refactor: reorganize project structure by moving core modules and update import paths in API server

.temp/log_swin.txt

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+🚀 BẮT ĐẦU HUẤN LUYỆN MÔ HÌNH SWIN-UNET (GPU & CACHE)
+Initializing FeatureExtractor (mode=extended)...
+📦 Đang load cache: training_data_507bd2ba4ec0d3fe107839cbf73a7a7d.joblib...
+✅ Loaded 632 samples từ cache!
+   ⚡ Đã bỏ qua download위성 data (tiết kiệm thời gian)
+[CACHE HIT] Using cached dataset with 632 samples
+Training SWIN-UNET model...
+Building Swin-UNet model on cuda...
+[SWIN-UNET] Class distribution: [ 48  89   3  86  74  38 117  50]
+[SWIN-UNET] Class weights: [0.37418982 0.20181024 5.98703525 0.20885013 0.24271772 0.47266082
+ 0.15351377 0.35922223]
+Training Swin-UNet model with PyTorch (with class weights)...
+Swin-UNet Epoch 5/40, Train Loss: 1.4096, Val Loss: 1.2804, Val Acc: 48.03%, LR: 0.000293
+[SWIN-UNET] Epoch 5/40 - Train Loss: 1.4096, Val Loss: 1.2804, Val Acc: 48.03%
+Swin-UNet Epoch 10/40, Train Loss: 1.1129, Val Loss: 1.2746, Val Acc: 57.48%, LR: 0.000271
+[SWIN-UNET] Epoch 10/40 - Train Loss: 1.1129, Val Loss: 1.2746, Val Acc: 57.48%
+Swin-UNet Epoch 15/40, Train Loss: 1.0479, Val Loss: 1.3126, Val Acc: 50.39%, LR: 0.000238
+[SWIN-UNET] Epoch 15/40 - Train Loss: 1.0479, Val Loss: 1.3126, Val Acc: 50.39%
+Swin-UNet Epoch 20/40, Train Loss: 0.9548, Val Loss: 1.1118, Val Acc: 52.76%, LR: 0.000196
+[SWIN-UNET] Epoch 20/40 - Train Loss: 0.9548, Val Loss: 1.1118, Val Acc: 52.76%
+[SWIN-UNET] Early stopping at epoch 22 (best val loss: 1.1096)
+Swin-UNet early stopped at epoch 22
+Evaluating model...
+Generating classification report...
+Saving model...
+[MODEL MANAGER] Saving model to: model_train/model_swin-unet_auto.joblib
+[MODEL MANAGER] Saving metadata to: model_train/model_swin-unet_auto_info.json
+[MODEL MANAGER] Model saved successfully!
+Training complete!
+✅ Hoàn thành! Accuracy: 0.5354