Update app.py

app.py

@@ -9,19 +9,24 @@ API_URL = "https://api.openai.com/v1/chat/completions" #os.getenv("API_URL") + "
 #Testing with my Open AI Key 
 OPENAI_API_KEY = os.getenv("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") 
 
-def predict(inputs, top_p, temperature, openai_api_key, chat_counter, chatbot=[], history=[]):  #repetition_penalty, top_k
+def predict(inputs, top_p, temperature, openai_api_key, chat_counter, chatbot=[], history=[]):
+
+    # 사용자의 입력을 나레이션 스타일의 프롬프트로 변환
+    narration_prompt = f"동영상에 사용할 전문적인 나레이션을 작성하세요. 입력: '{inputs}'"
 
     payload = {
-    "model": "gpt-4-1106-preview",
-    "messages": [{"role": "user", "content": f"{inputs}"}],
-    "temperature" : 1.0,
-    "top_p":1.0,
-    "n" : 1,
-    "stream": True,
-    "presence_penalty":0,
-    "frequency_penalty":0,
+        "model": "gpt-4-1106-preview",
+        "messages": [{"role": "system", "content": narration_prompt}],
+        "temperature": temperature,
+        "top_p": top_p,
+        "n": 1,
+        "stream": True,
+        "presence_penalty": 0,
+        "frequency_penalty": 0,
+        "max_tokens": 1000  # 나레이션의 길이를 제한
     }
 
+
     headers = {
     "Content-Type": "application/json",
     "Authorization": f"Bearer {openai_api_key}"
@@ -71,6 +76,9 @@ def predict(inputs, top_p, temperature, openai_api_key, chat_counter, chatbot=[]
         if counter == 0:
           counter+=1
           continue
+        # 나레이션의 길이를 8줄로 제한
+        if len(history) >= 8:
+            break            
         #counter+=1
         # check whether each line is non-empty
         if chunk.decode() :
@@ -87,12 +95,15 @@ def predict(inputs, top_p, temperature, openai_api_key, chat_counter, chatbot=[]
               chat = [(history[i], history[i + 1]) for i in range(0, len(history) - 1, 2) ]  # convert to tuples of list
               token_counter+=1
               yield chat, history, chat_counter  # resembles {chatbot: chat, state: history}  
-                   
+                
+
+    return chat, history, chat_counter
+
 
 def reset_textbox():
     return gr.update(value='')
 
-title = """<h1 align="center">Lisa Chatbot</h1>"""
+title = """<h1 align="center">글 생성</h1>"""
 description = """Language models can be conditioned to act like dialogue agents through a conversational prompt that typically takes the form:
 ```
 User: <utterance>