import os
import glob
import json
from tabulate import tabulate

print("📊 BẢNG SO SÁNH KẾT QUẢ CÁC MÔ HÌNH\n")

# 1. Phân loại đất
print("### 1. Nhóm Phân loại Lớp phủ (Land Classification)")
land_data = []

if os.path.exists("model_xgboost_info.json"):
    with open("model_xgboost_info.json", 'r') as f:
        data = json.load(f)
        params = data.get('params', {})
        param_str = f"estimators:{params.get('n_estimators')}, depth:{params.get('max_depth')}" if params else "N/A"
        land_data.append([
            data.get('model_type', 'XGBoost'),
            data.get('accuracy', ''),
            data.get('precision', ''),
            data.get('recall', ''),
            data.get('f1_score', ''),
            param_str
        ])

for info_file in glob.glob("model_train/*_info.json"):
    with open(info_file, 'r') as f:
        data = json.load(f)
        # Support both 'accuracy' and 'test_accuracy'
        acc = data.get('accuracy', data.get('test_accuracy', ''))
        
        f1 = data.get('f1_score', '')
        precision = data.get('precision', '')
        recall = data.get('recall', '')
        
        clf_rep = data.get('classification_report')
        if isinstance(clf_rep, dict) and 'macro avg' in clf_rep:
            if not f1:
                f1 = clf_rep['macro avg'].get('f1-score', '')
            if not precision:
                precision = clf_rep['macro avg'].get('precision', '')
            if not recall:
                recall = clf_rep['macro avg'].get('recall', '')
            
        if not acc and not f1:
            continue
            
        params = data.get('params', {})
        param_str = f"estimators:{params.get('n_estimators')}, depth:{params.get('max_depth')}" if params else "N/A"
        
        if data.get('model_type') == 'RandomForest_RealData':
            param_str = "estimators:100, depth:15"
            
        land_data.append([
            data.get('model_type', ''),
            acc,
            precision,
            recall,
            f1,
            param_str
        ])

if land_data:
    print(tabulate(land_data, headers=["Model", "Accuracy", "Precision", "Recall", "F1-Score", "Parameters"], tablefmt="github"))
print("\n")

# 2. Xóa mây
print("### 2. Nhóm Xóa mây (Cloud Removal)")
cloud_data = []
for info_file in glob.glob("cloud_removal_model/*_info.json"):
    with open(info_file, 'r') as f:
        data = json.load(f)
        cloud_data.append([
            data.get('model_type', ''),
            data.get('epoch', ''),
            data.get('train_loss', ''),
            data.get('val_loss', '')
        ])
if cloud_data:
    print(tabulate(cloud_data, headers=["Model", "Epochs", "Train Loss", "Val Loss"], tablefmt="github"))
print("\n")

# 3. Dự báo NDVI
print("### 3. Nhóm Dự báo Thực vật (NDVI Forecasting)")
ndvi_data = []
for info_file in glob.glob("ndvi_forecast_model/*_info.json"):
    with open(info_file, 'r') as f:
        data = json.load(f)
        ndvi_data.append([
            data.get('model_type', ''),
            data.get('rmse', ''),
            data.get('mae', ''),
            data.get('epoch', 'N/A')
        ])
if ndvi_data:
    print(tabulate(ndvi_data, headers=["Model", "RMSE", "MAE", "Epochs"], tablefmt="github"))
print("\n")