"""
Preprocessing utilities for Panic Detection model
"""
import numpy as np
from sklearn.preprocessing import LabelEncoder, MinMaxScaler


class PanicDetectionPreprocessor:
    def __init__(self):
        """Initialize encoders and scaler with the same configuration as training"""
        self.gender_encoder = LabelEncoder()
        self.activity_encoder = LabelEncoder()
        self.scaler = MinMaxScaler()
        
        # Fit encoders with expected values (based on your training data)
        # Adjust these if your actual categories are different
        self.gender_encoder.fit(['Male', 'Female'])
        self.activity_encoder.fit(['Running', 'Walking', 'Sitting', 'Standing', 'Cycling'])
        
        # Initialize scaler with approximate ranges from your data
        # In production, you should save and load the actual fitted scaler
        self.scaler.fit(np.array([
            [0, 18, 45, 60, 0, 0],      # min values (approximate)
            [1, 80, 120, 180, 15000, 4]  # max values (approximate)
        ]))
    
    def preprocess(self, gender: str, age: int, weight: float, 
                   heartrate: int, stepcount: int, activity: str):
        """
        Preprocess input data for model prediction
        
        Args:
            gender: Gender as string ('Male' or 'Female')
            age: Age in years
            weight: Weight in kg
            heartrate: Heart rate in bpm
            stepcount: Step count
            activity: Activity type as string
        
        Returns:
            Preprocessed numpy array ready for model input
        """
        try:
            # Encode categorical features
            gender_encoded = self.gender_encoder.transform([gender])[0]
            activity_encoded = self.activity_encoder.transform([activity])[0]
            
            # Create feature array
            features = np.array([[
                gender_encoded,
                age,
                weight,
                heartrate,
                stepcount,
                activity_encoded
            ]])
            
            # Scale features
            features_scaled = self.scaler.transform(features)
            
            # Reshape for CNN input (samples, timesteps, features)
            features_reshaped = features_scaled.reshape(1, 6, 1)
            
            return features_reshaped
            
        except Exception as e:
            raise ValueError(f"Preprocessing error: {str(e)}")
    
    def get_valid_genders(self):
        """Return list of valid gender values"""
        return list(self.gender_encoder.classes_)
    
    def get_valid_activities(self):
        """Return list of valid activity values"""
        return list(self.activity_encoder.classes_)