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# -*- coding: utf-8 -*-
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import time
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from pathlib import Path
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from typing import Optional, Tuple
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from openai import OpenAI, APIError
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class ContentAnalyzer:
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"""课程内容分析器（带Emoji进度展示）"""
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def __init__(
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self,
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api_key: str = "sk-01d13a39e09844038322108ecdbd1bbc",
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base_url: str = "https://dashscope.aliyuncs.com/compatible-mode/v1",
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model: str = "deepseek-r1"
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):
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"""
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初始化分析器
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:param api_key: 阿里云API密钥
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:param base_url: API基础地址
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:param model: 使用的模型名称
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"""
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self.client = OpenAI(api_key=api_key, base_url=base_url)
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self.model = model
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def _show_progress(self, emoji: str, message: str, level: int = 1, delay: float = 0.3):
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"""进度展示方法
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:param emoji: 表情符号
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:param message: 进度信息
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:param level: 缩进层级（0-无缩进，1-一级缩进）
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:param delay: 展示后的延迟时间
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"""
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indent = " " * level
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progress_line = f"{indent}{emoji} {message}"
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print(progress_line)
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time.sleep(delay) # 增加进度展示的节奏感
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def analyze_content(
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self,
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content: str,
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prompt_template: str = "帮我梳理：这节课分了几个部分，每部分的名称和开始的时间是多少：{}"
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) -> Tuple[bool, str]:
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"""
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分析课程内容结构
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:param content: 需要分析的文本内容
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:param prompt_template: 提示词模板
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:return: (成功标志, 分析结果或错误信息)
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"""
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try:
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# 构建提示词
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self._show_progress("📝", "正在构建提示词...", level=1)
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full_prompt = prompt_template.format(content)
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# 发送API请求
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self._show_progress("📡", "正在发送API请求...", level=1)
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start_time = time.time()
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completion = self.client.chat.completions.create(
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model=self.model,
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messages=[{'role': 'user', 'content': full_prompt}]
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)
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cost_time = time.time() - start_time
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# 处理响应
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self._show_progress("✅", f"收到API响应（耗时{cost_time:.1f}s）", level=1)
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if not completion.choices:
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self._show_progress("❌", "响应内容为空", level=2)
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return False, "API响应中未包含有效结果"
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# 解析结果
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self._show_progress("🔍", "正在解析结果内容...", level=1)
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return True, completion.choices[0].message.content
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except APIError as e:
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self._show_progress("🚨", f"API异常: {str(e)}", level=2)
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return False, f"API调用失败: {str(e)}"
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except Exception as e:
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self._show_progress("💥", f"未处理的异常: {str(e)}", level=2)
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return False, f"未处理的异常: {str(e)}"
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def analyze_file(
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self,
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file_path: Path,
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output_path: Optional[Path] = None,
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encoding: str = 'utf-8'
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) -> Tuple[bool, str]:
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"""
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从文件读取内容并分析
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:param file_path: 输入文件路径
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:param output_path: 输出文件路径（可选）
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:param encoding: 文件编码格式
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:return: (成功标志, 最终结果或错误信息)
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"""
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try:
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self._show_progress("📂", f"开始处理文件: {file_path}", level=0)
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# 检查文件存在性
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self._show_progress("🔎", "检查文件是否存在...", level=1)
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if not file_path.exists():
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self._show_progress("❌", "文件不存在", level=2)
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return False, f"文件不存在: {file_path}"
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# 读取文件内容
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self._show_progress("📖", "正在读取文件内容...", level=1)
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try:
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content = file_path.read_text(encoding=encoding)
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except UnicodeDecodeError:
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self._show_progress("🔠", f"解码失败（当前编码: {encoding}）", level=2)
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return False, f"文件解码失败，请尝试使用正确的编码格式"
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# 分析内容
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self._show_progress("🧠", "开始分析内容...", level=1)
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success, result = self.analyze_content(content)
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if not success:
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self._show_progress("⚠️", "内容分析失败", level=2)
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return False, result
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# 保存结果
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if output_path:
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self._show_progress("💾", f"正在保存结果到: {output_path}", level=1)
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try:
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output_path.parent.mkdir(parents=True, exist_ok=True)
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output_path.write_text(result, encoding=encoding)
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self._show_progress("✅", "文件保存成功", level=2)
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except IOError as e:
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self._show_progress("❌", f"保存失败: {str(e)}", level=2)
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return False, f"结果保存失败: {str(e)}"
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self._show_progress("🎉", "文件处理完成", level=0)
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return True, result
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except Exception as e:
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self._show_progress("💣", f"意外错误: {str(e)}", level=2)
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return False, f"文件处理异常: {str(e)}"
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def show_banner():
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"""显示启动横幅"""
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print("\n" + "=" * 50)
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print("🌟 课程内容智能分析系统 🌟".center(50))
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print("=" * 50 + "\n")
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def analyzer_action(input_file: str, output_file: Optional[str] = None):
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"""执行分析流程
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:param input_file: 输入文件路径
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:param output_file: 输出文件路径（可选）
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"""
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show_banner()
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analyzer = ContentAnalyzer()
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input_path = Path(input_file)
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output_path = Path(output_file) if output_file else None
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success, result = analyzer.analyze_file(input_path, output_path)
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print("\n" + "=" * 50)
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if success:
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print("✅ 最终分析结果：".center(50))
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print("-" * 50)
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print(result)
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print("-" * 50)
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if output_path:
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print(f"结果已保存至：{output_path.resolve()}")
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else:
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print(f"❌ 分析失败：{result}")
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print("=" * 50 + "\n")
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if __name__ == "__main__":
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# 示例配置
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input_file = r"D:\dsWork\QingLong\AI\音频文本.txt"
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output_file = r"D:\dsWork\QingLong\AI\分析结果.txt"
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# 执行分析
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analyzer_action(input_file, output_file) |
