Update app.py

app.py

@@ -1,6 +1,5 @@
 import time
 import gradio as gr  # 確認已正確導入 gradio
-import openai
 import os
 import requests
 import json
@@ -13,14 +12,6 @@ if not api_key:
 # OpenAI API key
 openai_api_key = api_key
 
-# 將 Gradio 的歷史紀錄轉換為 OpenAI 格式
-def transform_history(history):
-    new_history = []
-    for chat in history:
-        new_history.append({"role": "user", "content": chat[0]})
-        new_history.append({"role": "assistant", "content": chat[1]})
-    return new_history
-
 # 檢查是否為與調酒相關的問題
 def is_related_to_bars(message):
     keywords = [
@@ -38,9 +29,7 @@ def response(message, history):
         print(f"問題與調酒無關: {message}")
         return "抱歉，我只能回答與調酒、酒吧、酒類相關的問題。"  # In Traditional Chinese
 
-    # 如果問題相關，將歷史紀錄轉換為 OpenAI 格式
-    conversation_history = transform_history(history)
-
+    # 呼叫 OpenAI API
     url = "https://api.openai.com/v1/chat/completions"
     headers = {
         "Content-Type": "application/json",
@@ -49,52 +38,33 @@ def response(message, history):
 
     # 設置初始的 prompt_instruction
     prompt_instruction = """
-    你是酒吧的專業助理，名字叫做 '小李子'，要以專業、冷淡但是非常有禮貌的口氣，與用戶互動並解答問題:
+    你是李宗諺的專業小助教，名字叫做 '小李子'，要以專業、冷淡但是非常有禮貌的口氣，與用戶互動並解答問題:
     """
     prompt_to_gpt = prompt_instruction + message
 
-    # 新增至 conversation_history
-    conversation_history.append({"role": "system", "content": prompt_to_gpt})
-
     # 設置請求的數據
     data = {
-        "model": "gpt-4",  # 確認使用的模型是 gpt-4 或 gpt-3.5-turbo
-        "messages": conversation_history,
+        "model": "gpt-4o",  # 使用 GPT-4 或 gpt-3.5-turbo
+        "messages": [{"role": "system", "content": prompt_to_gpt}],
         "max_tokens": 200  # 控制生成的最大令牌數
     }
 
-    # 發送請求到 OpenAI API
     try:
         response = requests.post(url, headers=headers, data=json.dumps(data))
-        response.raise_for_status()  # 檢查 API 是否返回錯誤
+        response.raise_for_status()  # 檢查是否有錯誤
         response_json = response.json()
 
-        # Debugging: Print the response to ensure we can see what comes back from the API
+        # Debugging: 打印 API 回應
         print("API Response:", response_json)
 
-        # 提取模型的回應並加入歷史紀錄
         if 'choices' in response_json and len(response_json['choices']) > 0:
             model_response = response_json['choices'][0]['message']['content']
-            conversation_history.append({"role": "assistant", "content": model_response})
-
-            # 逐字回傳生成的文字，實現打字機效果
-            for i in range(len(model_response)):
-                time.sleep(0.05)  # 每個字符間隔 0.05 秒
-                yield model_response[: i+1]
+            return model_response
         else:
-            yield "Error: No response from the model."
-    except requests.exceptions.HTTPError as errh:
-        print(f"HTTP Error: {errh}")
-        yield f"HTTP Error: {errh}"
-    except requests.exceptions.ConnectionError as errc:
-        print(f"Error Connecting: {errc}")
-        yield f"Error Connecting: {errc}"
-    except requests.exceptions.Timeout as errt:
-        print(f"Timeout Error: {errt}")
-        yield f"Timeout Error: {errt}"
-    except requests.exceptions.RequestException as err:
-        print(f"Request Exception: {err}")
-        yield f"Request Exception: {err}"
+            return "Error: No response from the model."
+    except Exception as e:
+        print(f"API Error: {e}")
+        return f"API Error: {e}"
 
 # 初始訊息
 def initial_message():