generate_refine_paragraph

app.py

@@ -785,34 +785,44 @@ def update_paragraph_correct_grammatical_spelling_errors_input(paragraph):
 
 def generate_refine_paragraph(model, sys_content, eng_level, paragraph, user_refine_paragraph_prompt):
     """
-    根据用户输入的段落，调用OpenAI API生成相关的段落改善建議。
+    根据用户输入的段落，调用 LLM API 生成相关的段落改善建議。
     """
-    user_content = f"""
-        eng_level is: {eng_level}
-        paragraph is: {paragraph}
-        ---
-        {user_refine_paragraph_prompt}
-    """
-    messages = [
-        {"role": "system", "content": sys_content},
-        {"role": "user", "content": user_content}
-    ]
+    try:
+        user_content = f"""
+            eng_level is: {eng_level}
+            paragraph is: {paragraph}
+            ---
+            {user_refine_paragraph_prompt}
+        """
+        
+        messages = [
+            {"role": "system", "content": sys_content},
+            {"role": "user", "content": user_content}
+        ]
 
-    response_format = { "type": "json_object" }
+        # 根據模型選擇 provider
+        if "gemini" in model.lower():
+            print("====gemini====")
+            provider = GeminiProvider()
+        else:
+            print("====openai====")
+            provider = OpenAIProvider(OPEN_AI_CLIENT)
 
-    request_payload = {
-        "model": model,
-        "messages": messages,
-        "max_tokens": 4000,
-        "response_format": response_format
-    }
+        # 使用 LLMService 處理請求
+        llm_service = LLMService(provider)
+        content = llm_service.chat(
+            prompt=f"{sys_content}\n{user_content}" if "gemini" in model.lower() else None,
+            messages=messages,
+            model=model,
+            max_tokens=4000,
+            response_format={"type": "json_object"}
+        )
 
-    max_attempts = 2
-    attempt = 0
-    while attempt < max_attempts:
+        # 處理回應格式
+        if isinstance(content, str) and "```json" in content:
+            content = content.replace("```json", "").replace("```", "")
+            
         try:
-            response = OPEN_AI_CLIENT.chat.completions.create(**request_payload)
-            content = response.choices[0].message.content
             data = json.loads(content)
             headers = ["原文", "建議", "解釋"]
             table_data = [
@@ -821,15 +831,21 @@ def generate_refine_paragraph(model, sys_content, eng_level, paragraph, user_ref
             ]
 
             refine_paragraph_gr_update = gr.update(value=table_data, headers=headers, visible=True)
-            revised_paragraph_gr_update = gr.update(value=data["Revised Paragraph"],visible=False)
-            break
-        except Exception as e:
-            print(f"An error occurred while generating refine paragraph: {e}")
-            attempt += 1
-            if attempt == max_attempts:
-                raise gr.Error("網路塞車，請重新嘗試一次！")
+            revised_paragraph_gr_update = gr.update(value=data["Revised Paragraph"], visible=False)
+            return refine_paragraph_gr_update, revised_paragraph_gr_update
 
-    return refine_paragraph_gr_update, revised_paragraph_gr_update
+        except (json.JSONDecodeError, KeyError, ValueError) as e:
+            print(f"Error parsing refine suggestions: {e}")
+            raise gr.Error("無法解析改善建議，請重新嘗試")
+
+    except Exception as e:
+        print(f"An error occurred while generating refine paragraph: {str(e)}")
+        error_msg = "網路塞車，請重新嘗試一次！"
+        if "rate limit" in str(e).lower():
+            error_msg = "請求過於頻繁，請稍後再試"
+        elif "invalid_request_error" in str(e).lower():
+            error_msg = "請求格式錯誤，請檢查輸入"
+        raise gr.Error(error_msg)
 
 def update_paragraph_refine_input(text):
     return text