edit UI for small device & refactor major func

gpt_based_function.py

@@ -2,33 +2,49 @@ import ast
 import openai
 from text_annotator import generate_annotated_text
 
+def gpt_summary_generator(user_text):
+    '''
+    :param user_text:
+    :return:
+    '''
+    task_description_fs = "Summarize the text in 500 characters in Korean."
+    user_prompt = f"{user_text}"
+    messages = [{"role": "system", "content": task_description_fs}, {"role": "user", "content": user_prompt}]
+    response = openai.ChatCompletion.create(
+        model="gpt-3.5-turbo-16k",
+        messages=messages,
+        temperature=0.06,
+        max_tokens=8362,
+        top_p=0,
+        frequency_penalty=0,
+        presence_penalty=0
+    )
+    gpt_summary = response['choices'][0]['message']['content']
+
+    return gpt_summary
+
 
 def gpt_keyword_highlighter(user_text):
     '''
     :param user_text: str
     :return: annotated_text: str
     '''
-    task_description = "You are a Python function that extract 5 keywords from {input_text}. The output should be formatted as ['keyword1', 'keyword2', ...]. Return only the function's output, with no additional explanations."
-
+    # get keywords from user_text
+    task_description = "You are a Python function that extracts 5 keywords from {input_text} considering {overall_text_contents}. The output should be formatted as ['keyword1', 'keyword2', ...]. Return only the function's output, with no additional explanations."
     user_prompt = r"{input_text}=" + f"{user_text}"
-
     messages = [{"role": "system", "content": task_description}, {"role": "user", "content": user_prompt}]
-
     response = openai.ChatCompletion.create(
         model="gpt-3.5-turbo",
         messages=messages,
         temperature=0,
-        max_tokens=1019,
+        max_tokens=2006,
         top_p=0,
         frequency_penalty=0,
         presence_penalty=0
     )
-
     extracted_keywords = response['choices'][0]['message']['content']
-
     ## literal_eval 함수를 사용하여 string을 list로 변환
     extracted_keywords = ast.literal_eval(extracted_keywords)
-
     ## highlighted_text 후처리 함수 추가
     highlighted_text = generate_annotated_text(text=user_text, keyw_list=extracted_keywords)