import numpy as np
import torch
# Menggunakan ChronosPipeline untuk pemuatan dan inferensi yang efisien
from chronos import ChronosPipeline 

class ModelHandler:
    def __init__(self):
        # Mengganti model lama dengan Chronos-2 yang lebih canggih
        self.model_name = "amazon/chronos-2" 
        self.pipeline = None
        # Penentuan device: "cuda" jika ada GPU, jika tidak "cpu"
        self.device = "cuda" if torch.cuda.is_available() else "cpu"
        self.load_model()
    
    def load_model(self):
        """Load Chronos-2 model using the official ChronosPipeline"""
        try:
            print(f"Loading {self.model_name} on {self.device}...")
            
            # ChronosPipeline menangani semua proses tokenisasi dan pemuatan arsitektur dengan benar
            self.pipeline = ChronosPipeline.from_pretrained(
                self.model_name,
                device_map=self.device,
            )
            print("Chronos-2 pipeline loaded successfully.")
            
        except Exception as e:
            # Jika gagal, pipeline akan tetap None, dan fallback akan digunakan
            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. 'data' must be the dict from data_processor.prepare_for_chronos."""
        try:
            # Cek data: memastikan data yang masuk adalah dictionary yang valid
            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 (Menggunakan data['original']) ---
                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):
                    # Add trend with some noise
                    next_value = last_value + recent_trend * (i + 1)
                    # Use .get('std', 1.0) for safety
                    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,
                num_samples=20 
            )
            
            # Mengambil nilai rata-rata (mean) dari semua sampel untuk plot garis tunggal
            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)