Update app.py

app.py

@@ -11,16 +11,13 @@ MODEL_ID = "google/medasr"
 TITLE = "🏥 MedASR - Medical Speech Recognition"
 DESCRIPTION = """
 Demo for **MedASR** (Medical Automated Speech Recognition).
-
 ⚠️ **Note**: This is a gated model. You need to provide your HuggingFace Token to access it.
-
 ### How to get a Token:
 1. Visit https://huggingface.co/google/medasr
 2. Click "Access this model" and agree to the terms
 3. Go to https://huggingface.co/settings/tokens
 4. Create a new token with "Read" permission
 5. Copy the token (starts with `hf_`)
-
 ### Instructions:
 1. Enter your HF Token below
 2. Click "🔑 Load Model"
@@ -32,22 +29,12 @@ Demo for **MedASR** (Medical Automated Speech Recognition).
 pipeline_instance = None
 
 def load_model_with_token(hf_token):
-    """使用用户提供的 token 加载模型"""
     global pipeline_instance
-    
-    if not hf_token:
-        return "❌ Please enter your HuggingFace Token first.", "❌ Model not loaded"
-    
-    if not hf_token.startswith("hf_"):
-        return "❌ Invalid token format. Token should start with 'hf_'", "❌ Model not loaded"
+    if not hf_token or not hf_token.startswith("hf_"):
+        return gr.update(interactive=False), "❌ Invalid Token"
     
     try:
-        print(f"Loading model with token: {hf_token[:10]}...")
-        
-        # 设备配置
         device = 0 if torch.cuda.is_available() else -1
-        
-        # 创建 pipeline
         pipeline_instance = pipeline(
             "automatic-speech-recognition",
             model=MODEL_ID,
@@ -55,14 +42,9 @@ def load_model_with_token(hf_token):
             device=device,
             torch_dtype=torch.float16 if device == 0 else torch.float32
         )
-        
-        device_name = "GPU (CUDA)" if device == 0 else "CPU"
-        return f"✅ Model loaded successfully on {device_name}!", "✅ Model Ready"
-    
+        return gr.update(interactive=True), "✅ Model loaded successfully!"
     except Exception as e:
-        error_msg = f"❌ Error loading model: {str(e)}"
-        print(error_msg)
-        return error_msg, "❌ Model Failed"
+        return gr.update(interactive=False), f"❌ Error: {str(e)}"
 
 def transcribe_audio(audio_file):
     """转录音频"""
@@ -172,7 +154,7 @@ with gr.Blocks(theme=gr.themes.Soft()) as demo:
     
     # 事件绑定
     load_model_btn.click(
-        fn=lambda token: update_model_status(load_model_with_token(token)),
+        fn=load_model_with_token,      # 直接调用，不要用 lambda 嵌套 update_model_status
         inputs=[hf_token],
         outputs=[transcribe_btn, model_status]
     )