Update app.py

app.py

@@ -6,11 +6,11 @@ import os
 user_profile = {
     "mode": None,                # 是 / 否 （Backward / Forward）
     "specific_career": None,     # Backward 目标职业
-    "bg_info": None,             # Forward/Backward共用，学术背景
-    "work_value": None,          # Forward：工作意义
-    "personality_summary": None, # Forward：性格总结
-    "dream_day": None,           # Forward：理想一天
-    "forward_direction_choice": None  # 学生在3大方向中选哪一个
+    "bg_info": None,             # 学术背景
+    "work_value": None,          # 工作意义
+    "personality_summary": None, # 性格总结
+    "dream_day": None,           # 理想工作一天
+    "forward_direction_choice": None  # 选的大方向
 }
 
 # ============================ 基础问题 ============================
@@ -20,15 +20,13 @@ base_questions = [
 
 # ============================ Forward 模式问题 ============================
 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。"""),
+    ("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)"""),
+    ("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？"""),
+    ("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？""")
+    ("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？")
 ]
 
 # ============================ Backward 模式问题 ============================
@@ -49,10 +47,10 @@ backward_done = False
 forward_recommendation_given = False
 
 # 新增：多次换方向、深度分析、路线图
-recommendation_round = 0      # 记录已换几次大方向
-forward_deep_dive_done = False
-roadmap_offered = False
-roadmap_done = False
+recommendation_round = 0          # 换方向轮数
+forward_deep_dive_done = False    # 是否已进行“深度分析”
+roadmap_offered = False           # 是否已询问要不要时间轴
+roadmap_done = False              # 是否已生成或放弃时间轴
 
 # ============================ 模型设置 ============================
 model_default = "gpt-4o"
@@ -60,7 +58,7 @@ token_default = 2000
 temp_default = 0.7
 top_p_default = 0.95
 
-# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 路线图函数 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 路线图生成函数 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 def do_time_roadmap():
     direction = user_profile["forward_direction_choice"] or user_profile["specific_career"]
     bg_info = user_profile["bg_info"] or "未知专业"
@@ -85,9 +83,29 @@ def do_time_roadmap():
 
 请用Markdown分段写作，结合学生当前背景合理推断要点，写得具体些。
 """
-    return call_openai(prompt_roadmap, "你是一位专业的职业规划顾问，会生成时间轴式路线图")
+    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)
+
+        msgs = [
+            {"role": "system", "content": "你是一位专业的职业规划顾问，会生成时间轴式路线图"},
+            {"role": "user", "content": prompt_roadmap}
+        ]
+        resp = client.chat.completions.create(
+            model=model_default,
+            messages=msgs,
+            max_tokens=token_default,
+            temperature=temp_default,
+            top_p=top_p_default,
+            stream=False
+        )
+        return resp.choices[0].message.content
+    except Exception as e:
+        return f"生成路线图时出错: {str(e)}"
 
-# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 深度分析：3个具体职业 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 深度分析函数（3个具体职业） ~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 def do_deep_analysis():
     direction = user_profile["forward_direction_choice"] or "尚未选择"
     bg_info = user_profile["bg_info"] or "未知背景"
@@ -103,18 +121,37 @@ def do_deep_analysis():
 理想工作: {dd}
 
 请你再深入分析，为学生推荐3个更具体的职业，并说明：
-1. 这些职业对学生背景的契合度
+1. 这些职业对学生背景的契合度（机械工程/心理学/商科等都可）
 2. 日常工作内容
 3. 需要哪些课程或考试
-4. 利用本校资源建议
-5. 实习 & 经验积累
+4. 本校资源如何利用
+5. 实习 & 经验积累建议
 6. 简历优化思路
 
 用中文分段写作，字数500+。
 """
-    return call_openai(deep_prompt, "你是一位专业职业顾问，会深度分析并列出3个具体职业。")
+    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)
+        msgs = [
+            {"role": "system", "content": "你是一位专业职业顾问，会深度分析并列出3个具体职业。"},
+            {"role": "user", "content": deep_prompt}
+        ]
+        resp = client.chat.completions.create(
+            model=model_default,
+            messages=msgs,
+            max_tokens=token_default,
+            temperature=temp_default,
+            top_p=top_p_default,
+            stream=False
+        )
+        return resp.choices[0].message.content
+    except Exception as e:
+        return f"深度分析出错: {str(e)}"
 
-# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 推荐3大方向 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 推荐大方向函数 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 def recommend_3directions():
     bg_info = user_profile["bg_info"] or "未知"
     wv = user_profile["work_value"] or "无"
@@ -127,29 +164,23 @@ def recommend_3directions():
 - 工作价值: {wv}
 - 性格: {ps}
 - 理想工作: {dd}
-
 分两部分：
 [一、人物画像]：1) 学术背景 2) 价值观 3) 性格 4) 理想工作
 [二、推荐3个大方向]：每个方向(1~2段文字)
-
 最后用如下结尾：
 1. XXX方向
 2. XXX方向
 3. XXX方向
 如都不满意，可输入'换'。
 """
-    return call_openai(rec_prompt, "你是一位职业顾问，擅长根据用户资料推荐3大方向")
-
-# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Call OpenAI ~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-def call_openai(user_prompt, system_text):
     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)
         msgs = [
-            {"role":"system","content": system_text},
-            {"role":"user","content": user_prompt}
+            {"role": "system", "content": "你是一位职业顾问，擅长根据用户资料推荐3大方向"},
+            {"role": "user", "content": rec_prompt}
         ]
         resp = client.chat.completions.create(
             model=model_default,
@@ -161,7 +192,8 @@ def call_openai(user_prompt, system_text):
         )
         return resp.choices[0].message.content
     except Exception as e:
-        return f"出错: {str(e)}"
+        return f"多方向推荐出错: {str(e)}"
+
 
 # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~ generate_system_prompt ~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 def generate_system_prompt():
@@ -203,6 +235,7 @@ def generate_system_prompt():
 📍 现在 → 🎓 学习建议 → 💼 实践建议 → 📄 认证建议 → 🚀 求职建议
 """
 
+
 # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 主逻辑函数 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 def predict(message, history):
     global current_q_index, questions
@@ -218,12 +251,15 @@ def predict(message, history):
     if not history:
         current_q_index = 0
         questions = base_questions[:]
-        for k in user_profile:
-            user_profile[k] = None
-
-        in_forward_flow = in_backward_flow = False
-        forward_index = backward_index = 0
-        forward_done = backward_done = False
+        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
         forward_recommendation_given = False
         recommendation_round = 0
         forward_deep_dive_done = False
@@ -253,7 +289,7 @@ def predict(message, history):
     # ~~~~~~~ Backward模式 ~~~~~~~
     if "是" in mode:
         if in_backward_flow and not backward_done:
-            # 存储上一题回答
+            # 存储上一轮回答
             if backward_index > 0 and backward_index <= len(backward_additional_questions):
                 prev_key = backward_additional_questions[backward_index - 1][0]
                 user_profile[prev_key] = message.strip()
@@ -266,40 +302,64 @@ def predict(message, history):
                 backward_done = True
 
         if backward_done:
-            sprompt = generate_system_prompt()
-            return call_openai(f"请根据{user_profile}给出完整规划", sprompt)
+            # 直接进入最终
+            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)
+                system_prompt = generate_system_prompt()
+
+                msgs = [
+                    {"role":"system","content": system_prompt},
+                    {"role":"user","content": f"请根据信息{user_profile}给出完整规划"}
+                ]
+                resp = client.chat.completions.create(
+                    model=model_default,
+                    messages=msgs,
+                    max_tokens=token_default,
+                    temperature=temp_default,
+                    top_p=top_p_default,
+                    stream=False
+                )
+                return resp.choices[0].message.content
+            except Exception as e:
+                return f"发生错误: {str(e)}"
 
     # ~~~~~~~ Forward模式 ~~~~~~~
     else:
         if in_forward_flow and not forward_done and not forward_deep_dive_done and not roadmap_done:
-            # 存储上一题回答
+            # 存储上一回答
             if forward_index > 0 and forward_index <= len(forward_additional_questions):
                 prev_key = forward_additional_questions[forward_index - 1][0]
                 user_profile[prev_key] = message.strip()
 
-            # 若还有Forward问题没问完
+            # 若还有 Forward问题
             if forward_index < len(forward_additional_questions):
                 k, prompt_text = forward_additional_questions[forward_index]
                 forward_index += 1
                 return prompt_text
-            # 若4个问完 -> 推荐3方向
+
+            # 如果4个Forward问完，还没推荐
             elif not forward_recommendation_given:
                 forward_recommendation_given = True
                 return recommend_3directions()
+
+            # 已推荐过方向，等待学生选1/2/3 or '换'
             else:
-                # 用户输入 1/2/3 或 '换'
                 choice = message.strip().lower()
                 if choice in ["1","2","3"]:
                     user_profile["forward_direction_choice"] = choice
+                    # 进入深度分析
                     forward_deep_dive_done = True
                     return do_deep_analysis()
                 elif "换" in choice:
                     recommendation_round += 1
-                    if recommendation_round > 2:
+                    if recommendation_round > 2:  # 最多换2次
                         forward_done = True
                         return "已多次换方向，先进入下个阶段吧。"
-                    else:
-                        return recommend_3directions()
+                    # 再次推荐
+                    return recommend_3directions()
                 else:
                     return "请回复1/2/3选择方向，或输入'换'来请求新的推荐"
 
@@ -309,7 +369,6 @@ def predict(message, history):
             return "需要一个时间轴式的职业路线图吗？如果需要，请回复“是”，否则回复“否”。"
 
         elif roadmap_offered and not roadmap_done:
-            # 生成 or 不生成
             ans = message.strip().lower()
             if ans == "是":
                 roadmap_done = True
@@ -321,45 +380,133 @@ def predict(message, history):
                 return "好的，不生成路线图，本次规划到此结束。"
 
         if forward_done:
-            # 最终or结束
-            sprompt = generate_system_prompt()
-            return call_openai(f"以下是学生资料: {user_profile}, 如需更多建议可再次输入问题", sprompt)
+            # 最终生成 or 就结束
+            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)
+                system_prompt = generate_system_prompt()
+
+                msgs = [
+                    {"role":"system","content": system_prompt},
+                    {"role":"user","content": f"以下是学生资料: {user_profile}. 如需更详细方案可再次输入问题"}
+                ]
+                resp = client.chat.completions.create(
+                    model=model_default,
+                    messages=msgs,
+                    max_tokens=token_default,
+                    temperature=temp_default,
+                    top_p=top_p_default,
+                    stream=False
+                )
+                return resp.choices[0].message.content
+            except Exception as e:
+                return f"发生错误: {str(e)}"
 
     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() as demo:
+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;'>🎓 多步职业规划助手</h1>
+            <p style='font-size: 18px; font-weight:bold; color:#ec4899; margin-top: 0;'>Forward三步：推荐大方向→深度分析→可选时间轴路线图</p>
+          </div>
+        </div>
+        """)
+
     gr.Markdown("""
-    # 🎓 多步职业规划助手
-    Forward / Backward多轮问答，支持：
-    1. Forward可换3大方向
-    2. 学生选定方向后→深度分析3个具体职业
-    3. 可选时间轴规划
+    **📝 Forward / Backward 多轮交互，附带换方向、深度分析、以及可选时间轴路线图**  
+    1. 如果你已有明确职业目标 => 回答“是”（Backward）  
+    2. 如果还在探索 => 回答“否”（Forward）  
+    3. Forward模式下：先问4个问题→推荐3大方向→可换→选定后深度分析→询问是否要路线图
     """)
 
-    chatbot = gr.Chatbot()
-    msg = gr.Textbox()
-    send = gr.Button("发送")
-    reset = gr.Button("重置")
+    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")
 
-    def add_user_message(m, h):
-        h = h or []
-        h.append({"role":"user","content":m})
-        return "", h
+    with gr.Row():
+        reset_btn = gr.Button("🔄 重新开始")
 
-    def add_bot_response(h):
-        user_m = h[-1]["content"]
-        bot_m = predict(user_m, h[:-1])
-        h.append({"role":"assistant","content":bot_m})
-        return h
+    def add_message(message, history):
+        history = history or []
+        history.append({"role": "user", "content": message})
+        return "", history
 
-    send.click(add_user_message, [msg, chatbot], [msg, chatbot]) \
-        .then(add_bot_response, chatbot, chatbot)
+    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
 
-    msg.submit(add_user_message, [msg, chatbot], [msg, chatbot]) \
-       .then(add_bot_response, chatbot, chatbot)
+    submit_btn.click(fn=add_message, inputs=[msg, chatbot], outputs=[msg, chatbot]) \
+              .then(fn=bot_response, inputs=[chatbot], outputs=[chatbot])
 
-    def reset_all():
+    msg.submit(fn=add_message, inputs=[msg, chatbot], outputs=[msg, chatbot]) \
+       .then(fn=bot_response, inputs=[chatbot], outputs=[chatbot])
+
+    def reset_state():
         global current_q_index, questions
         global in_forward_flow, in_backward_flow
         global forward_index, backward_index
@@ -371,24 +518,29 @@ with gr.Blocks() as demo:
 
         current_q_index = 0
         questions = base_questions[:]
-        for k in user_profile:
-            user_profile[k] = None
-
-        in_forward_flow = in_backward_flow = False
-        forward_index = backward_index = 0
-        forward_done = backward_done = False
+        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
         forward_recommendation_given = False
         recommendation_round = 0
         forward_deep_dive_done = False
         roadmap_offered = False
         roadmap_done = False
+
         return []
 
-    reset.click(reset_all, outputs=chatbot)
+    reset_btn.click(fn=reset_state, inputs=None, outputs=chatbot, queue=False)
 
-    def start_q():
-        return [{"role":"assistant","content": questions[0][1]}]
+    def auto_first_question():
+        # 启动时自动问第一个问题
+        return [{"role": "assistant", "content": questions[0][1]}]
 
-    demo.load(start_q, outputs=chatbot)
+    demo.load(auto_first_question, inputs=None, outputs=chatbot)
+    demo.launch()
 
-demo.launch()