content = parse_and_display_topic_sentences(json_content)

app.py

@@ -84,10 +84,34 @@ def generate_topic_sentences(model, max_tokens, sys_content, scenario, eng_level
     }
 
     response = OPEN_AI_CLIENT.chat.completions.create(**request_payload)
-    content = response.choices[0].message.content
+    json_content = response.choices[0].message.content
+    content = parse_and_display_topic_sentences(json_content)
 
     return content
 
+def parse_and_display_topic_sentences(json_data):
+    """
+    解析JSON格式的主题句数据，并转换成易于阅读的格式。
+    """
+    # 将JSON字符串解析成Python字典
+    data = json.loads(json_data)
+    
+    # 初始化一个空字符串用于存放最终的格式化文本
+    formatted_text = ""
+    
+    # 遍历每个主题句及其评价
+    for key, value in data.items():
+        topic_sentence = value[0]['topic-sentence']
+        appropriate = "適當" if value[0]['appropriate'] == "Y" else "不適當"
+        reason = value[0]['reason']
+        
+        # 将每个主题句的信息添加到格式化文本中
+        formatted_text += f"主题句 {int(key)+1}: {topic_sentence}\n"
+        formatted_text += f"是否適當: {appropriate}\n"
+        formatted_text += f"原因: {reason}\n\n"
+    
+    return formatted_text
+
 with gr.Blocks() as demo:
     with gr.Row():
         with gr.Column():
@@ -127,8 +151,8 @@ with gr.Blocks() as demo:
                 Those sentences must include the three main points:". 
                 Use English language and each sentence should not be too long.
                 For each sentence, explain the reason in Traditional Chinese, Taiwan, 繁體中文 zh-TW. 
-
                 Make sure the vocabulary you use is at level.
+
                 Only return the result in JSON format starting as: 
                 {{ 
                     "0": [ {{ "topic-sentence": "#","appropriate": "Y/N", "reason": "#中文解釋" }} ],