fixes

API_DOCUMENTATION.md

@@ -409,3 +409,4 @@ fetch(`${BASE_URL}/compose`, {
 
 For API support, please contact: support@dressify.com
 
+

app.py

@@ -870,9 +870,9 @@ 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", "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")
+                "--out", os.path.join(export_dir, "resnet_item_embedder.pth")
             ] + dataset_args, capture_output=True, text=True, check=False)
             
             if resnet_result.returncode == 0:
@@ -897,7 +897,7 @@ 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", "train_vit_triplet.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")
@@ -909,7 +909,7 @@ def start_training_simple(dataset_size: str, res_epochs: int, vit_epochs: int):
             else:
                 log_message += f"❌ ViT training failed: {vit_result.stderr}\n"
                 return log_message
-                    service.reload_models()
+            service.reload_models()
             
             # Check if models loaded successfully
             model_status = service.get_model_status()

inference.py

@@ -58,8 +58,8 @@ class InferenceService:
         # Disable gradients
         for m in [self.resnet, self.vit]:
             if m is not None:
-            for p in m.parameters():
-                p.requires_grad_(False)
+                for p in m.parameters():
+                    p.requires_grad_(False)
 
         # Update overall status
         self.models_loaded = self.resnet_loaded and self.vit_loaded
@@ -298,8 +298,8 @@ class InferenceService:
         # Disable gradients
         for m in [self.resnet, self.vit]:
             if m is not None:
-            for p in m.parameters():
-                p.requires_grad_(False)
+                for p in m.parameters():
+                    p.requires_grad_(False)
         
         # Update overall status
         self.models_loaded = self.resnet_loaded and self.vit_loaded
@@ -323,12 +323,12 @@ class InferenceService:
             return []
             
         try:
-        batch = torch.stack([self.transform(img) for img in images])
-        batch = batch.to(self.device, memory_format=torch.channels_last)
-        use_amp = (self.device == "cuda")
-        with torch.autocast(device_type=("cuda" if use_amp else "cpu"), enabled=use_amp):
-            emb = self.resnet(batch)
-        emb = nn.functional.normalize(emb, dim=-1)
+            batch = torch.stack([self.transform(img) for img in images])
+            batch = batch.to(self.device, memory_format=torch.channels_last)
+            use_amp = (self.device == "cuda")
+            with torch.autocast(device_type=("cuda" if use_amp else "cpu"), enabled=use_amp):
+                emb = self.resnet(batch)
+            emb = nn.functional.normalize(emb, dim=-1)
             result = [e.detach().cpu().numpy().astype(np.float32) for e in emb]
             print(f"🔍 DEBUG: Successfully generated {len(result)} embeddings")
             return result

utils/tag_system.py

@@ -516,3 +516,4 @@ def validate_tags(tags: Dict[str, Any]) -> tuple[bool, List[str]]:
     
     return len(errors) == 0, errors
 
+