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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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