Update app.py

app.py

@@ -2,9 +2,9 @@ import gradio as gr
 from openai import OpenAI
 import os
 
-# ============================ 用户信息结构 ============================
+
 user_profile = {
-    "mode": None,  # 只在回答第一个问题时设置（是/否）
+    "mode": None,  
     "specific_career": None,
     "bg_info": None,
     "work_value": None,
@@ -12,33 +12,35 @@ user_profile = {
     "dream_day": None,
     "forward_direction_choice": None,
 
-    # Forward模式下更多状态
-    "recommended_directions": [],  # 3个大方向
-    "selected_direction": None,    # 学生最终选定的大方向
-    "recommended_jobs": [],        # 3个具体职业
-    "final_choice": None           # 学生最终选定的具体职业
+    "recommended_directions": [],  
+    "selected_direction": None,    
+    "recommended_jobs": [],        
+    "final_choice": None           
 }
 
-# ============================ 基础问题 ============================
+
 base_questions = [
-    ("mode", "你是否有心仪的职业方向？\n- 如果有，请回复'是'（Backward模式）\n- 如果没有，请回复'否'（Forward模式）")
+    ("mode", "Do you have any preferred future plans？\n- if yes, reply'Y'（Backward Mode）\n- if NO,reply'NO'（Forward Mode）")
 ]
 
-# ============================ Forward 模式问题 ============================
+
+
+
+# ============================ Forward Mode Questions ============================
 forward_additional_questions = [
-    ("bg_info", """那么接下来我会需要你提供一些你的资料，并分享一些你的性格和喜好。请先告诉我，你的学校、年级、专业，和主修课程是什么？如果能提供你的选课表或resume就更好啦。"""),
-    ("work_value", """非常好！那你认为"工作"的意义是什么？你觉得一份理想的工作，应该带来哪些价值或满足感？（例如：帮助他人、赚大钱、自由时间、个人成长、创意空间等）"""),
-    ("personality_summary", "用几句话总结你的性格：比如外向/内向？喜欢挑战？注重细节？讨厌重复吗？"),
-    ("dream_day", "再描述一下你理想工作的一天是什么样：在哪工作？做什么？和谁合作？忙还是闲？更自由还是更有秩序？")
+    ("bg_info", """To get started, please tell me a bit about your academic background and interests. What's your school, year, major, and core courses? If you have a transcript or resume, that would be even better!"""),
+    ("work_value", """Great! What do you think is the meaning or value of 'work'? What should an ideal job provide for you? (e.g., helping others, high income, free time, personal growth, creative space, etc.)"""),
+    ("personality_summary", "Briefly describe your personality: Are you introverted or extroverted? Do you enjoy challenges? Detail-oriented? Do you dislike repetitive work?"),
+    ("dream_day", "Now describe what your ideal workday looks like: Where are you working? What are you doing? Who are you collaborating with? Is it busy or relaxed? More freedom or more structure?")
 ]
 
-# ============================ Backward 模式问题 ============================
+# ============================ Backward Mode Questions ============================
 backward_additional_questions = [
-    ("specific_career", "请具体描述你想要从事的职业方向。"),
-    ("bg_info", "请提供你的学校、年级、专业以及已有的实习或项目经历，以便更好地帮你规划如何达成目标。")
+    ("specific_career", "Please describe the specific career path you want to pursue."),
+    ("bg_info", "Please provide your school, year, major, and any internship or project experience you already have, so I can help you build a clearer path to your goal.")
 ]
 
-# ============================ 状态控制变量 ============================
+
 current_q_index = 0
 questions = base_questions[:]
 in_forward_flow = False
@@ -53,44 +55,45 @@ forward_deep_dive_done = False
 roadmap_offered = False
 roadmap_done = False
 
-direction_chosen = False    # 是否已经选定大方向
-jobs_recommended = False    # 是否已给出3个具体职业
-job_chosen = False          # 是否选定了具体职业
+direction_chosen = False    
+jobs_recommended = False    
+job_chosen = False         
+
+
+post_career_detail_asked = False  
+post_career_detail_done = False  
 
-# ========== 用于 A/B/C 专项问题的标记 ==========
-post_career_detail_asked = False  # 是否已询问用户要看A/B/C
-post_career_detail_done = False   # 是否已回答完A/B/C
 
-# ============================ 模型设置 ============================
 model_default = "gpt-4o"
 token_default = 2000
 temp_default = 0.7
 top_p_default = 0.95
 
 
-# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-#  动态函数：根据用户选择的 A/B/C，让 OpenAI 生成相应分析
+
 def backward_strategy_plan(specific_career, bg_info):
     prompt = f"""
-你是一位专业的职业规划顾问。
-现在有一位学生，他的目标职业是：{specific_career}
-他的当前背景是：{bg_info}
-请根据这个学生的背景和目标，帮他做一个清晰的达成路径规划，内容要包括：
-1. 是否需要考虑转专业或辅修？
-2. 是否需要补哪些课？课程关键词有哪些？
-3. 推荐的证书/考试（如CFA等）和学习建议
-4. 推荐的实习方向（基于他的背景）
-5. 如何利用本校资源（career center、club、networking等）
-6. 如果他背景不匹配（比如机械去金融），如何填补Gap？
-请使用 Markdown 分段格式，不少于300字，内容务必紧扣他目前的背景和目标。
+You are a professional career planning advisor.
+A student has the target career: {specific_career}
+Their current background is: {bg_info}
+
+Please help the student build a clear plan to reach their goal, including:
+1. Should they consider changing majors or taking a minor?
+2. Are there any courses they should take? What are the key course topics?
+3. Recommended certificates/exams (e.g., CFA) and study tips
+4. Suggested internship directions (based on their background)
+5. How to make use of university resources (career center, clubs, networking, etc.)
+6. If their background is mismatched (e.g., mechanical engineering to finance), how to bridge the gap?
+
+Use Markdown formatting in sections, with a minimum of 300 words. The content must align closely with the student's current background and goals.
 """
     try:
         api_key = os.environ.get("API_TOKEN")
         if not api_key:
-            return "错误：API_TOKEN 未设置"
+            return "Wrong: API_TOKEN not set"
         client = OpenAI(api_key=api_key)
         msgs = [
-            {"role": "system", "content": "你是一位经验丰富的职业顾问，擅长根据用户背景制定路径规划"},
+            {"role": "system", "content": "You are an experienced career advisor, good at creating roadmaps based on user background. "},
             {"role": "user", "content": prompt}
         ]
         resp = client.chat.completions.create(
@@ -103,7 +106,7 @@ def backward_strategy_plan(specific_career, bg_info):
         )
         return resp.choices[0].message.content
     except Exception as e:
-        return f"生成职业路径建议时出错: {str(e)}"
+        return f"Wrong path: {str(e)}"
 
 # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 def answer_abc_questions(selected, bg_info, wv, ps, dd):
@@ -719,8 +722,8 @@ def predict(message, history):
             except Exception as e:
                 return f"发生错误: {str(e)}"
 
-    # 兜底
-    return "信息收集完毕，如还未得到最终回复，请再输入任意文字继续。"
+    
+    return "Information down"
 
 # ============================ Gradio UI ============================
 import gradio as gr
@@ -791,9 +794,9 @@ footer {
         """)
 
     chatbot = gr.Chatbot(height=500, show_label=False, show_copy_button=True, type="messages")
-    msg = gr.Textbox(placeholder="请输入你的回答...")
-    send = gr.Button("发送 🚀", elem_id="custom-send")
-    reset = gr.Button("🔄 重新开始")
+    msg = gr.Textbox(placeholder="enter your answer...")
+    send = gr.Button("Send 🚀", elem_id="custom-send")
+    reset = gr.Button("🔄 Restart")
 
     def add_user_message(m, hist):
         hist = hist or []