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Diffstat (limited to 'recitalMachine2.py')
| -rw-r--r-- | recitalMachine2.py | 197 |
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diff --git a/recitalMachine2.py b/recitalMachine2.py new file mode 100644 index 0000000..8ac7aaf --- /dev/null +++ b/recitalMachine2.py @@ -0,0 +1,197 @@ +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()
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