.

app.py

@@ -16,42 +16,39 @@ model_options = {
     "Foundation-Sec-8B": pipeline("text-generation", model="fdtn-ai/Foundation-Sec-8B"),
 }
 
-# Define the response function
-@spaces.GPU
+#@spaces.GPU
 def generate_text_local(model_pipeline, prompt):
     """Local text generation"""
     try:
-        logger.info(f"Running local text generation with {model_pipeline.path}")
-        
+        # モデル名取得（なければ 'unknown'）
+        model_name = getattr(getattr(model_pipeline, "model", None), "name_or_path", "unknown")
+        logger.info(f"Running local text generation with {model_name}")
+
         # Move model to GPU (entire pipeline)
         device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-        model_pipeline.model = model_pipeline.model.to(device)
-        
-        # Set other pipeline components to use GPU
-        if hasattr(model_pipeline, "device"):
-            model_pipeline.device = device
-        
+        if hasattr(model_pipeline, "model"):
+            model_pipeline.model = model_pipeline.model.to(device)
+
         # Record device information
-        device_info = next(model_pipeline.model.parameters()).device
-        logger.info(f"Model {model_pipeline.path} is running on device: {device_info}")
-        
+        device_info = next(model_pipeline.model.parameters()).device if hasattr(model_pipeline, "model") else "unknown"
+        logger.info(f"Model {model_name} is running on device: {device_info}")
+
         outputs = model_pipeline(
             prompt,
-            max_new_tokens=3,      # = model.generate(max_new_tokens=3, …)
+            max_new_tokens=3,
             do_sample=True,
             temperature=0.1,
             top_p=0.9,
-            clean_up_tokenization_spaces=True,  # echo 部分を整形
+            clean_up_tokenization_spaces=True,
         )
 
         # Move model back to CPU
-        model_pipeline.model = model_pipeline.model.to("cpu")
-        if hasattr(model_pipeline, "device"):
-            model_pipeline.device = torch.device("cpu")
-        
+        if hasattr(model_pipeline, "model"):
+            model_pipeline.model = model_pipeline.model.to("cpu")
+
         return outputs[0]["generated_text"].replace(prompt, "").strip()
     except Exception as e:
-        logger.error(f"Error in local text generation with {model_pipeline.path}: {str(e)}")
+        logger.error(f"Error in local text generation with {model_name}: {str(e)}")
         return f"Error: {str(e)}"
 
 # Build Gradio app