Update app.py

app.py

@@ -10,29 +10,19 @@ torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32
 
 # Whisper 模型初始化（語音轉文字）
 whisper_model_id = "openai/whisper-large-v3"
+whisper_model = AutoModelForSpeechSeq2Seq.from_pretrained(
+    whisper_model_id, torch_dtype=torch_dtype, low_cpu_mem_usage=True, use_safetensors=True
+)
+whisper_model.to(device)
+whisper_processor = AutoProcessor.from_pretrained(whisper_model_id)
 
-try:
-    whisper_model = AutoModelForSpeechSeq2Seq.from_pretrained(
-        whisper_model_id, torch_dtype=torch_dtype, low_cpu_mem_usage=True, use_safetensors=True,
-        revision="main",  # 嘗試指定修訂版以解決兼容性問題
-    )
-    whisper_model.to(device)
-    
-    # 加載處理器時也指定修訂版（如果需要）
-    whisper_processor = AutoProcessor.from_pretrained(whisper_model_id)
-except Exception as e:
-    print(f"加載Whisper模型或處理器時出現錯誤：{e}")
-else:
-    # 成功加載後建立pipeline進行語音轉文字工作
-    
-    whisper_pipe = pipeline(
-        "automatic-speech-recognition",
-        model=whisper_model,
-        tokenizer=whisper_processor.tokenizer,
-        feature_extractor=whisper_processor.feature_extractor,
-        device=device,
-        
-    )
+whisper_pipe = pipeline(
+    "automatic-speech-recognition",
+    model=whisper_model,
+    tokenizer=whisper_processor.tokenizer,
+    feature_extractor=whisper_processor.feature_extractor,
+    device=device,
+)
 
 # DeepSeek-V3 模型初始化（文本生成）
 deepseek_pipe = None  # 預設值，以防模型加載失敗
@@ -40,44 +30,55 @@ deepseek_pipe = None  # 預設值，以防模型加載失敗
 try:
     deepseek_pipe = pipeline("text-generation", model="deepseek-ai/DeepSeek-V3", trust_remote_code=True)
 except Exception as e:
-    print(f"加載DeepSeek-V3模型時出現錯誤：{e}")
+    print(f"加載模型時出現錯誤：{e}")
 
 # spaCy 初始化（文本分類與標籤）
 nlp = spacy.load("en_core_web_sm")
 
 def process_audio(audio_file):
     
-    try:
-        
-        result = whisper_pipe(audio_file)["text"]
-        
+    result = whisper_pipe(audio_file)["text"]
+    
             
         
             
             # 使用 DeepSeek 生成回應（如果成功加載模型）
-            if deepseek_pipe is not None:
-                messages=[{"role": "user", "content": result}]
-                deepseek_response=deepseek_pipe(messages)[0]["generated_text"]
-                
-                doc=nlp(deepseek_response)
-                entities=[(ent.text, ent.label_) for ent in doc.ents]
-                
-                return {
-                    "Transcription (Whisper)": result,
-                    "AI Response (DeepSeek)": deepseek_response,
-                    "Extracted Entities (spaCy)": entities
-                    
-                    
-                    
-                    }
+            
+        
                 
             
+    
                 
             
         
-        
     
     
+        # 修正後：將這個判斷放在函數內部，並且去掉多餘的縮進
+        
+        if deepseek_pipe is not None:
+            messages=[{"role": "user", "content": result}]
+            deepseek_response=deepseek_pipe(messages)[0]["generated_text"]
+                
+            doc=nlp(deepseek_response)
+            entities=[(ent.text, ent.label_) for ent in doc.ents]
+                
+            return {
+                "Transcription (Whisper)": result,
+                "AI Response (DeepSeek)": deepseek_response,
+                "Extracted Entities (spaCy)": entities
+                    
+                    
+                    
+                    }
+                    
+        else:
+            return {
+                "Transcription (Whisper)": result,
+                # 可以添加預設回應或錯誤信息
+                
+                    }
+                    
+
 
 with gr.Blocks() as app:
 
@@ -93,7 +94,6 @@ with gr.Blocks() as app:
        
        
        
-