM system, including model paths, prediction intervals, buffer sizes, sampling rates, and feature flags for real-time processing, data ingestion, and preprocessing.

```Python
@dataclass
class EQMConfig:
    model_path: str                           # Path to trained model
    preprocessor_path: Optional[str] = None   # Path to preprocessor config
    prediction_interval: float = 1.0          # Prediction frequency (seconds)
    buffer_size_seconds: int = 300            # Data buffer size
    sampling_rate: int = 10                   # Sensor sampling rate
    enable_real_time: bool = True             # Enable real-time processing
    enable_data_ingestion: bool = True        # Enable data collection
    enable_preprocessing: bool = True         # Enable preprocessing
    storage_path: str = "./data"              # Data storage path
```

--------------------------------

### ModelTrainer API Reference (Python)

Source: https://github.com/agk4444/aditya369/blob/master/README.md

Provides the interface for model training and evaluation. It includes initialization and the method for training a model with specified data and configurations.

```Python
class ModelTrainer:
    def __init__(self, config: TrainingConfig)
    def train_model(model_config, X_train, y_train) -> Tuple[Model, TrainingResult]
```

--------------------------------

### EQM Project Structure - Python and Markdown

Source: https://github.com/agk4444/aditya369/blob/master/README.md

Provides an overview of the EQM project's directory structure, highlighting key components for data ingestion, preprocessing, model training, inference, and documentation. This structure facilitates organized development and maintenance.

```markdown
eqm_aditya369/
├── docs/                          # Documentation
│   ├── ARCHITECTURE.md           # System architecture
│   ├── DATA_COLLECTION_PLAN.md   # Data collection strategy
│   ├── DATA_PROCESSING_FEATURE_ENGINEERING.md
│   ├── ML_MODEL_ARCHITECTURE.md  # ML model designs
│   └── USE_CASES.md              # Real-world applications
├── src/
│   ├── data_ingestion/           # Data collection components
│   │   ├── device_connectors.py  # Device-specific connectors
│   │   ├── data_validator.py     # Data validation and quality checks
│   │   ├── ingestion_pipeline.py # Data ingestion orchestration
│   │   └── storage_manager.py    # Data persistence
│   ├── data_preprocessing/       # Data preprocessing components
│   │   ├── data_cleaner.py       # Data cleaning and outlier removal
│   │   ├── feature_extractor.py  # Feature extraction algorithms
│   │   └── preprocessing_pipeline.py # Preprocessing orchestration
│   ├── model_training/           # Model training components
│   │   ├── model_builder.py      # Neural network architectures
│   │   └── trainer.py            # Training and evaluation
│   └── emotion_inference/        # Real-time inference components
│       ├── emotion_predictor.py  # Core prediction engine
│       ├── real_time_processor.py # Real-time processing
│       └── inference_pipeline.py # Complete pipeline
├── models/                       # Trained models (generated)
├── data/                         # Data storage (generated)
│   ├── raw/                      # Raw sensor data
│   ├── processed/                # Preprocessed features
│   └── intermediate/             # Temporary processing files
└── README.md                     # This file
```

--------------------------------

### Async and Networking Dependencies (Python)

Source: https://github.com/agk4444/aditya369/blob/master/requirements.txt

Lists the necessary libraries for asynchronous operations and network communication. This includes aiohttp and aiofiles for asynchronous HTTP clients/servers and file operations, websockets for WebSocket communication, and requests for standard HTTP requests.

```Python
aiohttp>=3.8.0
aiofiles>=0.8.0
websockets>=10.0
requests>=2.25.0
```

--------------------------------

### Optional: Additional Data Formats Dependencies (Python)

Source: https://github.com/agk4444/aditya369/blob/master/requirements.txt

Specifies optional dependencies for handling additional data formats, including PyArrow for Apache Arrow data and openpyxl for reading/writing Excel files.

```Python
# pyarrow>=6.0.0
# openpyxl>=3.0.0
```

--------------------------------

### Optional: Advanced ML Model Dependencies (Python)

Source: https://github.com/agk4444/aditya369/blob/master/requirements.txt

Lists optional dependencies for working with advanced machine learning models, specifically PyTorch (torch) and Torchvision for computer vision tasks.

```Python
# torch>=1.9.0
# torchvision>=0.10.0
```

--------------------------------

### Scientific Computing and Signal Processing Dependencies (Python)

Source: https://github.com/agk4444/aditya369/blob/master/requirements.txt

Outlines the libraries for scientific computing and signal processing tasks. This includes Matplotlib and Seaborn for data visualization and PyWavelets (pywt) for wavelet transforms.

```Python
matplotlib>=3.4.0
seaborn>=0.11.0
pywt>=1.1.0
```

--------------------------------

### Feed Sensor Data Continuously (Python)

Source: https://github.com/agk4444/aditya369/blob/master/README.md

This snippet demonstrates the continuous feeding of sensor data into the system. It includes fetching sensor readings, adding them to a processor, and managing sampling rate using asyncio.

```Python
while True:
    sensor_data = get_sensor_reading()  # Your sensor data source
    processor.add_sensor_data(sensor_data)
    await asyncio.sleep(0.1)  # 10Hz sampling
```

--------------------------------

### Data Storage and Processing Dependencies (Python)

Source: https://github.com/agk4444/aditya369/blob/master/requirements.txt

Details the libraries required for asynchronous data storage and processing. This includes aiosqlite for asynchronous SQLite database access and aioredis for asynchronous Redis client operations.

```Python
aiosqlite>=0.17.0
aioredis>=2.0.0
```

--------------------------------

### EmotionPredictor API Reference (Python)

Source: https://github.com/agk4444/aditya369/blob/master/README.md

Details the core emotion prediction engine. It includes initialization, loading the prediction model, and the method for predicting emotions based on sensor data.

```Python
class EmotionPredictor:
    def __init__(self, config: PredictionConfig)
    def load_model() -> bool
    def predict_emotion(sensor_data: np.ndarray) -> EmotionPrediction
```

--------------------------------

### Development and Testing Dependencies (Python)

Source: https://github.com/agk4444/aditya369/blob/master/requirements.txt

Specifies the libraries needed for project development and testing. This includes Pytest for running tests and pytest-asyncio for integrating asynchronous code with Pytest.

```Python
pytest>=6.2.0
pytest-asyncio>=0.15.0
```

--------------------------------

### Quantize TensorFlow Model for Edge Deployment

Source: https://github.com/agk4444/aditya369/blob/master/docs/ML_MODEL_ARCHITECTURE.md

Quantizes a Keras model to TensorFlow Lite format for efficient deployment on edge devices. Supports default optimizations and FP16 type conversion.

```python
import tensorflow as tf

def quantize_model_for_edge(model, optimization_level='DEFAULT'):
    converter = tf.lite.TFLiteConverter.from_keras_model(model)

    if optimization_level == 'DEFAULT':
        converter.optimizations = [tf.lite.Optimize.DEFAULT]
    elif optimization_level == 'FLOAT16':
        converter.target_spec.supported_types = [tf.float16]

    quantized_model = converter.convert()
    return quantized_model
```

--------------------------------

### Adaptive Learning System with EQM Integration

Source: https://github.com/agk4444/aditya369/blob/master/docs/USE_CASES.md

Implements an adaptive learning system that monitors student engagement using EQM. It adjusts learning difficulty based on detected emotions like sadness (confusion) or happiness (engagement).

```Python
# Assuming necessary imports and client setup
# from eqm_sdk import EQMClient
# async def get_student_wearable_data(student_id):
#     # Placeholder for fetching sensor data
#     return {"heart_rate": 75, "gsr": 1000}
# def provide_additional_explanation():
#     print("Providing additional explanation.")
# def present_challenge_question():
#     print("Presenting a challenge question.")

# Educational emotion monitoring
class AdaptiveLearningSystem:
    def __init__(self, eqm_pipeline):
        self.eqm = eqm_pipeline
        self.current_difficulty = "medium"

    async def monitor_student_engagement(self, student_id):
        sensor_data = await get_student_wearable_data(student_id)
        # Assuming process_sensor_data returns a dictionary with 'emotion' and 'confidence'
        result = await self.eqm.process_sensor_data(sensor_data)

        if result['emotion'] == 'sad' and result['confidence'] > 0.7:
            # Student is confused - provide additional help
            self.adjust_difficulty("easier")
            provide_additional_explanation()

        elif result['emotion'] == 'happy' and result['confidence'] > 0.8:
            # Student is engaged and understanding
            self.adjust_difficulty("harder")
            present_challenge_question()

    def adjust_difficulty(self, level):
        self.current_difficulty = level
        print(f"Difficulty adjusted to: {level}")

# Example usage (requires an initialized eqm_pipeline)
# async def main():
#     eqm_client = EQMClient()
#     adaptive_system = AdaptiveLearningSystem(eqm_client)
#     await adaptive_system.monitor_student_engagement("student123")

# import asyncio
# asyncio.run(main())
```

--------------------------------

### Real-Time Emotion Processing

Source: https://github.com/agk4444/aditya369/blob/master/README.md

Sets up and runs a real-time emotion processor that utilizes a pre-trained emotion predictor. It allows for configuring prediction intervals, buffer sizes, and adaptive prediction. A callback function can be registered to handle emotion detection results, printing the detected emotion and confidence.

```Python
from src.emotion_inference import RealTimeEmotionProcessor, RealTimeConfig
from src.emotion_inference import EmotionPredictor, PredictionConfig

# Configure predictor
prediction_config = PredictionConfig(
    model_path="./models/emotion_model.h5",
    sequence_length=300,
    confidence_threshold=0.6
)

predictor = EmotionPredictor(prediction_config)
predictor.load_model()

# Configure real-time processor
rt_config = RealTimeConfig(
    prediction_interval=1.0,
    buffer_size_seconds=300,
    enable_adaptive_prediction=True
)

processor = RealTimeEmotionProcessor(predictor, rt_config)

# Add emotion callback
async def emotion_callback(prediction, emotion_state):
    print(f"Emotion: {prediction.emotion}, Confidence: {prediction.confidence:.2f}")

processor.add_emotion_callback(emotion_callback)

# Start processing
await processor.start_processing()
```

--------------------------------

### Oura Ring API Client

Source: https://github.com/agk4444/aditya369/blob/master/docs/DATA_COLLECTION_PLAN.md

Python class to interact with the Oura Ring API v2. It handles authentication and fetching heart rate data within a specified date range. Requires an access token for initialization.

```python
class OuraClient:
    def __init__(self, access_token):
        self.base_url = "https://api.ouraring.com/v2"
        self.headers = {"Authorization": f"Bearer {access_token}"}

    async def get_heart_rate_data(self, start_date, end_date):
        endpoint = f"/usercollection/heartrate"
        params = {
            "start_date": start_date,
            "end_date": end_date
        }
        response = await self._make_request(endpoint, params)
        return response
```

--------------------------------

### Aditya369 Project Citation

Source: https://github.com/agk4444/aditya369/blob/master/README.md

This is the BibTeX entry for citing the EQM (Aditya369) project in academic research. It includes the title, author, year, and URL for the project.

```bibtex
@software{eqm_aditya369,
  title={EQM (Aditya369): Emotional Quotient Model for Physiological Emotion Detection},
  author={AGK FIRE INC},
  year={2023},
  url={https://github.com/agk4444/Aditya369}
}
```

--------------------------------

### Emotion-Adaptive Gaming Difficulty Adjustment (Python)

Source: https://github.com/agk4444/aditya369/blob/master/docs/USE_CASES.md

Adapts game difficulty and content based on the player's emotional state. It processes player sensor data to detect emotions like 'fear' or 'happy' and adjusts gameplay accordingly, such as enabling 'easy_mode' or 'challenge_mode'. Requires an EQM pipeline.

```Python
# Emotion-adaptive gaming
class EmotionAdaptiveGame:
    def __init__(self, eqm_pipeline):
        self.eqm = eqm_pipeline
        self.game_state = "normal"

    async def adapt_gameplay(self, player_data):
        result = await self.eqm.process_sensor_data(player_data)

        if result['emotion'] == 'fear' and result['confidence'] > 0.8:
            # Player is getting frustrated - ease difficulty
            self.game_state = "easy_mode"
            reduce_enemy_count()
            provide_power_ups()

        elif result['emotion'] == 'happy' and result['confidence'] > 0.7:
            # Player is engaged - increase challenge
            self.game_state = "challenge_mode"
            increase_difficulty()
            unlock_bonus_content()
```

--------------------------------

### Prepare Data Splits

Source: https://github.com/agk4444/aditya369/blob/master/docs/ML_MODEL_ARCHITECTURE.md

Splits data into training, validation, and testing sets based on specified ratios. This function is designed for time-series data, ensuring that the temporal order is maintained during the split.

```Python
def prepare_data_splits(data, train_ratio=0.7, val_ratio=0.15, test_ratio=0.15):
    # Time-based split to maintain temporal order
    n_samples = len(data)
    train_end = int(n_samples * train_ratio)
    val_end = int(n_samples * (train_ratio + val_ratio))

    train_data = data[:train_end]
    val_data = data[train_end:val_end]
    test_data = data[val_end:]

    return train_data, val_data, test_data
```

--------------------------------

### FastAPI Model Serving for Emotion Prediction (FastAPI)

Source: https://github.com/agk4444/aditya369/blob/master/docs/ML_MODEL_ARCHITECTURE.md

Provides a FastAPI endpoint for real-time emotion prediction. It defines a POST request handler that accepts sensor data, validates it, uses an `emotion_classifier` to predict the emotion, and returns the predicted emotion label, confidence, and timestamp. Includes error handling for invalid data. Requires a pre-configured `emotion_classifier` instance and data validation functions.

```Python
from fastapi import FastAPI, HTTPException
from pydantic import BaseModel
from datetime import datetime

# Assume SensorDataRequest, validate_sensor_data, emotion_classifier, and emotion_labels are defined elsewhere

# Placeholder for request model
class SensorDataRequest(BaseModel):
    data: dict # Replace with actual sensor data structure

# Placeholder for FastAPI app instance
app = FastAPI()

# Placeholder for validation function
def validate_sensor_data(data):
    # Implement data validation logic
    return data # Return processed data or raise HTTPException

# Placeholder for emotion classifier instance
# emotion_classifier = RealTimeEmotionClassifier(...) 

# Placeholder for emotion labels mapping
# emotion_labels = {0: 'happy', 1: 'sad', ...}

@app.post("/predict_emotion")
async def predict_emotion(request: SensorDataRequest):
    # Validate and preprocess request data
    sensor_data = validate_sensor_data(request.data)

    # Make prediction
    prediction = emotion_classifier.predict_emotion(sensor_data)

    if prediction:
        return {
            "emotion": emotion_labels[prediction['emotion']],
            "confidence": float(prediction['confidence']),
            "timestamp": datetime.utcnow().isoformat()
        }

    raise HTTPException(status_code=400, detail="Insufficient data for prediction")
```

--------------------------------

### Consumer Emotional Response Analysis

Source: https://github.com/agk4444/aditya369/blob/master/docs/USE_CASES.md

Analyzes consumer emotional responses to products in real-time. It processes consumer data using an EQM pipeline, tracks responses per product, and calculates metrics like average emotion and happiness scores.

```Python
# Consumer response analysis
class ConsumerResponseAnalyzer:
    def __init__(self, eqm_pipeline):
        self.eqm = eqm_pipeline
        self.product_responses = {}

    async def analyze_product_response(self, product_id, consumer_data):
        result = await self.eqm.process_sensor_data(consumer_data)

        if product_id not in self.product_responses:
            self.product_responses[product_id] = []

        self.product_responses[product_id].append(result)

        # Calculate average emotional response
        responses = self.product_responses[product_id]
        avg_happiness = sum(1 for r in responses if r['emotion'] == 'happy') / len(responses)

        return {
            'product_id': product_id,
            'average_emotion': result['emotion'],
            'happiness_score': avg_happiness,
            'total_responses': len(responses)
        }
```

--------------------------------

### Data Flow Architecture

Source: https://github.com/agk4444/aditya369/blob/master/docs/ARCHITECTURE.md

Illustrates the data flow from smart devices through various processing stages to the final applications, showing the transformation of raw sensor data into actionable insights.

```Diagram
Smart Devices → Data Collection → Processing Pipeline → ML Engine → Applications
     ↓              ↓              ↓              ↓              ↓
   Sensors     Raw Data      Features      Predictions    Insights
   (HR, HRV,   (JSON)       (Vectors)     (Emotions)     (Reports)
    Temp, GSR)
```

--------------------------------

### EQM System Architecture - Multi-Modal Emotion Detection

Source: https://github.com/agk4444/aditya369/blob/master/README.md

Illustrates the flow of data from physiological, visual, and acoustic sources to a unified emotion prediction output. This represents the core multi-modal approach of the EQM system.

```text
Physiological (HR, HRV, GSR) + Visual (Eye gaze, facial) + Acoustic (Voice, tone)
                       ↓
                Unified Emotion Prediction
```

--------------------------------

### Real-time Feature Processing with Python Class

Source: https://github.com/agk4444/aditya369/blob/master/docs/DATA_PROCESSING_FEATURE_ENGINEERING.md

Implements a real-time feature processing pipeline using a Python class. It manages data buffers for streaming sensor data and utilizes feature extractors to compute features when sufficient data is available. Requires `collections.deque` and a configuration dictionary.

```Python
class RealTimeFeatureProcessor:
    def __init__(self, feature_config):
        self.config = feature_config
        self.buffers = {}
        self.feature_extractors = self._initialize_extractors()

    def _initialize_extractors(self):
        # Placeholder for initializing feature extractor functions
        # Example: return {'mean_extractor': lambda data: {'mean': data['sensor1'].mean()}}
        return {}

    def _has_sufficient_data(self):
        # Placeholder for checking if buffers have enough data
        # Example: return all(len(buffer) >= self.config['min_data_points'] for buffer in self.buffers.values())
        return True

    def process_streaming_data(self, sensor_data):
        # Update buffers with new data
        for sensor_type, values in sensor_data.items():
            if sensor_type not in self.buffers:
                self.buffers[sensor_type] = deque(maxlen=self.config['buffer_size'])
            self.buffers[sensor_type].extend(values)

        # Extract features when sufficient data available
        if self._has_sufficient_data():
            features = {}
            for extractor_name, extractor in self.feature_extractors.items():
                features.update(extractor(self.buffers))

            return features
        return None
```

--------------------------------

### Core ML and Data Processing Dependencies (Python)

Source: https://github.com/agk4444/aditya369/blob/master/requirements.txt

Specifies the required versions for core machine learning and data processing libraries. This includes TensorFlow for ML, NumPy for numerical operations, Pandas for data manipulation, Scikit-learn for machine learning algorithms, and SciPy for scientific and technical computing.

```Python
tensorflow>=2.8.0
numpy>=1.21.0
pandas>=1.3.0
scikit-learn>=1.0.0
scipy>=1.7.0
```

--------------------------------

### Mental Health Monitoring with EQM Alerts

Source: https://github.com/agk4444/aditya369/blob/master/docs/USE_CASES.md

Sets up EQM for continuous mental health monitoring with frequent predictions and alerts. It defines a callback function to send caregiver alerts and suggest coping strategies when distress is detected.

```Python
from eqm_sdk import EQMConfig, EQMClient

# Assuming EQMClient and necessary functions are defined elsewhere
# eqm = EQMClient()
# send_caregiver_alert = lambda pred: print(f"Alert: {pred.emotion} detected with confidence {pred.confidence}")
# suggest_coping_strategy = lambda emotion: print(f"Suggestion: Try a coping strategy for {emotion}")

# Mental health monitoring setup
eqm_config = EQMConfig(
    model_path="./models/mental_health_model.h5",
    prediction_interval=0.5,  # More frequent monitoring
    enable_alerts=True,
    alert_threshold=0.8
)

# Real-time monitoring with alerts
async def mental_health_callback(prediction, emotion_state):
    if prediction.emotion in ['sad', 'angry', 'fear']:
        if prediction.confidence > 0.8:
            send_caregiver_alert(prediction)
            suggest_coping_strategy(prediction.emotion)

# eqm.add_emotion_callback(mental_health_callback)
```

--------------------------------

### Emotion Classification Metrics

Source: https://github.com/agk4444/aditya369/blob/master/docs/ML_MODEL_ARCHITECTURE.md

Defines a list of common metrics for evaluating emotion classification models. Includes accuracy, precision, recall, AUC, and F1-score.

```Python
def emotion_classification_metrics():
    return [
        'accuracy',
        tf.keras.metrics.Precision(name='precision'),
        tf.keras.metrics.Recall(name='recall'),
        tf.keras.metrics.AUC(name='auc'),
        F1Score(num_classes=7, name='f1_score')
    ]
```

--------------------------------

### Python WebSocket Streaming Handler

Source: https://github.com/agk4444/aditya369/blob/master/docs/DATA_COLLECTION_PLAN.md

Handles real-time data streaming via WebSockets. This asynchronous function receives messages, parses them as JSON, and processes the streaming data. It's designed for continuous, low-latency data flow from devices or services that support WebSocket connections.

```python
# Real-time data streaming
async def websocket_handler(websocket, path):
    async for message in websocket:
        data = json.loads(message)
        await process_streaming_data(data)
```

--------------------------------

### Athlete Performance Emotional State Monitoring (Python)

Source: https://github.com/agk4444/aditya369/blob/master/docs/USE_CASES.md

Monitors athletes' emotional states during training and competition to optimize performance. It identifies optimal emotional states for different sports and detects performance anxiety, alerting coaches when necessary. Requires an EQM pipeline for sensor data processing.

```Python
# Athlete performance monitoring
class AthletePerformanceMonitor:
    def __init__(self, eqm_pipeline):
        self.eqm = eqm_pipeline
        self.optimal_states = {
            'basketball': ['happy', 'surprise'],
            'swimming': ['neutral', 'happy'],
            'weightlifting': ['neutral', 'angry']
        }

    async def monitor_performance_state(self, athlete_id, sport, sensor_data):
        result = await self.eqm.process_sensor_data(sensor_data)

        optimal_emotions = self.optimal_states.get(sport, ['neutral'])

        if result['emotion'] in optimal_emotions and result['confidence'] > 0.7:
            # Athlete in optimal emotional state
            log_performance_peak(athlete_id, result)

        elif result['emotion'] == 'fear' and result['confidence'] > 0.8:
            # Athlete experiencing performance anxiety
            alert_coach(athlete_id, result)
            suggest_confidence_building_technique()
```

--------------------------------

### Emotion Prediction Format (JSON)

Source: https://github.com/agk4444/aditya369/blob/master/README.md

Defines the output format for emotion predictions, including the predicted emotion, confidence scores, probabilities for different emotions, and processing timestamps.

```JSON
{
  "emotion": "happy",
  "confidence": 0.87,
  "probabilities": {
    "neutral": 0.02,
    "happy": 0.87,
    "sad": 0.05,
    "angry": 0.03,
    "fear": 0.01,
    "surprise": 0.01,
    "disgust": 0.01
  },
  "timestamp": "2023-12-01T10:30:01Z",
  "processing_time": 0.023
}
```

--------------------------------

### Create Sliding Window Features

Source: https://github.com/agk4444/aditya369/blob/master/docs/DATA_PROCESSING_FEATURE_ENGINEERING.md

Generates features from time-series data using rolling statistics (mean, std, min, max) and rate of change over specified window sizes. Requires Pandas.

```Python
import pandas as pd

def create_sliding_window_features(data, window_sizes=[30, 60, 120]):
    window_features = {}

    for window_size in window_sizes:
        # Rolling statistics
        window_features[f'mean_{window_size}s'] = data.rolling(window=window_size).mean()
        window_features[f'std_{window_size}s'] = data.rolling(window=window_size).std()
        window_features[f'min_{window_size}s'] = data.rolling(window=window_size).min()
        window_features[f'max_{window_size}s'] = data.rolling(window=window_size).max()

        # Rate of change
        window_features[f'rate_of_change_{window_size}s'] = data.diff(window_size) / window_size

    return pd.DataFrame(window_features)
```

--------------------------------

### Train EQM Model

Source: https://github.com/agk4444/aditya369/blob/master/README.md

Trains a machine learning model for emotion inference using provided configurations and synthetic data. It supports various model types like CNN and allows for specifying training parameters such as epochs, batch size, and validation split. The output includes the trained model and training results like accuracy.

```Python
import numpy as np
from src.model_training import ModelTrainer, ModelConfig, TrainingConfig

# Create synthetic training data
n_samples = 1000
sequence_length = 300
n_features = 5

# Generate emotion-specific patterns
emotions = ['neutral', 'happy', 'sad', 'angry', 'fear', 'surprise', 'disgust']
X_train = []
y_train = []

for i, emotion in enumerate(emotions):
    # Create emotion-specific physiological patterns
    samples = np.random.randn(100, sequence_length, n_features)
    # Add emotion-specific modifications here

    X_train.extend(samples)
    y_train.extend([i] * 100)

X_train = np.array(X_train)
y_train = np.array(y_train)

# Configure and train model
model_config = ModelConfig(
    model_type='cnn',
    input_shape=(sequence_length, n_features),
    num_classes=len(emotions)
)

training_config = TrainingConfig(
    epochs=100,
    batch_size=32,
    validation_split=0.2
)

trainer = ModelTrainer(training_config)
model, result = trainer.train_model(model_config, X_train, y_train)

print(f"Training completed. Accuracy: {result.val_accuracy:.4f}")
```

--------------------------------

### Real-Time Emotion Classifier Pipeline (Python)

Source: https://github.com/agk4444/aditya369/blob/master/docs/ML_MODEL_ARCHITECTURE.md

Implements a real-time emotion classifier using a loaded Keras model and a feature processor. It maintains a buffer of recent predictions to smooth output using a moving average. The class processes sensor data, makes predictions, and returns the most probable emotion along with its confidence. Requires a trained TensorFlow/Keras model and a feature processing object.

```Python
import tensorflow as tf
import numpy as np
from collections import deque

class RealTimeEmotionClassifier:
    def __init__(self, model_path, feature_processor):
        self.model = tf.keras.models.load_model(model_path)
        self.feature_processor = feature_processor
        self.prediction_buffer = deque(maxlen=30)  # 30-second buffer

    def predict_emotion(self, sensor_data):
        # Extract features from recent sensor data
        features = self.feature_processor.process(sensor_data)

        if features is not None:
            # Make prediction
            prediction = self.model.predict(np.expand_dims(features, axis=0))[0]

            # Add to prediction buffer for smoothing
            self.prediction_buffer.append(prediction)

            # Smooth predictions using moving average
            smoothed_prediction = np.mean(list(self.prediction_buffer), axis=0)

            # Get emotion with highest probability
            emotion_idx = np.argmax(smoothed_prediction)
            confidence = smoothed_prediction[emotion_idx]

            return {
                'emotion': emotion_idx,
                'confidence': confidence,
                'probabilities': smoothed_prediction
            }

        return None
```

--------------------------------

### Stress Pattern Detection

Source: https://github.com/agk4444/aditya369/blob/master/docs/USE_CASES.md

Analyzes a history of detected emotions to identify stress levels. It calculates the ratio of stress-related emotions in the recent history to categorize stress as high, moderate, or low.

```Python
# Stress detection and intervention
def detect_stress_pattern(emotion_history):
    stress_indicators = ['angry', 'fear', 'surprise']
    recent_emotions = emotion_history[-10:]  # Last 10 predictions

    stress_count = sum(1 for emotion in recent_emotions if emotion in stress_indicators)
    stress_ratio = stress_count / len(recent_emotions)

    if stress_ratio > 0.6:
        return "high_stress"
    elif stress_ratio > 0.3:
        return "moderate_stress"
    else:
        return "low_stress"
```

--------------------------------

### Sensor Data Format (JSON)

Source: https://github.com/agk4444/aditya369/blob/master/README.md

Specifies the structure for sensor data, including device information, timestamps, sensor type, value, unit, and metadata such as confidence and quality.

```JSON
{
  "device_id": "apple_watch_001",
  "user_id": "user_001",
  "timestamp": "2023-12-01T10:30:00Z",
  "sensor_type": "heart_rate",
  "value": 72.5,
  "unit": "BPM",
  "metadata": {
    "confidence": 0.95,
    "quality": "good"
  }
}
```

--------------------------------

### Create Feature Fusion Model

Source: https://github.com/agk4444/aditya369/blob/master/docs/ML_MODEL_ARCHITECTURE.md

Defines a Keras model that fuses features from multiple input sources. It takes a dictionary of feature dimensions and the number of output classes, creating separate input layers and dense layers for each feature before concatenating and passing them through fusion layers to a final softmax output.

```Python
def create_feature_fusion_model(feature_dims, num_classes):
    inputs = {}
    fusion_layers = []

    # Separate inputs for different feature types
    for feature_name, dim in feature_dims.items():
        inputs[feature_name] = Input(shape=(dim,), name=feature_name)
        fusion_layers.append(Dense(64, activation='relu')(inputs[feature_name]))

    # Concatenate all features
    concatenated = Concatenate()(fusion_layers)

    # Fusion layers
    fusion = Dense(256, activation='relu')(concatenated)
    fusion = Dropout(0.4)(fusion)
    fusion = Dense(128, activation='relu')(fusion)
    fusion = Dropout(0.3)(fusion)

    # Output layer
    output = Dense(num_classes, activation='softmax')(fusion)

    model = Model(inputs=inputs, outputs=output)
    return model
```

--------------------------------

### Batch Feature Processing with Python Class

Source: https://github.com/agk4444/aditya369/blob/master/docs/DATA_PROCESSING_FEATURE_ENGINEERING.md

Provides a Python class for batch feature processing. It loads raw data from a CSV file, applies data cleaning, extracts features, selects relevant features, and saves the processed data to a new CSV file. Requires pandas and json for configuration loading.

```Python
class BatchFeatureProcessor:
    def __init__(self, config_path):
        with open(config_path, 'r') as f:
            self.config = json.load(f)

    def _apply_data_cleaning(self, data):
        # Placeholder for data cleaning steps
        return data

    def _extract_all_features(self, data):
        # Placeholder for feature extraction logic
        return data

    def _select_features(self, features):
        # Placeholder for feature selection logic
        return features

    def process_dataset(self, raw_data_path, output_path):
        # Load raw data
        raw_data = pd.read_csv(raw_data_path)

        # Apply processing pipeline
        processed_data = self._apply_data_cleaning(raw_data)
        features = self._extract_all_features(processed_data)
        selected_features = self._select_features(features)

        # Save processed features
        selected_features.to_csv(output_path, index=False)
        return selected_features
```

--------------------------------

### Analyze Feature Importance with Permutation (Python)

Source: https://github.com/agk4444/aditya369/blob/master/docs/ML_MODEL_ARCHITECTURE.md

Analyzes feature importance using the permutation importance technique. It evaluates the model's performance on a test set, then iteratively permutes the values of each feature and re-evaluates performance to measure the impact of that feature. Returns a sorted list of feature names and their importance scores. Requires a trained model, test data, and feature names.

```Python
import numpy as np

def analyze_feature_importance(model, X_test, y_test, feature_names):
    # Permutation importance
    baseline_accuracy = model.evaluate(X_test, y_test)[1] # Assuming accuracy is the metric at index 1

    importance_scores = {}
    for i, feature_name in enumerate(feature_names):
        # Permute feature values
        X_permuted = X_test.copy()
        X_permuted[:, i] = np.random.permutation(X_permuted[:, i])

        # Calculate drop in performance
        permuted_accuracy = model.evaluate(X_permuted, y_test)[1]
        importance_scores[feature_name] = baseline_accuracy - permuted_accuracy

    return sorted(importance_scores.items(), key=lambda x: x[1], reverse=True)
```

--------------------------------

### Extract PSD Features in Python

Source: https://github.com/agk4444/aditya369/blob/master/docs/DATA_PROCESSING_FEATURE_ENGINEERING.md

Calculates Power Spectral Density (PSD) features using the Welch method. It computes power and peak frequencies within defined bands (very low, low, high) for signal analysis.

```Python
def extract_psd_features(data, sampling_rate=10):
    frequencies, psd = welch(data, fs=sampling_rate, nperseg=256)

    features = {}

    # Power in different frequency bands
    bands = {
        'very_low': (0, 0.04),
        'low': (0.04, 0.15),
        'high': (0.15, 0.4)
    }

    for band_name, (low_freq, high_freq) in bands.items():
        mask = (frequencies >= low_freq) & (frequencies < high_freq)
        features[f'{band_name}_power'] = np.sum(psd[mask])
        features[f'{band_name}_peak'] = frequencies[mask][np.argmax(psd[mask])]

    return features
```

--------------------------------

### Apply Model Pruning for Size Reduction

Source: https://github.com/agk4444/aditya369/blob/master/docs/ML_MODEL_ARCHITECTURE.md

Applies model pruning using the TensorFlow Model Optimization Toolkit to reduce the size of a machine learning model. It utilizes a low-magnitude pruning schedule.

```python
import tensorflow_model_optimization as tfmot

def apply_model_pruning(model, pruning_schedule):
    # Apply pruning to reduce model size
    pruned_model = tfmot.sparsity.keras.prune_low_magnitude(
        model,
        pruning_schedule=pruning_schedule
    )

    return pruned_model
```

--------------------------------

### Swift HealthKit API Authorization for Apple Watch

Source: https://github.com/agk4444/aditya369/blob/master/docs/DATA_COLLECTION_PLAN.md

Demonstrates requesting authorization for accessing health data from Apple Watch using HealthKit in Swift. It shows the process of requesting permissions for specific health data types and initiating data queries upon successful authorization.

```swift
# Using Apple's HealthKit framework via native iOS app
HKHealthStore.shared.requestAuthorization(toShare: nil, read: healthTypes) { (success, error) in
    if success {
        startHeartRateQuery()
    }
}
```

--------------------------------

### Corporate Wellness Stress Monitoring (Python)

Source: https://github.com/agk4444/aditya369/blob/master/docs/USE_CASES.md

Monitors workplace stress levels for employees using sensor data. It compares current emotional states against a baseline to detect elevated stress and trigger alerts or suggest interventions. Requires an EQM pipeline for data processing.

```Python
# Workplace wellness monitoring
class CorporateWellnessMonitor:
    def __init__(self, eqm_pipeline):
        self.eqm = eqm_pipeline
        self.employee_baseline = {}

    async def monitor_workplace_stress(self, employee_id, current_data):
        result = await self.eqm.process_sensor_data(current_data)

        # Compare with employee's baseline
        baseline_stress = self.employee_baseline.get(employee_id, {}).get('avg_stress', 0.5)

        if result['emotion'] in ['angry', 'fear'] and result['confidence'] > baseline_stress + 0.2:
            # Employee showing elevated stress
            alert_manager(employee_id, result)
            suggest_break_or_meditation(employee_id)
```

--------------------------------

### Extract Activity Features

Source: https://github.com/agk4444/aditya369/blob/master/docs/DATA_PROCESSING_FEATURE_ENGINEERING.md

Computes features related to physical activity intensity, movement patterns, and posture from accelerometer data. Requires NumPy and SciPy.

```Python
import numpy as np
import scipy.signal

def extract_activity_features(accel_data, window_size=60):
    features = {}

    # Activity intensity
    magnitude = np.sqrt(np.sum(accel_data**2, axis=1))
    features['activity_intensity'] = np.mean(magnitude)
    features['activity_std'] = np.std(magnitude)

    # Movement patterns
    features['dominant_frequency'] = scipy.signal.find_peaks(magnitude)[0].mean()

    # Posture indicators (simplified)
    features['vertical_movement'] = np.std(accel_data[:, 2])  # Z-axis
    features['horizontal_movement'] = np.sqrt(np.std(accel_data[:, 0])**2 + np.std(accel_data[:, 1])**2)

    return features
```

--------------------------------

### Visualize Attention Weights (Python)

Source: https://github.com/agk4444/aditya369/blob/master/docs/ML_MODEL_ARCHITECTURE.md

Visualizes attention weights from a transformer model using a heatmap. It extracts attention weights from a specified layer and plots them against time steps and emotion labels. This helps understand which parts of the input sequence influence the prediction for different emotions. Requires Matplotlib and Seaborn libraries, and access to the model's attention layer weights.

```Python
import matplotlib.pyplot as plt
import seaborn as sns

def visualize_attention_weights(model, input_sequence, emotion_labels):
    # Extract attention weights from transformer model
    attention_layer = model.get_layer('attention') # Assumes the attention layer is named 'attention'
    attention_weights = attention_layer.get_weights()[0]

    # Visualize attention patterns
    plt.figure(figsize=(10, 8))
    sns.heatmap(
        attention_weights,
        xticklabels=emotion_labels,
        yticklabels=[f'Time_{i}' for i in range(len(input_sequence))],
        cmap='viridis'
    )
    plt.title('Attention Weights: Time Steps vs Emotions')
    plt.show()
```

--------------------------------

### Custom Emotion Loss Function

Source: https://github.com/agk4444/aditya369/blob/master/docs/ML_MODEL_ARCHITECTURE.md

Implements a custom weighted categorical cross-entropy loss function for emotion classification. It assigns different weights to each emotion class to handle class imbalance.

```Python
def custom_emotion_loss(y_true, y_pred):
    # Weighted cross-entropy for emotion classes
    weights = tf.constant([1.0, 1.2, 1.5, 1.0, 1.0, 1.3, 1.1])  # Different weights per emotion

    # Categorical cross-entropy with class weights
    cce = tf.keras.losses.CategoricalCrossentropy()
    loss = cce(y_true, y_pred)

    # Apply class weights
    weight_mask = tf.reduce_sum(y_true * weights, axis=-1)
    weighted_loss = loss * weight_mask

    return tf.reduce_mean(weighted_loss)
```