import gradio as gr import openai import time import json from datetime import datetime import os #TODO #Restart game #Local knowledge base #dynamic difficulty #save log locally - Done client = openai.OpenAI( # 若没有配置环境变量，请用阿里云百炼API Key将下行替换为：api_key="sk-xxx", api_key="sk-8563fbb803fb41868b54d2ab8ba563e4", base_url="https://dashscope.aliyuncs.com/compatible-mode/v1" ) system_prompt = """ 你是架空朝代的皇帝，文治武功，英明神武，权术高超，文武百官无不敬畏。我是你的儿子，今年十岁。 今日你来检查我的学业，心情尚可，但要求极为严苛。 # 角色设定： - **身份**：严父与君王的结合体，对继承人期望极高。 - **语气**：威严、简洁、不容置疑，带有帝王般的压迫感。常用“朕”、“皇子”、“皇儿”称呼。 - **核心行为**：化身无情的出题机器，持续考察皇子对国学经典的掌握。 # 出题规则： 1. **出题内容**：你自己不需要出题，用户会在提示词中给出“已知上句，回答下句”的题目。你需要用皇帝的语气复述这道题。 2. **反馈机制**： - 若皇子答对，给予简单的正反馈，然后立即出下一题。保持压力。 - 若皇子答错，你必须立即予以斥责，并打皇子十下戒尺。你会提示正确答案，然后让皇子再背。 3. **边界条件**：如果皇子的回答十分出格，例如不背书了要去蹴鞠，你要给予额外的严厉惩罚。惩罚完继续出题。 4. **终止条件**： 除非皇子主动哭泣、求饶（说出“我错了”、“别打了”、“疼”等类似词），否则绝不停下出题。一旦皇子求饶，你可表现出失望又略带一丝心疼的情绪，并结束考验。结束时总结一共答对了几题，答错了几题，惩罚有哪些，并输出<游戏结束>作为标记。 """ # 游戏状态 game_state = { "conversation_history": [], # 对话历史 "correct_count": 0, # 连续答对次数 "total_rounds": 0, # 总答题数 "difficulty_level": 1, # 难度等级 (1, 2, 3) "is_game_over": False # 游戏是否结束 } game_state["conversation_history"].append({"role": "system", "content": system_prompt}) def get_ai_response(): """调用API，让皇帝出题""" response = client.chat.completions.create( model="Moonshot-Kimi-K2-Instruct", #model='deepseek-v3', messages=game_state["conversation_history"], temperature=0.7, # 温度不宜过高，保证出题的准确性 top_p=0.95, stream=True, #max_tokens=150, ) full_response = "" for chunk in response: if chunk.choices[0].delta.content is not None: chunk_content = chunk.choices[0].delta.content full_response += chunk_content yield chunk_content # 逐块返回 # 将完整的AI回复加入历史 game_state["conversation_history"].append({"role": "assistant", "content": full_response}) # ai_response = response.choices[0].message.content.strip() # # 将AI的回复（题目）加入历史 # game_state["conversation_history"].append({"role": "assistant", "content": ai_response}) # return ai_response def chat_with_ai(message, chat_history): # 添加用户消息到历史 game_state["conversation_history"].append({"role": "user", "content": message}) #ai_response = get_ai_response() chat_history.append({'role':'user','content':message}) chat_history.append({'role':'assistant', 'content':""}) #return "",chat_history # 获取流式响应并逐步更新聊天界面 full_response = "" for chunk in get_ai_response(): full_response += chunk chat_history[-1] = {'role':'assistant', 'content': full_response} yield "", chat_history # 逐步更新界面 return "", chat_history def get_initial_chat_display(): initial_message = "(在书房里恭敬地站在你面前) 父皇今日可要考校儿臣功课？" game_state["conversation_history"].append({"role": "user", "content": initial_message}) # 获取初始响应（非流式） response = client.chat.completions.create( model="Moonshot-Kimi-K2-Instruct", messages=game_state["conversation_history"], temperature=0.7, top_p=0.95 ) ai_response = response.choices[0].message.content.strip() game_state["conversation_history"].append({"role": "assistant", "content": ai_response}) return [ {'role':'user','content':initial_message}, {'role':'assistant','content':ai_response} ] def clear_history(): """清空对话历史""" game_state["conversation_history"] = [] return "历史已清空" def show_history(): """显示当前对话历史""" history_text = "" for msg in game_state["conversation_history"]: role = "用户" if msg["role"] == "user" else "AI" if msg["role"] == "assistant" else "系统" history_text += f"{role}: {msg['content']}\n\n" return history_text if history_text else "暂无对话历史" def save_conversation_history(): """保存对话历史到JSON文件""" try: # 创建保存目录（如果不存在） os.makedirs("gradio_history", exist_ok=True) # 生成文件名（包含时间戳） timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") filename = f"gradio_history/conversation_{timestamp}.json" # 准备要保存的数据 save_data = { "save_time": datetime.now().strftime("%Y-%m-%d %H:%M:%S"), "total_rounds": game_state["total_rounds"], "correct_count": game_state["correct_count"], "conversation": game_state["conversation_history"] } # 保存到文件 with open(filename, 'w', encoding='utf-8') as f: json.dump(save_data, f, ensure_ascii=False, indent=2) return f"对话历史已保存到: {filename}" except Exception as e: return f"保存失败: {str(e)}" # 创建界面 with gr.Blocks(title="皇帝出题机") as demo: gr.Markdown("# 皇帝出题机") with gr.Row(): with gr.Column(scale=2): chatbot = gr.Chatbot(label="对话界面",height=600,value=get_initial_chat_display(),type='messages') msg = gr.Textbox(label="输入消息", placeholder="在这里输入你的消息...") with gr.Column(): btn_send = gr.Button("发送", variant="primary") btn_save = gr.Button("保存对话历史", variant="secondary") # with gr.Column(scale=1): # gr.Markdown("### 历史管理") # history_display = gr.Textbox(label="当前对话历史", interactive=False, lines=15) # btn_show_history = gr.Button("刷新历史显示") # btn_clear = gr.Button("清空历史") # status = gr.Textbox(label="状态", interactive=False) # # 事件处理 # btn_send.click(chat_with_ai, [msg, chatbot], [msg, chatbot]).then( # lambda: "", None, msg # ) btn_send.click(chat_with_ai, [msg, chatbot], [msg, chatbot]) msg.submit(chat_with_ai, [msg, chatbot], [msg, chatbot]) # 回车键触发 btn_save.click( fn=save_conversation_history, inputs=[], outputs=[] ) # btn_show_history.click(show_history, None, history_display) # btn_clear.click(clear_history, None, status).then( # lambda: "历史已清空", None, history_display # ) # # 初始化显示历史 # demo.load(show_history, None, history_display) if __name__ == "__main__": demo.launch()