Update app.py

app.py

@@ -32,7 +32,7 @@ def main():
         # )"""
 
 
-        task_description = """ You are a useful helper that generates annotated text for Python's st-annotated-text library. Your task is to identify the topic of the passage and highlight the key words needed to convey the meaning. You should be able to identify the main points. Also, please mark keywords based on the different paragraphs provided in the text. The output should be formatted in the following way:
+        task_description = """ You are a useful helper that generates annotated text for Python's st-annotated-text library. Your task is to identify the topic of the passage and highlight the key words needed to convey the meaning. You should be able to identify the main points. Also, please mark keywords based on the different paragraphs and headings provided in the text. The output should be formatted in the following way:
         annotated_text(
             "This ",
             ("is", ""),
@@ -48,23 +48,6 @@ def main():
             ". "
         )"""
         
-        #출력 파인튜닝 여러분은 Python의 st-annotated-text 라이브러리를 위해 주석이 달린 텍스트를 생성하는 유용한 도우미입니다. 지문의 주제를 파악하고 의미 전달에 필요한 핵심어를 강조 표시하는 것이 여러분의 임무입니다. 출력물의 형식은 다음과 같은 방식으로 지정해야 합니다.  
-        
-        # task_description = """You are a useful helper that generates annotated text for Python's st-annotated-text library. Your job is to identify the topic of the fingerprint and highlight the key words needed to convey meaning. The output should be formatted in the following way:
-        # annotated_text(
-        #     "This ",
-        #     ("is", ""),
-        #     " some ",
-        #     ("annotated", ""),
-        #     ("text", ""),
-        #     " for those of ",
-        #     ("you", ""),
-        #     " who ",
-        #     ("like", ""),
-        #     " this sort of ",
-        #     ("thing", ""),
-        #     ". "
-        # )"""
 
         # user_prompt = f"Now, please annotate this text: {user_text}"
         
@@ -93,7 +76,7 @@ def main():
         response = openai.ChatCompletion.create(
             model="gpt-3.5-turbo-16k",
             messages=messages,
-            temperature=1.3,
+            temperature=1,
             max_tokens=2500,
             top_p=1,
             frequency_penalty=0,