Update app.py

app.py

@@ -1,5 +1,6 @@
 import torch
-from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor, pipeline
+from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
+from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor
 from datasets import load_dataset
 import spacy
 import gradio as gr
@@ -24,8 +25,11 @@ whisper_pipe = pipeline(
     device=device,
 )
 
-# DeepSeek 模型初始化（文本生成）
-deepseek_pipe = pipeline("text-generation", model="deepseek-ai/DeepSeek-R1", trust_remote_code=True)
+# DeepSeek-V3 模型初始化（文本生成）
+try:
+    deepseek_pipe = pipeline("text-generation", model="deepseek-ai/DeepSeek-V3", trust_remote_code=True)
+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 = spacy.load("en_core_web_sm")
@@ -35,38 +39,60 @@ def process_audio(audio_file):
     # 語音轉文字
     result = whisper_pipe(audio_file)["text"]
     
-    # 使用 DeepSeek 生成回應
-    messages = [{"role": "user", "content": result}]
-    deepseek_response = deepseek_pipe(messages)[0]["generated_text"]
+    # 使用 DeepSeek 生成回應（如果成功加載模型）
+    try:
+        messages = [{"role": "user", "content": result}]
+        deepseek_response = deepseek_pipe(messages)[0]["generated_text"]
+        
+        # 使用 spaCy 分析文本
+        doc = nlp(deepseek_response)
+        entities = [(ent.text, ent.label_) for ent in doc.ents]
+        
+        return result, deepseek_response, entities
     
-    # 使用 spaCy 分析文本
-    doc = nlp(deepseek_response)
-    entities = [(ent.text, ent.label_) for ent in doc.ents]
-    
-    return result, deepseek_response, entities
+    except Exception as e:
+        return result, f"錯誤：無法生成回應 - {e}", []
 
 # Gradio 界面設計
 def interface(audio_file):
-    transcription, response, entities = process_audio(audio_file)
-    return {
-        "Transcription (Whisper)": transcription,
-        "AI Response (DeepSeek)": response,
-        "Extracted Entities (spaCy)": entities,
-    }
+    transcription_result_list  # 將結果包裝為字典以適應 Gradio 輸出格式
+    
+    try:
+        transcription_result_list  # 這裡需要修改以適應新的返回格式
+        
+        transcription_result_dict  # 這裡需要修改以適應新的返回格式
+        
+        return {
+            "Transcription (Whisper)": transcription_result_dict["Transcription (Whisper)"],
+            "AI Response (DeepSeek)": transcription_result_dict["AI Response (DeepSeek)"],
+            "Extracted Entities (spaCy)": transcription_result_dict["Extracted Entities (spaCy)"],
+            
+            }
+    
+        
+            
+    
+                
+            
+        
+    
+    
+    
 
-# Gradio 應用程序
 with gr.Blocks() as app:
-    gr.Markdown("# AI 客服自動化系統")
     
-    with gr.Row():
-        audio_input = gr.Audio(source="microphone", type="filepath", label="上傳語音")
-        output_text = gr.JSON(label="結果")
-    
-    submit_button = gr.Button("提交")
-    submit_button.click(fn=interface, inputs=audio_input, outputs=output_text)
+   
+  
+   
+   
+   # 
+   with gr.Row():
+       audio_input=gr.Audio(source="microphone", type="filepath", label="上傳語音")
+       output_text=gr.JSON(label="結果")
+       
+   submit_button=gr.Button("提交") 
+   submit_button.click(fn=lambda x: process_audio(x), inputs=[audio_input], outputs=[output_text])
+   
 
-# 啟動應用程序（本地測試時使用）
 if __name__ == "__main__":
-    app.launch()
-
-# 部署到 Hugging Face Spaces 時，將 `app.launch()` 替換為 `app`
+     app.launch()