folder reorganise

app.py

@@ -152,7 +152,7 @@ def download_all_files():
 def get_training_status():
     """Get current training status from the monitor."""
     try:
-        from training_monitor import create_monitor
+        from ui.monitor import create_monitor
         monitor = create_monitor()
         status = monitor.get_status()
         return status if status else {"status": "no-training"}
@@ -1092,7 +1092,7 @@ def start_training_advanced(
                 dataset_args = ["--max_samples", dataset_size]
             
             resnet_cmd = [
-                "python", "train_resnet.py",
+                "python", "training/train_resnet.py",
                 "--data_root", DATASET_ROOT,
                 "--epochs", str(resnet_epochs),
                 "--batch_size", str(resnet_batch_size),
@@ -1137,7 +1137,7 @@ def start_training_advanced(
             log_message += f"Optimizer: {vit_optimizer}, Triplet Margin: {vit_triplet_margin}\n"
             
             vit_cmd = [
-                "python", "train_vit_triplet.py",
+                "python", "training/train_vit.py",
                 "--data_root", DATASET_ROOT,
                 "--epochs", str(vit_epochs),
                 "--batch_size", str(vit_batch_size),
@@ -1225,7 +1225,7 @@ def start_training_simple(dataset_size: str, res_epochs: int, vit_epochs: int):
             # Train ResNet first and wait for completion
             log_message += f"\n🚀 Starting ResNet training on {dataset_size} samples...\n"
             resnet_result = subprocess.run([
-                        "python", "train_resnet.py", "--data_root", DATASET_ROOT, "--epochs", str(res_epochs),
+                        "python", "training/train_resnet.py", "--data_root", DATASET_ROOT, "--epochs", str(res_epochs),
                 "--batch_size", "4", "--lr", "1e-3", "--early_stopping_patience", "3",
                         "--out", os.path.join(export_dir, "resnet_item_embedder.pth")
             ] + dataset_args, capture_output=True, text=True, check=False)
@@ -1252,7 +1252,7 @@ def start_training_simple(dataset_size: str, res_epochs: int, vit_epochs: int):
             
             log_message += f"\n🚀 Starting ViT training on {dataset_size} samples...\n"
             vit_result = subprocess.run([
-                        "python", "train_vit_triplet.py", "--data_root", DATASET_ROOT, "--epochs", str(vit_epochs),
+                        "python", "training/train_vit.py", "--data_root", DATASET_ROOT, "--epochs", str(vit_epochs),
                 "--batch_size", "4", "--lr", "5e-4", "--early_stopping_patience", "5",
                 "--max_samples", "5000", "--triplet_margin", "0.5", "--gradient_clip", "1.0",
                 "--warmup_epochs", "2", "--export", os.path.join(export_dir, "vit_outfit_model.pth")

integrate_advanced_training.py → training/integrate.py

@@ -5,7 +5,13 @@ Shows how to add comprehensive parameter controls to the main Gradio app
 """
 
 import gradio as gr
-from advanced_training_ui import create_advanced_training_interface, start_advanced_training, start_simple_training
+import sys
+import os
+
+# Add parent directory to path to find ui module
+sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+
+from ui.advanced_training import create_advanced_training_interface, start_advanced_training, start_simple_training
 
 
 def create_enhanced_app():

test_training.py → training/test_training.py

@@ -8,38 +8,18 @@ import os
 import sys
 import torch
 
+# Add parent directory to path to find models, data, utils
+sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+
+from data.polyvore import PolyvoreTripletDataset
+from models.resnet_embedder import ResNetItemEmbedder
+from models.vit_outfit import OutfitCompatibilityModel
+from utils.transforms import build_train_transforms
+
 def test_imports():
     """Test if all required modules can be imported."""
     print("🔍 Testing imports...")
-    
-    try:
-        from models.resnet_embedder import ResNetItemEmbedder
-        print("✅ ResNet embedder imported successfully")
-    except Exception as e:
-        print(f"❌ Failed to import ResNet embedder: {e}")
-        return False
-    
-    try:
-        from models.vit_outfit import OutfitCompatibilityModel
-        print("✅ ViT outfit model imported successfully")
-    except Exception as e:
-        print(f"❌ Failed to import ViT outfit model: {e}")
-        return False
-    
-    try:
-        from data.polyvore import PolyvoreTripletDataset
-        print("✅ Polyvore dataset imported successfully")
-    except Exception as e:
-        print(f"❌ Failed to import Polyvore dataset: {e}")
-        return False
-    
-    try:
-        from utils.transforms import build_train_transforms
-        print("✅ Transforms imported successfully")
-    except Exception as e:
-        print(f"❌ Failed to import transforms: {e}")
-        return False
-    
+    print("✅ All modules imported successfully at top level")
     return True
 
 def test_models():

train_resnet.py → training/train_resnet.py

@@ -9,7 +9,7 @@ import torch.optim as optim
 from torch.utils.data import DataLoader
 
 # Fix import paths
-sys.path.append(os.path.dirname(os.path.abspath(__file__)))
+sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
 
 from data.polyvore import PolyvoreTripletDataset
 from models.resnet_embedder import ResNetItemEmbedder
@@ -51,7 +51,7 @@ def main() -> None:
         os.makedirs(splits_dir, exist_ok=True)
         try:
             # Try to import and run the prepare script
-            sys.path.append(os.path.join(os.path.dirname(__file__), "scripts"))
+            sys.path.append(os.path.join(os.path.dirname(os.path.dirname(__file__)), "scripts"))
             from prepare_polyvore import main as prepare_main
             print("✅ Successfully imported prepare_polyvore")
             

train_vit_triplet.py → training/train_vit.py

@@ -9,7 +9,7 @@ import torch.optim as optim
 from torch.utils.data import DataLoader
 
 # Fix import paths
-sys.path.append(os.path.dirname(os.path.abspath(__file__)))
+sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
 
 from data.polyvore import PolyvoreOutfitTripletDataset
 from models.vit_outfit import OutfitCompatibilityModel
@@ -70,7 +70,7 @@ def main() -> None:
         os.makedirs(splits_dir, exist_ok=True)
         try:
             # Try to import and run the prepare script
-            sys.path.append(os.path.join(os.path.dirname(__file__), "scripts"))
+            sys.path.append(os.path.join(os.path.dirname(os.path.dirname(__file__)), "scripts"))
             from prepare_polyvore import main as prepare_main
             print("✅ Successfully imported prepare_polyvore")
             

advanced_training_ui.py → ui/advanced_training.py

@@ -259,7 +259,7 @@ def start_advanced_training(
             train_log.value += f"Optimizer: {resnet_optimizer}, Triplet Margin: {resnet_triplet_margin}\n"
             
             resnet_cmd = [
-                "python", "train_resnet.py",
+                "python", "training/train_resnet.py",
                 "--data_root", dataset_root,
                 "--epochs", str(resnet_epochs),
                 "--batch_size", str(resnet_batch_size),
@@ -288,7 +288,7 @@ def start_advanced_training(
             train_log.value += f"Optimizer: {vit_optimizer}, Triplet Margin: {vit_triplet_margin}\n"
             
             vit_cmd = [
-                "python", "train_vit_triplet.py",
+                "python", "training/train_vit.py",
                 "--data_root", dataset_root,
                 "--epochs", str(vit_epochs),
                 "--batch_size", str(vit_batch_size),
@@ -333,12 +333,12 @@ def start_simple_training(res_epochs: int, vit_epochs: int, dataset_root: str =
             os.makedirs(export_dir, exist_ok=True)
             train_log.value = "Training ResNet…\n"
             subprocess.run([
-                "python", "train_resnet.py", "--data_root", dataset_root, "--epochs", str(res_epochs),
+                "python", "training/train_resnet.py", "--data_root", dataset_root, "--epochs", str(res_epochs),
                 "--out", os.path.join(export_dir, "resnet_item_embedder.pth")
             ], check=False)
             train_log.value += "\nTraining ViT (triplet)…\n"
             subprocess.run([
-                "python", "train_vit_triplet.py", "--data_root", dataset_root, "--epochs", str(vit_epochs),
+                "python", "training/train_vit.py", "--data_root", dataset_root, "--epochs", str(vit_epochs),
                 "--export", os.path.join(export_dir, "vit_outfit_model.pth")
             ], check=False)
             train_log.value += "\nDone. Artifacts in models/exports."

artifact_management_ui.py → ui/artifact_management.py

@@ -6,8 +6,13 @@ Provides download, upload, and organization features for all system artifacts.
 
 import os
 import json
+import sys
 import gradio as gr
 from typing import Dict, List, Any
+
+# Add parent directory to path to find utils
+sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+
 from utils.artifact_manager import create_artifact_manager
 
 def create_artifact_management_interface():