182 lines
6.7 KiB
Python
182 lines
6.7 KiB
Python
import os
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import re
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from openai import OpenAI
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# 加载LLM配置的函数
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class LLMConfig:
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def __init__(self, api_key, model_name, base_url):
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self.api_key = api_key
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self.model_name = model_name
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self.base_url = base_url
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def load_llm_config():
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"""
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加载LLM配置
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返回:
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LLMConfig: 包含API密钥、模型名称和基础URL的配置对象
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"""
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try:
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from Config.Config import ALY_LLM_API_KEY, ALY_LLM_MODEL_NAME, ALY_LLM_BASE_URL
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return LLMConfig(
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api_key=ALY_LLM_API_KEY,
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model_name=ALY_LLM_MODEL_NAME,
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base_url=ALY_LLM_BASE_URL
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)
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except Exception as e:
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print(f"加载配置时出错: {str(e)}")
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return None
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SYS_PROMPT = """
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你是一位资深 Manim 动画工程师,专注于教育场景。请遵循以下规范:
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1. 仅输出 Python 代码,不解释思路。
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2. 使用 Manim 社区版 v0.18.0 语法。
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3. 严格按提示的步骤进行编码,不要发挥、扩展。
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4. 所有公式用 MathTex,文字用 Text(更好地支持中文),颜色统一用 Manim 内置调色板。
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5. 关键坐标用常量定义在类顶部,例如 LEFT_MARGIN = 3.2。
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6. 每行不超过 88 字符,函数名用 snake_case。
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7. 动画时长 3-8 秒 / 步,默认 ease-in-out。
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8. 请严格按照以下模板:坐标轴范围、颜色、字体统一使用 Manim 社区版默认主题。
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9. 代码的最后,参考下面的代码添加相应内容:
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if __name__ == "__main__":
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# 导入必要的模块
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import sys
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import os
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from manim import *
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from manim.utils.rate_functions import ease_in_out_sine
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# 设置 UTF-8 编码
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os.environ['PYTHONIOENCODING'] = 'utf-8'
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sys.stdout.reconfigure(encoding='utf-8')
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sys.stderr.reconfigure(encoding='utf-8')
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# 全局指定 ctex 模板(一次性)
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config.tex_template = TexTemplateLibrary.ctex
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# 设置输出格式为MP4
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config.output_format = "mp4"
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# 设置输出目录
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config.media_dir = "./output/"
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# 使用临时配置渲染场景(配置只在with块内有效)
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with tempconfig({{
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"quality": "high_quality",
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"background_color": BLACK,
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"preview": {Preview},
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"pixel_height": 1080,
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"pixel_width": 1920,
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"frame_rate": 30
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}}):
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# 实例化场景类
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scene = Skeleton()
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# 执行渲染流程(包含文件生成和预览)
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scene.render()
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10. 【输出要求】
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(1). 完整可运行 Python 脚本
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(2). 包含 `if __name__ == "__main__":` 可直接运行。
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(3). 代码内只出现中文注释,方便二次修改。
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本任务的具体需求如下:
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{XuQiu}
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"""
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def generate_code_with_llm(prompt, save_path, config=None):
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"""
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调用大模型生成代码并保存
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参数:
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prompt: 提示词
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save_path: 代码保存路径
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config: 可选的配置对象,如果未提供则自动加载
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返回:
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bool: 是否成功保存代码
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"""
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try:
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# 如果未提供配置,则加载默认配置
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if config is None:
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config = load_llm_config()
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if config is None:
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print("无法加载配置,生成代码失败")
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return False
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# 初始化OpenAI客户端
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client = OpenAI(
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api_key=config.api_key,
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base_url=config.base_url
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)
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reasoning_content = "" # 定义完整思考过程
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answer_content = "" # 定义完整回复
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is_answering = False # 判断是否结束思考过程并开始回复
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# 创建聊天完成请求
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completion = client.chat.completions.create(
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model=config.model_name,
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messages=[
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{
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"role": "user",
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"content": [
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{"type": "text",
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"text": prompt},
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],
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},
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],
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stream=True,
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)
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print("\n" + "=" * 20 + "思考过程" + "=" * 20 + "\n")
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for chunk in completion:
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# 如果chunk.choices为空,则打印usage
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if not chunk.choices:
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print("\nUsage:")
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print(chunk.usage)
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else:
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delta = chunk.choices[0].delta
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# 打印思考过程
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if hasattr(delta, 'reasoning_content') and delta.reasoning_content != None:
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print(delta.reasoning_content, end='', flush=True)
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reasoning_content += delta.reasoning_content
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else:
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# 开始回复
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if delta.content != "" and is_answering is False:
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print("\n" + "=" * 20 + "完整回复" + "=" * 20 + "\n")
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is_answering = True
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# 打印回复过程
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print(delta.content, end='', flush=True)
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answer_content += delta.content
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# 处理并保存生成的代码
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return save_code_to_file(answer_content, save_path)
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except Exception as e:
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print(f"调用大模型时出错: {str(e)}")
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return False
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def save_code_to_file(content, file_path):
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try:
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# 提取Python代码块
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code_match = re.search(r'```python(.*?)```', content, re.DOTALL)
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if code_match:
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code_content = code_match.group(1).strip()
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else:
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# 如果没有代码块,尝试提取所有内容
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code_content = content.strip()
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# 替换全角符号为半角
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code_content = code_content.replace(',', ',').replace('。', '.').replace(';', ';')
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code_content = code_content.replace('(', '(').replace(')', ')').replace('【', '[').replace('】', ']')
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# 添加UTF-8编码声明
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code_content = f"# -*- coding: utf-8 -*-.py\n{code_content}"
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# 确保目录存在
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os.makedirs(os.path.dirname(file_path), exist_ok=True)
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# 写入文件
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with open(file_path, 'w', encoding='utf-8') as f:
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f.write(code_content)
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print(f"代码已成功保存到: {file_path}")
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return True
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except Exception as e:
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print(f"保存代码时出错: {str(e)}")
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return False |