Update app.py

app.py

@@ -22,19 +22,6 @@ whisper_pipe = pipeline(
     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
-
-try:
-    
-        # 這裡需要添加正確的內容以避免 IndentationError
-        
-        deepseek_pipe=pipeline("text-generation",model="t5-base")  # 暫時使用 T5 作為替代
-        
-except Exception as 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=None 
 
@@ -48,30 +35,37 @@ except Exception as e:
 
 def process_audio(audio_file):
     
-        
+        # 語音轉文字
             
-                result= whisper_pipe(audio_file)["text"]
+        result= whisper_pipe(audio_file)["text"]
                 
+        # 使用 T5 作為替代模型
+        
+        messages=[{"role": "user", "content": result}]
+        
+        deepseek_response="" 
+        
+        try: 
+            from transformers import pipeline
+            
+            pipe=pipeline("text-generation",model="t5-base")
+            
+            deepseek_response=pipe(messages)[0]["generated_text"]
                     
-                        messages=[{"role": "user", "content": result}]
-                        deepseek_response="" 
-                        
-                        try: 
-                            if deepseek_pipe is not None:  
-                                deepseek_response=deepseek_pipe(messages)[0]["generated_text"]
-                            
-                            doc=nlp(deepseek_response) if nlp is not None else None 
-                            entities=[(ent.text, ent.label_) for ent in doc.ents] if doc is not None else []
-                            
-                            return {
-                                "Transcription (Whisper)": result,
-                                "AI Response (T5)": deepseek_response,# 修改為 T5 回應以避免與原來不同步                            
-                                "Extracted Entities (spaCy)": entities}
+                # 使用 spaCy 分析文本
+                    
+            doc=nlp(deepseek_response) if nlp is not None else None 
+            entities=[(ent.text, ent.label_) for ent in doc.ents] if doc is not None else []
+                    
+            return {
+                "Transcription (Whisper)": result,
+                "AI Response (T5)": deepseek_response,# 修改為 T5 回應以避免與原來不同步                            
+                "Extracted Entities (spaCy)": entities}
                         
-                        except Exception as e: 
-                            return {
-                                "Transcription (Whisper)": result,# 保留原始轉錄內容                                
-                                    }
+        except Exception as e: 
+                    return {
+                        "Transcription (Whister)": result,# 保留原始轉錄內容                                
+                            }