lofyerandfactory-droid[bot] <138933559+factory-droid[bot]@users.noreply.github.com>
71db82393a
Co-authored-by: factory-droid[bot] <138933559+factory-droid[bot]@users.noreply.github.com>
120 lines
4.0 KiB
Python
120 lines
4.0 KiB
Python
#!/usr/bin/env python3
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"""
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完整报告生成脚本：Excel → 赋分 → 统计 → LLM并行生成 → HTML报告
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"""
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import sys
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import time
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import argparse
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import logging
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from pathlib import Path
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sys.path.insert(0, str(Path(__file__).parent.parent / "backend"))
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from app.engines.data_engine import DataEngine
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from app.engines.scoring_engine import ScoringEngine
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from app.engines.stats_engine import StatsEngine
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from app.engines.llm_engine import LLMEngine
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from app.engines.report_renderer import ReportRenderer
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logging.basicConfig(
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level=logging.INFO,
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format="%(asctime)s [%(levelname)s] %(message)s",
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datefmt="%H:%M:%S",
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)
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logger = logging.getLogger(__name__)
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def progress_callback(completed, total, segment_id):
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"""进度回调"""
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pct = completed / total * 100
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bar = "█" * int(pct / 5) + "░" * (20 - int(pct / 5))
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print(f"\r [{bar}] {pct:.0f}% ({completed}/{total}) {segment_id:<40}", end="", flush=True)
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def main():
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parser = argparse.ArgumentParser(description="生成学校课程实施监测报告")
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parser.add_argument("--school", type=str, default="延安中学", help="学校名称")
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parser.add_argument("--no-cache", action="store_true", help="不使用LLM缓存")
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parser.add_argument("--no-llm", action="store_true", help="跳过LLM生成（仅图表+数据）")
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parser.add_argument("--list-schools", action="store_true", help="列出所有可用学校")
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args = parser.parse_args()
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start_time = time.time()
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# ===== Step 1: 加载数据 =====
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print("=" * 70)
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print("📊 课程实施监测报告生成系统")
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print("=" * 70)
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logger.info("[1/5] 加载Excel数据...")
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data_engine = DataEngine()
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data_engine.load_all()
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if args.list_schools:
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print("\n可用学校:")
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for s in data_engine.schools:
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from app.config import SCHOOL_TYPE_MAP
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info = SCHOOL_TYPE_MAP.get(s, {})
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print(f" {s} ({info.get('type', '')})")
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return
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school = args.school
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if school not in data_engine.schools:
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print(f"\n❌ 学校 '{school}' 不在数据中。可用学校:")
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for s in data_engine.schools:
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print(f" - {s}")
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return
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print(f"\n🏫 目标学校: {school}")
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# ===== Step 2: 赋分 =====
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logger.info("[2/5] 全区赋分计算...")
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scoring_engine = ScoringEngine(data_engine)
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raw_scores = scoring_engine.score_all_schools()
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# ===== Step 3: 统计分析 =====
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logger.info("[3/5] PCA合成 + 标准化 + 统计分析...")
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stats_engine = StatsEngine()
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sub_scores = stats_engine.compute_dimension_scores(raw_scores)
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dim_scores = stats_engine.compute_dimension_aggregates(sub_scores)
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report_data = stats_engine.compute_school_report_data(school, sub_scores, dim_scores)
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# ===== Step 4: LLM生成 =====
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if args.no_llm:
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logger.info("[4/5] 跳过LLM生成（--no-llm模式）")
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llm_sections = {}
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else:
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logger.info("[4/5] LLM并行生成报告文字...")
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llm_engine = LLMEngine()
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llm_engine.set_progress_callback(progress_callback)
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llm_sections = llm_engine.generate_report_segments(
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report_data,
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use_cache=not args.no_cache,
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)
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print() # 换行
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# ===== Step 5: 渲染HTML =====
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logger.info("[5/5] 渲染HTML报告...")
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renderer = ReportRenderer()
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output_dir = Path(__file__).parent.parent / "output"
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output_dir.mkdir(parents=True, exist_ok=True)
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output_path = output_dir / f"{school}_报告.html"
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renderer.render_to_file(report_data, llm_sections, output_path)
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elapsed = time.time() - start_time
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print(f"\n{'=' * 70}")
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print(f"✅ 报告生成完成!")
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print(f" 学校: {school}")
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print(f" 总体得分: {report_data['overall']['score']}分 (区内第{report_data['overall']['rank_in_district']}名)")
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print(f" LLM段落: {len(llm_sections)}个")
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print(f" 耗时: {elapsed:.1f}秒")
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print(f" 输出: {output_path}")
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print(f"{'=' * 70}")
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if __name__ == "__main__":
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main()
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