raise a gr.Error if model is missing

app.py

@@ -19,13 +19,21 @@ pipe = DiffusionPipeline.from_pretrained(
 device="cuda" if torch.cuda.is_available() else "cpu"
 
 def load_model(custom_model, weight_name):
+    
+    if custom_model == "":
+        gr.Warning("If you want to use a private model, you need to duplicate this space on your personal account.")
+        raise gr.Error("You forgot to define Model ID.")
+    
     # This is where you load your trained weights
     pipe.load_lora_weights(custom_model, weight_name=weight_name, use_auth_token=True)
     pipe.to(device)
+    
     return "Model loaded!"
 
 def infer (prompt, inf_steps, guidance_scale, seed, lora_weight, progress=gr.Progress(track_tqdm=True)):
+    
     generator = torch.Generator(device="cuda").manual_seed(seed)
+    
     image = pipe(
         prompt=prompt, 
         num_inference_steps=inf_steps,
@@ -33,6 +41,7 @@ def infer (prompt, inf_steps, guidance_scale, seed, lora_weight, progress=gr.Pro
         generator=generator,
         cross_attention_kwargs={"scale": lora_weight}
     ).images[0]
+    
     return image
 
 css="""