Update app.py

app.py

@@ -9,7 +9,8 @@ user_profile = {
     "bg_info": None,
     "work_value": None,
     "personality_summary": None,
-    "dream_day": None
+    "dream_day": None,
+    "forward_direction_choice": None
 }
 
 # ============================ 基础问题 ============================
@@ -50,6 +51,7 @@ forward_index = 0
 backward_index = 0
 forward_done = False
 backward_done = False
+forward_recommendation_given = False
 
 # ============================ 模型设置 ============================
 model_default = "gpt-4o"
@@ -65,6 +67,7 @@ def generate_system_prompt():
     work_value = user_profile["work_value"]
     personality_summary = user_profile["personality_summary"]
     dream_day = user_profile["dream_day"]
+    forward_direction_choice = user_profile["forward_direction_choice"]
 
     if "是" in (mode or ""):
         return f"""
@@ -83,17 +86,18 @@ def generate_system_prompt():
     else:
         return f"""
 你是一位专业的职业规划顾问，使用Forward Design方法帮助学生探索合适的职业路径。
+
 学生背景:
 - 学术背景: {bg_info}
 - 工作意义: {work_value}
 - 性格总结: {personality_summary}
 - 理想一天: {dream_day}
+- 选择方向: {forward_direction_choice}
+
 请输出：
 1. 学生的优势、性格、价值观分析
-2. 推荐6个职业方向，并说明匹配理由
-3. 等学生选择后，再推荐3个具体职业，每个说明日常内容、适配性、要求、准备路径
-最后请用Markdown格式输出职业路径图：
-📍 现在 → 🎓 学习建议 → 💼 实践建议 → 📄 认证建议 → 🚀 求职建议
+2. 推荐具体职业（3个），说明日常内容、适配性、要求、准备路径
+3. 输出职业路径图：📍 现在 → 🎓 → 💼 → 📄 → 🚀
 """
 
 # ============================ 主逻辑函数 ============================
@@ -101,19 +105,17 @@ def predict(message, history):
     global current_q_index, questions
     global in_forward_flow, in_backward_flow
     global forward_index, backward_index
-    global forward_done, backward_done
+    global forward_done, backward_done, forward_recommendation_given
 
     if not history:
         current_q_index = 0
         questions = base_questions[:]
         for key in user_profile:
             user_profile[key] = None
-        in_forward_flow = False
-        in_backward_flow = False
-        forward_index = 0
-        backward_index = 0
-        forward_done = False
-        backward_done = False
+        in_forward_flow = in_backward_flow = False
+        forward_index = backward_index = 0
+        forward_done = backward_done = False
+        forward_recommendation_given = False
 
     if current_q_index > 0 and current_q_index <= len(questions):
         key = questions[current_q_index - 1][0]
@@ -144,7 +146,38 @@ def predict(message, history):
                 key, q_text = forward_additional_questions[forward_index]
                 forward_index += 1
                 return q_text
+            elif not forward_recommendation_given:
+                forward_recommendation_given = True
+                try:
+                    api_key = os.environ.get("API_TOKEN")
+                    if not api_key:
+                        return "错误：API_TOKEN 未设置"
+                    client = OpenAI(api_key=api_key)
+                    recommendation_prompt = f"""
+根据以下信息，总结学生的特点，并推荐3个职业方向（大类）。最后请以如下格式结尾：\n
+1. xxx\n2. xxx\n3. xxx\n\n请学生从中选择一个方向继续：
+- 学术背景: {user_profile['bg_info']}
+- 工作意义: {user_profile['work_value']}
+- 性格总结: {user_profile['personality_summary']}
+- 理想一天: {user_profile['dream_day']}
+"""
+                    messages = [
+                        {"role": "system", "content": "你是一位职业顾问，善于总结和推荐方向。"},
+                        {"role": "user", "content": recommendation_prompt}
+                    ]
+                    response = client.chat.completions.create(
+                        model=model_default,
+                        messages=messages,
+                        max_tokens=token_default,
+                        temperature=temp_default,
+                        top_p=top_p_default,
+                        stream=False
+                    )
+                    return response.choices[0].message.content
+                except Exception as e:
+                    return f"生成推荐方向时出错: {str(e)}"
             else:
+                user_profile["forward_direction_choice"] = message.strip()
                 forward_done = True
 
     if in_backward_flow and backward_index > 0 and not backward_done:
@@ -184,119 +217,3 @@ def predict(message, history):
 
     return "信息收集完毕，若尚未得到最终回复，请输入任意文字以继续。"
 
-
-# ============================ Gradio UI ============================
-with gr.Blocks(css="""
-body {
-    background-color: #1e1e1e;
-    color: #ffffff;
-}
-.gradio-container {
-    font-family: 'Segoe UI', sans-serif;
-}
-.message.user {
-    background-color: #cce6ff !important;
-    color: #000000 !important;
-    border-radius: 10px !important;
-    padding: 10px;
-    margin: 6px;
-}
-.message.bot {
-    background-color: #5599ff !important;
-    color: #000000 !important;
-    border-radius: 10px !important;
-    padding: 10px;
-    margin: 6px;
-}
-.gradio-container .chat-msg.bot-msg .message.bot p {
-    color: #000000 !important;
-}
-.gr-button {
-    border-radius: 8px;
-}
-#custom-send {
-    background-color: #ec4899 !important;
-    color: white !important;
-    border-radius: 999px !important;
-    padding: 10px 24px !important;
-    font-weight: bold;
-    box-shadow: 0 0 10px #ec4899;
-    transition: all 0.3s ease-in-out;
-}
-#custom-send:hover {
-    background-color: #d63384 !important;
-    box-shadow: 0 0 12px #ec4899;
-}
-textarea, input {
-    background-color: #ffe4f1 !important;
-    color: #5e2c49 !important;
-    border: 1px solid #ec4899 !important;
-}
-footer {
-    display: none !important;
-}
-""") as demo:
-    with gr.Row():
-        gr.HTML("""
-        <div style='display: flex; align-items: center; justify-content: center; gap: 20px; margin-bottom: 10px;'>
-          <img src='https://media3.giphy.com/media/v1.Y2lkPTc5MGI3NjExMmFucGwxbmNsd3J5NXV0Y282NXNtMzNsZW5jMm4wNWh6c2dqbXIwdiZlcD12MV9pbnRlcm5hbF9naWZfYnlfaWQmY3Q9Zw/l41m18LjqpzxUr2WA/giphy.gif' width='200' style='border-radius: 12px; box-shadow: 0 0 10px #ec4899;'>
-          <div style='text-align: left;'>
-            <h1 style='color:white; font-size: 36px; margin-bottom: 6px;'>🎓 AI职业规划助手（升级版）</h1>
-            <p style='font-size: 18px; font-weight:bold; color:#ec4899; margin-top: 0;margin-left: 150px'>让梦想照进现实 💖</p>
-          </div>
-        </div>
-        """)
-    gr.Markdown("""
-    **📝 个性化职业规划助手：Forward / Backward 多轮交互示例**
-
-    - 如果你已确定了职业目标，选择 **Backward** 模式（回答“是”）。
-    - 如果你需要探索适合的职业方向，选择 **Forward** 模式（回答“否”）。
-    - 我会收集你的基础信息、学术背景，并根据你的回答做多轮引导，最后给出职业规划建议。
-    """)
-    chatbot = gr.Chatbot(height=500, show_label=False, show_copy_button=True, type="messages")
-    with gr.Row():
-        with gr.Column(scale=8):
-            msg = gr.Textbox(placeholder="请在这里输入你的回答...", show_label=False, container=False)
-        with gr.Column(scale=1):
-            submit_btn = gr.Button("🚀 发送", elem_id="custom-send")
-    with gr.Row():
-        reset_btn = gr.Button("🔄 重新开始")
-    def add_message(message, history):
-        history = history or []
-        history.append({"role": "user", "content": message})
-        return "", history
-    def bot_response(history):
-        history = history or []
-        user_message = history[-1]["content"]
-        bot_message = predict(user_message, history[:-1] if len(history) > 1 else [])
-        history.append({"role": "assistant", "content": bot_message})
-        return history
-    submit_btn.click(fn=add_message, inputs=[msg, chatbot], outputs=[msg, chatbot]).then(
-        fn=bot_response, inputs=[chatbot], outputs=[chatbot]
-    )
-    msg.submit(fn=add_message, inputs=[msg, chatbot], outputs=[msg, chatbot]).then(
-        fn=bot_response, inputs=[chatbot], outputs=[chatbot]
-    )
-    def reset_conversation():
-        global current_q_index, questions
-        global in_forward_flow, in_backward_flow
-        global forward_index, backward_index
-        global forward_done, backward_done
-        current_q_index = 0
-        questions = base_questions[:]
-        for key in user_profile:
-            user_profile[key] = None
-        in_forward_flow = False
-        in_backward_flow = False
-        forward_index = 0
-        backward_index = 0
-        forward_done = False
-        backward_done = False
-        return []
-    reset_btn.click(fn=reset_conversation, inputs=None, outputs=chatbot, queue=False)
-    def auto_first_question():
-        return [{"role": "assistant", "content": questions[0][1]}]
-    demo.load(auto_first_question, inputs=None, outputs=chatbot)
-    demo.launch()
-
-