import numpy as np
import torch
from chronos import BaseChronosPipeline 

class ModelHandler:
    def __init__(self):
        self.model_name = "amazon/chronos-2" 
        self.pipeline = None
        self.device = "cuda" if torch.cuda.is_available() else "cpu"
        self.load_model()
    
    def load_model(self):
        """Load Chronos-2 model using the official BaseChronosPipeline"""
        try:
            print(f"Loading {self.model_name} on {self.device}...")
            
            self.pipeline = BaseChronosPipeline.from_pretrained(
                self.model_name,
                device_map=self.device,
            )
            print("Chronos-2 pipeline loaded successfully.")
            
        except Exception as e:
            print(f"Error loading Chronos-2 model: {e}")
            print("Using fallback prediction method")
            self.pipeline = None
    
    def predict(self, data, horizon=10):
        """Generate predictions using Chronos-2 or fallback."""
        try:
            if data is None or not isinstance(data, dict) or 'original' not in data or len(data['original']) < 20:
                return np.array([0] * horizon)
            
            if self.pipeline is None:
                # Fallback Logic
                values = data['original']
                recent_trend = np.polyfit(range(len(values[-20:])), values[-20:], 1)[0]
                
                predictions = []
                last_value = values[-1]
                
                for i in range(horizon):
                    next_value = last_value + recent_trend * (i + 1)
                    noise = np.random.normal(0, data.get('std', 1.0) * 0.1)
                    predictions.append(next_value + noise)
                
                return np.array(predictions)
            
            # Chronos-2 Inference
            predictions_samples = self.pipeline.predict(
                data['original'],
                prediction_length=horizon,
                n_samples=20 
            )
            
            # Mengambil nilai rata-rata (mean) dari semua sampel
            mean_predictions = np.mean(predictions_samples, axis=0)
            
            return mean_predictions
            
        except Exception as e:
            print(f"Prediction error: {e}")
            return np.array([0] * horizon)