This is the last update

inference.py

@@ -27,7 +27,7 @@ class InferenceService:
         self.embed_dim = int(os.getenv("EMBED_DIM", "512"))
         self.resnet_version = "resnet_v1"
         self.vit_version = "vit_v1"
-        
+
         # Model loading status tracking
         self.models_loaded = False
         self.model_errors = []
@@ -45,9 +45,9 @@ 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
         if not self.models_loaded:
@@ -177,8 +177,8 @@ 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
@@ -191,18 +191,37 @@ class InferenceService:
 
     @torch.inference_mode()
     def embed_images(self, images: List[Image.Image]) -> List[np.ndarray]:
+        print(f"🔍 DEBUG: embed_images called with {len(images)} images")
         if len(images) == 0:
+            print("🔍 DEBUG: No images provided, returning empty list")
+            return []
+        
+        print(f"🔍 DEBUG: ResNet model is None: {self.resnet is None}")
+        if self.resnet is None:
+            print("🔍 DEBUG: ResNet model is None, returning empty list")
+            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)
+            result = [e.detach().cpu().numpy().astype(np.float32) for e in emb]
+            print(f"🔍 DEBUG: Successfully generated {len(result)} embeddings")
+            return result
+        except Exception as e:
+            print(f"🔍 DEBUG: Error in embed_images: {e}")
             return []
-        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)
-        return [e.detach().cpu().numpy().astype(np.float32) for e in emb]
 
     @torch.inference_mode()
     def compose_outfits(self, items: List[Dict[str, Any]], context: Dict[str, Any]) -> List[Dict[str, Any]]:
+        # Debug: Print model status
+        print(f"🔍 DEBUG: models_loaded={self.models_loaded}, resnet_loaded={self.resnet_loaded}, vit_loaded={self.vit_loaded}")
+        print(f"🔍 DEBUG: model_errors={self.model_errors}")
+        print(f"🔍 DEBUG: items count={len(items)}")
+        
         # Validate that models are properly loaded
         if not self.models_loaded:
             error_msg = f"❌ Cannot provide recommendations: Models not properly loaded. Errors: {self.model_errors}"
@@ -215,13 +234,16 @@ class InferenceService:
         
         # 1) Ensure embeddings for each input item
         proc_items: List[Dict[str, Any]] = []
-        for it in items:
+        for i, it in enumerate(items):
+            print(f"🔍 DEBUG: Processing item {i}: id={it.get('id')}, has_image={it.get('image') is not None}, has_embedding={it.get('embedding') is not None}")
             emb = it.get("embedding")
             if emb is None and it.get("image") is not None:
                 # Compute on-the-fly if image provided
+                print(f"🔍 DEBUG: Generating embedding for item {i}")
                 emb = self.embed_images([it["image"]])[0]
             if emb is None:
                 # Skip if we cannot get an embedding
+                print(f"🔍 DEBUG: Skipping item {i} - no embedding generated")
                 continue
             emb_np = np.asarray(emb, dtype=np.float32)
             proc_items.append({
@@ -229,8 +251,11 @@ class InferenceService:
                 "embedding": emb_np,
                 "category": it.get("category")
             })
+            print(f"🔍 DEBUG: Added item {i} to proc_items, total: {len(proc_items)}")
 
+        print(f"🔍 DEBUG: Final proc_items count: {len(proc_items)}")
         if len(proc_items) < 2:
+            print("🔍 DEBUG: Returning empty array - not enough items (< 2)")
             return []
 
         # 2) Candidate generation with outfit templates
@@ -238,7 +263,7 @@ class InferenceService:
         num_outfits = int(context.get("num_outfits", 3))
         min_size, max_size = 4, 6
         ids = list(range(len(proc_items)))
-        
+
         # Outfit templates for cohesive styling
         outfit_templates = {
             "casual": {
@@ -274,7 +299,7 @@ class InferenceService:
         # Enhanced category-aware pools with diversity checks
         def cat_str(i: int) -> str:
             return (proc_items[i].get("category") or "").lower()
-        
+
         def extract_color_from_category(category: str) -> str:
             """Extract color information from category name"""
             category_lower = category.lower()
@@ -442,7 +467,7 @@ class InferenceService:
             # Remove duplicates and validate
             subset = list(set(subset))
             if len(subset) >= 3:  # At least 3 items for a valid outfit
-                candidates.append(subset)
+            candidates.append(subset)
 
         # 3) Score using ViT
         def score_subset(idx_subset: List[int]) -> float: