try operation

app.py

@@ -14,6 +14,7 @@ import tempfile
 import urllib.parse
 import pandas as pd
 import re
+import time
 
 # From other files
 from storage_service import GoogleCloudStorage
@@ -1241,84 +1242,101 @@ def get_chinese_conversation_thread_id(thread_id):
         thread_id = thread.id
         return thread_id
 
-def get_chinese_paragraph_evaluate_content(thread_id, model, user_content, paragraph):
-    # 先做 主題與內容、遣詞造句
-    content = generate_content_by_open_ai_assistant(user_content, thread_id, model_name=model)
-    print(f"====generate_paragraph_evaluate====")
-    print(content)
-
-    if "```json" not in content:
-        raise gr.Error("網路塞車，或是內容有誤，請稍後重新嘗試！")
-
-    content_list = content.split("```json")
-    content_text = content_list[0]
-    print(f"content_text: {content_text}")
-    content_json = content_list[1].split("```")[0]
-    print(f"content_json: {content_json}")
+def retry_operation(operation, max_attempts=3, delay=2):
+    for attempt in range(max_attempts):
+        try:
+            return operation()
+        except Exception as e:
+            if attempt == max_attempts - 1:
+                raise e
+            time.sleep(delay)
 
-    data = json.loads(content_json)["results"]
+def get_chinese_paragraph_evaluate_content(thread_id, model, user_content, paragraph):
+    content_text = ""
+    table_data = []
     headers = ["架構", "評分", "解釋"]
-    table_data = [
-        ["主題與內容", data['主題與內容']['level'], data['主題與內容']['explanation']],
-        ["遣詞造句", data['遣詞造句']['level'], data['遣詞造句']['explanation']],
-    ]
-
-    # 段落結構
-    paragraph_structure_content = generate_content_by_open_ai_assistant_structure_robot(paragraph, thread_id=None, model_name=model)
-    print(f"paragraph_structure_content: {paragraph_structure_content}")
-
-    if "```json" not in paragraph_structure_content:
-        raise gr.Error("網路塞車，或是內容有誤，請稍後重新嘗試！")
-    
-    paragraph_structure_content_list = paragraph_structure_content.split("```json")
-    paragraph_structure_content_text = paragraph_structure_content_list[0]
-    paragraph_structure_content_json = paragraph_structure_content_list[1].split("```")[0]
-    paragraph_structure_content_table = json.loads(paragraph_structure_content_json)["results"]["段落結構"]
-    paragraph_structure_table_data = [
-        ["段落結構", paragraph_structure_content_table['level'], paragraph_structure_content_table['explanation']],
-    ]
+    error_messages = []
 
-    # 合併 table_data
-    table_data.extend(paragraph_structure_table_data)
-    content_text = content_text + "\n" + paragraph_structure_content_text
-    
-    # 挑錯字
-    spelling_content = generate_content_by_open_ai_assistant_spelling_robot(paragraph, thread_id=None, model_name=model)
-    print(f"spelling_content: {spelling_content}")
+    # 主題與內容、遣詞造句
+    try:
+        def content_operation():
+            content = generate_content_by_open_ai_assistant(user_content, thread_id, model_name=model)
+            if "```json" not in content:
+                raise ValueError("回應格式不正確")
+            return content
+        
+        content = retry_operation(content_operation)
+        content_list = content.split("```json")
+        content_text = content_list[0]
+        content_json = content_list[1].split("```")[0]
+        data = json.loads(content_json)["results"]
+        table_data.extend([
+            ["主題與內容", data['主題與內容']['level'], data['主題與內容']['explanation']],
+            ["遣詞造句", data['遣詞造句']['level'], data['遣詞造句']['explanation']],
+        ])
+    except Exception as e:
+        error_messages.append(f"主題與內容、遣詞造句評估出錯: {str(e)}")
 
-    if "```json" not in spelling_content:
-        raise gr.Error("網路塞車，或是內容有誤，請稍後重新嘗試！")
-    
-    spelling_content_list = spelling_content.split("```json")
-    spelling_content_text = spelling_content_list[0]
-    spelling_content_json = spelling_content_list[1].split("```")[0]
-    spelling_content_table = json.loads(spelling_content_json)["results"]["錯別字"]
-    spelling_table_data = [
-        ["錯別字", spelling_content_table['level'], spelling_content_table['explanation']],
-    ]
+    # 段落結構
+    try:
+        def structure_operation():
+            content = generate_content_by_open_ai_assistant_structure_robot(paragraph, thread_id=None, model_name=model)
+            if "```json" not in content:
+                raise ValueError("回應格式不正確")
+            return content
+        
+        paragraph_structure_content = retry_operation(structure_operation)
+        structure_content_list = paragraph_structure_content.split("```json")
+        content_text += "\n" + structure_content_list[0]
+        structure_json = structure_content_list[1].split("```")[0]
+        structure_data = json.loads(structure_json)["results"]["段落結構"]
+        table_data.append(["段落結構", structure_data['level'], structure_data['explanation']])
+    except Exception as e:
+        error_messages.append(f"段落結構評估出錯: {str(e)}")
 
-    # ========= 合併 =========
-    table_data.extend(spelling_table_data)
-    content_text = content_text + "\n" + spelling_content_text
+    # 錯別字
+    try:
+        def spelling_operation():
+            content = generate_content_by_open_ai_assistant_spelling_robot(paragraph, thread_id=None, model_name=model)
+            if "```json" not in content:
+                raise ValueError("回應格式不正確")
+            return content
+        
+        spelling_content = retry_operation(spelling_operation)
+        spelling_content_list = spelling_content.split("```json")
+        content_text += "\n" + spelling_content_list[0]
+        spelling_json = spelling_content_list[1].split("```")[0]
+        spelling_data = json.loads(spelling_json)["results"]["錯別字"]
+        table_data.append(["錯別字", spelling_data['level'], spelling_data['explanation']])
+    except Exception as e:
+        error_messages.append(f"錯別字檢查出錯: {str(e)}")
 
     # 綜合評分
-    grade = assign_grade(
-        data['主題與內容']['level'], 
-        paragraph_structure_content_table['level'],
-        data['遣詞造句']['level'], 
-        spelling_content_table['level']
-    )
-    grade_content_text = f"# 綜合評分：{grade}"
-    total_content_text = grade_content_text + "\n" + content_text
+    if len(table_data) == 4:  # 確保所有評估都成功完成
+        grade = assign_grade(
+            table_data[0][1],  # 主題與內容
+            table_data[2][1],  # 段落結構
+            table_data[1][1],  # 遣詞造句
+            table_data[3][1]   # 錯別字
+        )
+        grade_content_text = f"# 綜合評分：{grade}"
+        content_text = grade_content_text + "\n" + content_text
 
-    # 綜合回饋
-    feedback_match = re.search(r"綜合回饋(.*?)評分標準與回饋", content_text, re.DOTALL)
-    feedback_text = feedback_match.group(1).strip() if feedback_match else ""
-    table_data.append(["綜合評分", grade, feedback_text])
+        # 綜合回饋
+        feedback_match = re.search(r"綜合回饋(.*?)評分標準與回饋", content_text, re.DOTALL)
+        feedback_text = feedback_match.group(1).strip() if feedback_match else "無法提取綜合回饋"
+        table_data.append(["綜合評分", grade, feedback_text])
+    else:
+        error_messages.append("無法進行綜合評分，因為部分評估未完成")
+
+    # 處理錯誤信息
+    if error_messages:
+        error_content = "\n".join(error_messages)
+        content_text = f"評估過程中遇到以下問題:\n{error_content}\n\n" + content_text
 
     content_table = gr.update(value=table_data, headers=headers, visible=True)
     
-    return total_content_text, content_table
+    return content_text, content_table
 
 def get_chinese_paragraph_1st_evaluate_content(
         thread_id,