#!/usr/bin/env python
# coding: utf-8
import os
import json
import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader
from tqdm import tqdm
from core.ndvi_data_loader import NDVITimeSeriesDataset

print("=" * 70)
print("🚀 Training LSTM/GRU Time Series model for NDVI (REAL DATA & GPU)")
print("=" * 70)

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
print(f"[SYSTEM] Device: {device.type.upper()}")

dataset = NDVITimeSeriesDataset(sequence_length=3, spatial=False)
dataloader = DataLoader(dataset, batch_size=4, shuffle=True)

class LSTMModel(nn.Module):
    def __init__(self, input_size=1, hidden_size=32, num_layers=2):
        super().__init__()
        self.lstm = nn.LSTM(input_size, hidden_size, num_layers, batch_first=True)
        self.fc = nn.Linear(hidden_size, 1)
        
    def forward(self, x):
        out, _ = self.lstm(x)
        out = self.fc(out[:, -1, :]) # Take last time step
        return out

model = LSTMModel().to(device)
criterion = nn.MSELoss()
optimizer = optim.Adam(model.parameters(), lr=1e-3)

print("\n[TRAIN] Bắt đầu Training...")
model.train()
total_loss = 0
for epoch in range(50):
    for inputs, targets in dataloader:
        inputs, targets = inputs.to(device), targets.to(device)
        optimizer.zero_grad()
        outputs = model(inputs)
        loss = criterion(outputs, targets)
        loss.backward()
        optimizer.step()
        total_loss += loss.item()

avg_loss = total_loss / (len(dataloader) * 50)
print(f"✅ Training completed! Avg MSE Loss (RMSE): {avg_loss**0.5:.4f}")

model_dir = "ndvi_forecast_model"
model_path = os.path.join(model_dir, "ndvi_lstm_real.pth")
torch.save(model.state_dict(), model_path)
print(f"\n[SAVE] Model saved to {model_path}")

with open(os.path.join(model_dir, "ndvi_lstm_real_info.json"), "w") as f:
    json.dump({
        "model_type": "LSTM Time Series (Real Data & GPU)",
        "target": "NDVI", "epoch": 50,
        "rmse": float(avg_loss**0.5), "mae": float(avg_loss)
    }, f, indent=2)
print("[SAVE] Model info saved.")