fix: prioritize Hub model loading for Spaces deployment

test_constrained_model.py

@@ -35,20 +35,20 @@ def load_trained_model():
             low_cpu_mem_usage=True  # Reduce memory usage during loading
         )
         
-        # Try to load fine-tuned adapter - local first, then Hub
+        # Try to load fine-tuned adapter - Hub first for Spaces deployment
         try:
-            print("🔄 Attempting to load fine-tuned adapter locally...")
+            print("🔄 Loading fine-tuned adapter from Hugging Face Hub...")
             from peft import PeftModel
-            model = PeftModel.from_pretrained(model, "./smollm3_robust")
+            model = PeftModel.from_pretrained(model, "jlov7/SmolLM3-Function-Calling-LoRA")
             model = model.merge_and_unload()
-            print("✅ Fine-tuned model loaded successfully from local files!")
+            print("✅ Fine-tuned model loaded successfully from Hub!")
         except Exception as e:
             try:
-                print(f"⚠️ Local adapter failed: {e}")
-                print("🔄 Trying Hugging Face Hub...")
-                model = PeftModel.from_pretrained(model, "jlov7/SmolLM3-Function-Calling-LoRA")
+                print(f"⚠️ Hub adapter failed: {e}")
+                print("🔄 Trying local backup...")
+                model = PeftModel.from_pretrained(model, "./smollm3_robust")
                 model = model.merge_and_unload()
-                print("✅ Fine-tuned model loaded successfully from Hub!")
+                print("✅ Fine-tuned model loaded successfully from local files!")
             except Exception as e2:
                 print(f"⚠️ Could not load fine-tuned adapter: {e2}")
                 print("🔧 Using base model with optimized prompting")