429 lines
16 KiB
Python
429 lines
16 KiB
Python
import os
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import time
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import pythoncom
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import pandas as pd
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from loguru import logger
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from pptx import Presentation
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from rich.console import Console
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import traceback
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import comtypes.client
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from config.config import load_config
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from utils.agent_utils import generate_comment
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from utils.file_utils import check_file_exists
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from utils.font_utils import install_fonts_from_directory
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from utils.image_utils import find_image_path
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from utils.zodiac_utils import calculate_zodiac
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from utils.growt_utils import (
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replace_one_page,
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replace_two_page,
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replace_three_page,
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replace_four_page,
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replace_five_page,
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)
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# 如果你之前没有全局定义 console，这里定义一个
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console = Console()
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# ==========================================
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# 1. 配置区域 (Configuration)
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# ==========================================
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config = load_config("config.toml")
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# ==========================================
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# 1. 生成模板(根据names.xlsx文件生成名字图片文件夹)
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# ==========================================
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def generate_template():
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try:
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# 2. 读取数据
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df = pd.read_excel(config["excel_file"], sheet_name="Sheet1")
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# --- 修改点开始 ---
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# 直接读取 "姓名" 这一列，不使用列表包裹列名，这样得到的是一维数据
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datas = df["姓名"].values.tolist()
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# --- 修改点结束 ---
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logger.info(f"开始生成学生模版文件，共 {len(datas)} 位学生...")
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# 3. 循环处理
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# 此时 name 就是字符串 '张三'，而不是列表 ['张三']
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for i, name in enumerate(datas):
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logger.info(f"[{i + 1}/{len(datas)}] 正在生成: {name}")
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# 确保 name 是字符串且去除了空格 (增加健壮性)
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name = str(name).strip()
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student_folder = os.path.join(config["image_folder"], name)
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if os.path.exists(student_folder):
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logger.info(f"学生图片文件夹已存在 {student_folder}")
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else:
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logger.info(f"正在生成学生图片文件夹 {student_folder}")
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os.makedirs(student_folder, exist_ok=True)
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except Exception as e:
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logger.error(f"程序运行出错: {str(e)}")
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# 打印详细报错位置，方便调试
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logger.error(traceback.format_exc())
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# ==========================================
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# 2. 生成评语(根据names.xlsx文件生成评价)
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# ==========================================
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def generate_comment_all():
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try:
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# 1. 读取数据
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excel_path = config["excel_file"]
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df = pd.read_excel(excel_path, sheet_name="Sheet1")
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# 检查是否存在"评价"列，不存在则新建（防止报错）
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if "评价" not in df.columns:
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df["评价"] = ""
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# 获取学生数据行数
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total_count = len(df)
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logger.info(f"开始生成学生评语，共 {total_count} 位学生...")
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# 强制将“评价”列转换为 object 类型
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df["评价"] = df["评价"].astype("object")
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# --- 遍历 DataFrame 的索引 (index) ---
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# 这样我们可以通过索引 i 精准地把数据写回某一行
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for i in df.index:
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name = df.at[i, "姓名"] # 获取当前行的姓名
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sex = df.at[i, "性别"]
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if pd.isna(sex):
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sex = "男"
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else:
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sex = str(sex).strip()
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# 健壮性处理
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if pd.isna(name):
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continue # 跳过空行
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name = str(name).strip()
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# 获取当前行的特征（如果Excel里有“特征”这一列就读，没有就用默认值）
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# 假设Excel里有一列叫 "表现特征"，如果没有则用默认的 "有礼貌..."
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traits = (
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df.at[i, "表现特征"]
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if "表现特征" in df.columns and not pd.isna(df.at[i, "表现特征"])
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else "有礼貌、守纪律"
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)
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# 优化：如果“评价”列已经有内容了，跳过不生成（节省API费用）
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current_comment = df.at[i, "评价"]
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if not pd.isna(current_comment) and str(current_comment).strip() != "":
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logger.info(f"[{i + 1}/{total_count}] {name} 已有评语，跳过。")
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continue
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logger.info(f"[{i + 1}/{total_count}] 正在生成评价: {name}")
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try:
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# 调用AI大模型生成内容
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generated_text = generate_comment(
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name, config["age_group"], traits, sex
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)
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if generated_text:
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# 赋值
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df.at[i, "评价"] = str(generated_text).strip()
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else:
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df.at[i, "评价"] = "" # 防空处理
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logger.success(f"学生：{name}，评语生成完毕")
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# 可选：每生成 5 个就保存一次
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if (i + 1) % 5 == 0:
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df.to_excel(excel_path, index=False)
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logger.success("✅ 阶段性保存成功")
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# 避免触发API速率限制
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time.sleep(1)
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except Exception as e:
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logger.error(f"学生：{name}，生成评语出错: {str(e)}")
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# --- 循环结束后最终保存文件 ---
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# index=False 表示不把 pandas 的索引 (0,1,2...) 写到 Excel 第一列
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df.to_excel(excel_path, index=False)
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logger.success(f"所有评语已生成并写入文件：{excel_path}")
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except PermissionError:
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logger.error(f"保存失败！请先关闭 Excel 文件：{config['excel_file']}")
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except Exception as e:
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logger.error(f"程序运行出错: {str(e)}")
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logger.error(traceback.format_exc())
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# ==========================================
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# 3. 生成成长报告(根据names.xlsx文件生成)
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# ==========================================
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def generate_report():
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# 1. 资源准备
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if install_fonts_from_directory(config["fonts_dir"]):
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logger.info("等待系统识别新安装的字体...")
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time.sleep(2)
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os.makedirs(config["output_folder"], exist_ok=True)
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# 检查模版文件是否存在
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if not os.path.exists(config["source_file"]):
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logger.info(f"错误: 找不到模版文件 {config["source_file"]}")
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return
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# 检查数据文件是否存在
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if not os.path.exists(config["excel_file"]):
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logger.info(f"错误: 找不到数据文件 {config['excel_file']}")
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return
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try:
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# 2. 读取数据
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df = pd.read_excel(config["excel_file"], sheet_name="Sheet1")
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# 确保列名对应
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columns = [
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"姓名",
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"英文名",
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"性别",
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"生日",
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"属相",
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"我的好朋友",
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"我的爱好",
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"喜欢的游戏",
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"喜欢吃的食物",
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"评价",
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]
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datas = df[columns].values.tolist()
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teacher_names_str = " ".join(config["teachers"])
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logger.info(f"开始处理，共 {len(datas)} 位学生...")
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# 3. 循环处理
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for i, row_data in enumerate(datas):
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# 解包数据
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(
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name,
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english_name,
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sex,
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birthday,
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zodiac,
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friend,
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hobby,
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game,
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food,
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comments,
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) = row_data
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logger.info(f"[{i + 1}/{len(datas)}] 正在生成: {name}")
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# 每次循环重新加载模版
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prs = Presentation(config["source_file"])
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# --- 页面 1 ---
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replace_one_page(prs, name, config["class_name"])
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# --- 页面 2 ---
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replace_two_page(prs, comments, teacher_names_str)
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# --- 页面 3 ---
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student_image_folder = os.path.join(config["image_folder"], name)
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logger.info(f"学生：{name}，图片文件夹: {student_image_folder}")
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# 判断学生图片文件夹是否存在
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if not os.path.exists(student_image_folder):
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logger.warning(
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f"⚠️ 警告: 学生：{name}，学生图片文件夹不存在 {student_image_folder}"
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)
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continue
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# 检查姓名是否为空
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if not name:
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logger.error(f"⚠️ 警告: 学生：{name}，姓名为空，跳过")
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break
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# 构造学生信息字典
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student_info_dict = {
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"name": name,
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"english_name": english_name if pd.notna(english_name) else "",
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"sex": sex if pd.notna(sex) else "男",
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"birthday": (
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birthday.strftime("%Y-%m-%d") if pd.notna(birthday) else ""
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),
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"zodiac": zodiac if pd.notna(zodiac) else "",
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"friend": friend if pd.notna(friend) else "",
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"hobby": hobby if pd.notna(hobby) else "",
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"game": game if pd.notna(game) else "",
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"food": food if pd.notna(food) else "",
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}
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# 获取学生个人照片路径
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me_image_path = find_image_path(student_image_folder, "me")
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# 检查学生图片是否存在，若不存在则跳过
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if check_file_exists(me_image_path):
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replace_three_page(prs, student_info_dict, me_image_path)
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else:
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logger.warning(f"⚠️ 警告: 学生图片文件不存在 {me_image_path}")
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# --- 页面 4 ---
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class_image_path = find_image_path(
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config["image_folder"], config["class_name"]
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)
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# 添加检查班级图片是否存在，若不存在则跳过
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if check_file_exists(class_image_path):
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replace_four_page(prs, class_image_path)
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else:
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logger.warning(f"⚠️ 警告: 班级图片文件不存在 {class_image_path}")
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# --- 页面 5 ---
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if os.path.exists(student_image_folder):
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img1_path = find_image_path(student_image_folder, "1")
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img2_path = find_image_path(student_image_folder, "2")
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# 逻辑优化：
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# 情况A: 两张都找到了 -> 正常插入
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if img1_path and img2_path:
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replace_five_page(prs, img1_path, img2_path)
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# 情况B: 只找到了 1 -> 两张图都用 1 (避免报错)
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elif img1_path and not img2_path:
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replace_five_page(prs, img1_path, img1_path)
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# 情况C: 一张都没找到
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else:
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logger.warning(
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f"⚠️ 警告: {name} 缺少作品照片 (1.jpg/png 或 2.jpg/png)[/]"
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)
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else:
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logger.warning(f"错误: 学生图片文件夹不存在 {student_image_folder}")
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# --- 保存文件 ---
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file_ext = os.path.splitext(config["source_file"])[1]
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safe_name = str(name).strip()
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new_filename = f"{config['class_name']} {safe_name} 幼儿成长报告{file_ext}"
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output_path = os.path.join(config["output_folder"], new_filename)
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try:
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prs.save(output_path)
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logger.success(f"学生：{name}，保存成功: {new_filename}")
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except PermissionError:
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logger.error(
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f"保存失败: 文件 {new_filename} 可能已被打开，请关闭后重试。"
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)
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logger.success("所有报告生成完毕！")
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except Exception as e:
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logger.error(f"程序运行出错: {str(e)}")
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logger.error(traceback.format_exc())
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# ==========================================
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# 5. 转换格式(根据names.xlsx文件生成PPT转PDF)
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# ==========================================
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def batch_convert_folder(folder_path):
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"""
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【推荐】批量转换文件夹下的所有 PPT (只启动一次 PowerPoint，速度快)
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已修复多线程 CoInitialize 报错，并适配 GUI 日志
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"""
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# 子线程初始化 COM 组件
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pythoncom.CoInitialize()
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try:
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folder_path = os.path.abspath(folder_path)
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if not os.path.exists(folder_path):
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logger.error(f"文件夹不存在: {folder_path}")
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return
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# 获取所有 ppt/pptx 文件
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files = [
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f for f in os.listdir(folder_path) if f.lower().endswith((".ppt", ".pptx"))
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]
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if not files:
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logger.warning("没有找到 PPT 文件")
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return
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logger.info(f"发现 {len(files)} 个文件，准备开始转换...")
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powerpoint = None
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try:
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# 1. 启动应用 (只启动一次)
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powerpoint = comtypes.client.CreateObject("PowerPoint.Application")
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# 【建议】在后台线程运行时，有时设置为不可见更稳定，
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# 但如果遇到转换卡死，可以尝试去掉下面这行的注释，让它显示出来
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# powerpoint.Visible = 1
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for filename in files:
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ppt_path = os.path.join(folder_path, filename)
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pdf_path = os.path.splitext(ppt_path)[0] + ".pdf"
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# 如果 PDF 已存在，可以选择跳过
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if os.path.exists(pdf_path):
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logger.info(f"[跳过] 已存在: {filename}")
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continue
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logger.info(f"正在转换: {filename} ...")
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try:
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# 打开 -> 另存为 -> 关闭
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deck = powerpoint.Presentations.Open(ppt_path)
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deck.SaveAs(pdf_path, 32) # 32 代表 PDF 格式
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deck.Close()
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except Exception as e:
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logger.error(f"文件 {filename} 转换出错: {e}")
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except Exception as e:
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logger.error(f"PowerPoint 进程启动出错: {e}")
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finally:
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# 2. 退出应用
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if powerpoint:
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try:
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powerpoint.Quit()
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except:
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pass
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logger.success("PowerPoint 已关闭，批量转换完成。")
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except Exception as e:
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logger.error(f"未知错误: {e}")
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finally:
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# 【核心修复 2】释放资源
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pythoncom.CoUninitialize()
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# ==========================================
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# 5. 生成属相(根据names.xlsx文件生成属相)
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# ==========================================
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def generate_zodiac():
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try:
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# 1. 读取数据
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excel_path = config["excel_file"]
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# sheet_name 根据实际情况修改，如果不确定可以用 sheet_name=0 读取第一个
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df = pd.read_excel(excel_path, sheet_name="Sheet1")
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# 2. 检查必要的列
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date_column = "生日"
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target_column = "属相"
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if date_column not in df.columns:
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logger.error(f"Excel中找不到列名：【{date_column}】，请检查表头。")
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return
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# 检查是否存在"属相"列，不存在则新建
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if target_column not in df.columns:
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df[target_column] = ""
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# --- 获取总行数，用于日志 ---
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total_count = len(df)
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logger.info(f"开始生成学生属相，共 {total_count} 位学生...")
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# 3. 数据清洗与计算
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temp_dates = pd.to_datetime(df[date_column], errors="coerce")
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df[target_column] = temp_dates.apply(calculate_zodiac)
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# 5. 保存结果
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save_path = excel_path
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try:
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df.to_excel(save_path, index=False)
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logger.success(f"所有属相已更新并写入文件：{save_path}")
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logger.warning(f"请检查文件 {save_path} 修改日期格式。")
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except PermissionError:
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logger.error(f"保存失败！请先关闭 Excel 文件：{save_path}")
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except FileNotFoundError:
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logger.error(f"找不到文件 {config.get('excel_file')}")
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except Exception as e:
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logger.error(f"程序运行出错: {str(e)}")
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logger.error(traceback.format_exc())
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