feat: AI识别生成带排版HTML题目，试卷PDF用HTML渲染（含图形表格）
- OCR prompt升级：输出question_html字段（CSS绘制图形、表格、数学符号） - Mistake模型加question_html字段 - 新增html_pdf_service.py用xhtml2pdf渲染HTML为A4 PDF - exam路由优先用HTML PDF服务，回退到reportlab - 前端类型加question_html
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@ -33,6 +33,7 @@ class Mistake(Base):
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grade_level: Mapped[str] = mapped_column(String(20), nullable=True)
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question_text: Mapped[str] = mapped_column(Text, nullable=False)
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question_html: Mapped[str] = mapped_column(Text, nullable=True) # AI-generated formatted HTML
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correct_answer: Mapped[str] = mapped_column(Text, nullable=True)
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student_answer: Mapped[str] = mapped_column(Text, nullable=True)
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error_analysis: Mapped[str] = mapped_column(Text, nullable=True)
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@ -15,6 +15,7 @@ from app.models.mistake import Mistake
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from app.models.user import User
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from app.routers.dependencies import get_current_user
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from app.schemas.exam import ExamGenerateRequest, ExamGenerateResponse
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from app.services.html_pdf_service import generate_html_exam_pdf, HAS_HTML2PDF
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from app.services.pdf_service import generate_exam_pdf
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router = APIRouter(prefix="/exam", tags=["试卷"])
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@ -43,10 +44,11 @@ async def generate_exam(
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if not mistakes:
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raise HTTPException(status_code=400, detail="没有符合条件的错题")
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# Prepare mistake dicts for PDF (include image paths)
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# Prepare mistake dicts for PDF (include image paths + AI HTML)
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mistake_dicts = [
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{
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"question_text": m.question_text,
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"question_html": m.question_html,
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"question_type": m.question_type,
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"correct_answer": m.correct_answer,
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"student_answer": m.student_answer,
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@ -60,20 +62,30 @@ async def generate_exam(
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for m in mistakes
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]
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# Generate PDF
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# Generate PDF — prefer HTML-based (weasyprint) for rich formatting
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filename = f"exam_{uuid.uuid4().hex[:12]}.pdf"
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user_dir = os.path.join(settings.UPLOAD_DIR, str(user.id))
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os.makedirs(user_dir, exist_ok=True)
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output_path = os.path.join(user_dir, filename)
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generate_exam_pdf(
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title=data.title,
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student_name=user.display_name or user.username,
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grade=user.grade or "",
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mistakes=mistake_dicts,
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include_answers=data.include_answers,
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output_path=output_path,
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)
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if HAS_HTML2PDF:
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generate_html_exam_pdf(
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title=data.title,
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student_name=user.display_name or user.username,
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grade=user.grade or "",
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mistakes=mistake_dicts,
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include_answers=data.include_answers,
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output_path=output_path,
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)
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else:
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generate_exam_pdf(
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title=data.title,
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student_name=user.display_name or user.username,
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grade=user.grade or "",
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mistakes=mistake_dicts,
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include_answers=data.include_answers,
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output_path=output_path,
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)
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# Save record
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exam = ExamPaper(
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@ -62,7 +62,9 @@ async def ocr_mistake(
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ocr_result = all_ocr_results[0]
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if len(all_ocr_results) > 1:
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combined_text = "\n".join(r.get("question_text", "") for r in all_ocr_results if r.get("question_text"))
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combined_html = "<br>".join(r.get("question_html", "") for r in all_ocr_results if r.get("question_html"))
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ocr_result["question_text"] = combined_text
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ocr_result["question_html"] = combined_html
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# Create mistake record
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mistake = Mistake(
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@ -70,6 +72,7 @@ async def ocr_mistake(
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subject=ocr_result.get("subject") or subject or "数学",
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question_type=ocr_result.get("question_type"),
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question_text=ocr_result.get("question_text") or "识别失败，请手动输入",
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question_html=ocr_result.get("question_html"),
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correct_answer=ocr_result.get("correct_answer"),
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student_answer=ocr_result.get("student_answer"),
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error_analysis=ocr_result.get("error_analysis"),
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@ -36,6 +36,7 @@ class MistakeResponse(BaseModel):
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question_type: Optional[str]
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grade_level: Optional[str]
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question_text: str
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question_html: Optional[str] = None
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correct_answer: Optional[str]
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student_answer: Optional[str]
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error_analysis: Optional[str]
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@ -0,0 +1,249 @@
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"""HTML-based PDF generation using xhtml2pdf + AI-generated question HTML."""
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from __future__ import annotations
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import os
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import hashlib
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from datetime import date
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# Fix reportlab 4.x + Python 3.8 compatibility (must be before xhtml2pdf import)
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_original_md5 = hashlib.md5
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def _patched_md5(*args, **kwargs):
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kwargs.pop("usedforsecurity", None)
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return _original_md5(*args, **kwargs)
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hashlib.md5 = _patched_md5
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try:
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from xhtml2pdf import pisa
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HAS_HTML2PDF = True
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except ImportError:
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HAS_HTML2PDF = False
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from app.config import settings
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# Chinese font stack
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FONT_FAMILY = '"Noto Sans SC", "Microsoft YaHei", "PingFang SC", sans-serif'
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HTML_TEMPLATE = """<!DOCTYPE html>
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<html lang="zh-CN">
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<head>
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<meta charset="utf-8">
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<style>
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@page {{
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size: A4;
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margin: 20mm;
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}}
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* {{ margin: 0; padding: 0; box-sizing: border-box; }}
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body {{
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font-family: {font};
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font-size: 14px;
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color: #1e293b;
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line-height: 1.6;
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}}
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.header {{
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text-align: center;
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border-bottom: 2px solid #333;
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padding-bottom: 12px;
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margin-bottom: 20px;
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}}
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.header h1 {{
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font-size: 22px;
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margin-bottom: 4px;
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}}
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.header .subtitle {{
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font-size: 12px;
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color: #666;
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}}
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.section-title {{
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font-size: 16px;
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font-weight: bold;
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margin-top: 24px;
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margin-bottom: 12px;
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padding: 6px 0;
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border-bottom: 1px solid #ddd;
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}}
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.question {{
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margin-bottom: 24px;
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page-break-inside: avoid;
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}}
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.question .q-num {{
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font-weight: bold;
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margin-bottom: 6px;
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}}
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.question .q-content {{
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padding: 8px 0;
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}}
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.question .q-content img {{
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max-width: 60%;
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height: auto;
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display: block;
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margin: 8px auto;
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}}
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.question .q-content table {{
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border-collapse: collapse;
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margin: 8px 0;
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width: auto;
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}}
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.question .q-content table td,
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.question .q-content table th {{
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border: 1px solid #333;
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padding: 4px 8px;
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text-align: center;
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}}
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.question .q-image {{
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text-align: center;
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margin: 8px 0;
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}}
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.question .q-image img {{
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max-width: 50%;
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height: auto;
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border: 1px solid #eee;
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}}
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.answer-section {{
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page-break-before: always;
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}}
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.answer-section h2 {{
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font-size: 18px;
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text-align: center;
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border-bottom: 2px solid #333;
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padding-bottom: 8px;
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margin-bottom: 16px;
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}}
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.answer-item {{
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margin-bottom: 8px;
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font-size: 13px;
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}}
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.answer-item .analysis {{
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color: #666;
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font-size: 12px;
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}}
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/* AI-generated question HTML overrides */
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.q-content svg {{ max-width: 100%; }}
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.q-content .diagram {{ text-align: center; margin: 8px 0; }}
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</style>
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</head>
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<body>
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<div class="header">
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<h1>{title}</h1>
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<div class="subtitle">{student_name} · {grade} · {today}</div>
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</div>
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{questions_html}
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{answers_html}
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</body>
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</html>"""
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def _get_image_html(m: dict) -> str:
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"""Generate <img> tags for a mistake's images."""
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upload_dir = m.get("upload_dir", "")
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urls = []
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raw = m.get("image_urls")
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if raw and isinstance(raw, list):
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urls.extend(raw)
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elif m.get("image_url"):
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urls.append(m["image_url"])
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if not urls:
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return ""
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imgs = []
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for url in urls:
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if not url:
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continue
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# Convert /uploads/2/filename.jpg to file:// absolute path
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rel = url.lstrip("/")
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full = os.path.normpath(os.path.join(upload_dir, "..", rel))
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if os.path.exists(full):
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imgs.append(f'<div class="q-image"><img src="file:///{full.replace(os.sep, "/")}" /></div>')
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return "\n".join(imgs)
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def generate_html_exam_pdf(
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title: str,
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student_name: str,
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grade: str,
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mistakes: list[dict],
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include_answers: bool,
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output_path: str,
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) -> str:
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"""Generate A4 PDF using AI-generated HTML + xhtml2pdf."""
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if not HAS_HTML2PDF:
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raise RuntimeError("xhtml2pdf not installed. Run: pip install xhtml2pdf")
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section_labels = ["一", "二", "三", "四", "五", "六", "七", "八", "九", "十"]
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# Group by question type
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groups: dict[str, list[dict]] = {}
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for m in mistakes:
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qtype = m.get("question_type") or "其他"
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groups.setdefault(qtype, []).append(m)
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questions_html = ""
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answers_html = ""
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q_num = 1
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for idx, (qtype, questions) in enumerate(groups.items()):
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label = section_labels[idx] if idx < len(section_labels) else str(idx + 1)
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questions_html += f'<div class="section-title">{label}、{qtype}（共{len(questions)}题）</div>\n'
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for q in questions:
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# Use AI-generated HTML if available, otherwise plain text
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q_html = q.get("question_html", "")
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if not q_html:
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q_html = f'<p>{q.get("question_text", "")}</p>'
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# Add original images below the question
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img_html = _get_image_html(q)
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student_ans = q.get("student_answer", "")
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ans_note = f' <span style="color:#ef4444;font-size:12px;">（你的答案：{student_ans}）</span>' if student_ans else ""
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questions_html += f'''<div class="question">
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<div class="q-num">{q_num}.{ans_note}</div>
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<div class="q-content">{q_html}</div>
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{img_html}
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</div>
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'''
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# Answer
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if include_answers:
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correct = q.get("correct_answer") or "—"
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analysis = q.get("error_analysis") or ""
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analysis_html = f' <span class="analysis">（{analysis}）</span>' if analysis else ""
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answers_html += f'<div class="answer-item">{q_num}. {correct}{analysis_html}</div>\n'
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q_num += 1
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# Build answers section
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answer_section = ""
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if include_answers and answers_html:
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answer_section = f'''<div class="answer-section">
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<h2>参考答案</h2>
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{answers_html}
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</div>'''
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full_html = HTML_TEMPLATE.format(
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font=FONT_FAMILY,
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title=title,
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student_name=student_name,
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grade=grade,
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today=date.today().strftime("%Y年%m月%d日"),
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questions_html=questions_html,
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answers_html=answer_section,
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)
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# Write HTML to temp file, then convert to PDF
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html_path = output_path + ".html"
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with open(html_path, "w", encoding="utf-8") as f:
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f.write(full_html)
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with open(output_path, "wb") as pdf_file:
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status = pisa.CreatePDF(full_html, dest=pdf_file, encoding="utf-8")
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# Cleanup temp HTML
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if os.path.exists(html_path):
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os.remove(html_path)
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if status.err:
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raise RuntimeError(f"PDF generation failed with {status.err} errors")
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return output_path
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@ -6,23 +6,26 @@ import httpx
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from app.config import settings
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SYSTEM_PROMPT = """你是一个专门为小学生错题识别设计的AI助手。
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你需要分析上传的错题图片，提取以下信息：
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SYSTEM_PROMPT = """你是一个专门为小学生错题识别和排版设计的AI助手。
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你需要分析上传的错题图片，做两件事：
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1. **题目内容**：完整准确地提取题目文字
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2. **题型**：判断题目类型
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- 数学：填空题、选择题、计算题、应用题、判断题
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- 语文：拼音题、字词题、句子题、阅读理解、作文
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- 英语：单词题、语法题、翻译题、阅读理解
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3. **学生答案**：如果能看到学生写的错误答案
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4. **正确答案**：如果能判断出正确答案
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5. **可能的错误原因**：根据错误类型推断
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6. **知识点**：归类到具体知识点
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## 任务一：提取结构化信息
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提取：科目、题型、题目文字、学生错误答案、正确答案、错误原因、知识点、置信度。
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注意事项：
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## 任务二：生成排版HTML
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把题目用精美的HTML排版出来，要求：
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- 用HTML+CSS绘制题目中出现的图形（几何图形用CSS border/transform，线段图用div背景色，表格用table标签）
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- 数学公式用Unicode符号（× ÷ ≥ ≤ √ ² ³ ½ ¼）或HTML标签
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- 选择题的选项用整齐排列
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- 填空题的空位用下划线或空格
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- 适合A4纸打印，宽度800px以内
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- 字体大小：题目16px，选项14px，说明12px
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- 如果有图形/图表/示意图，必须用HTML+CSS重新绘制出来，不能只写文字描述
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- 如果有表格，用标准table标签，带边框
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注意：
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- 手写体和印刷体都要能识别
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- 数学公式要准确提取（使用标准数学符号）
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- 如果图片模糊或不确定，confidence设为较低值
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- 如果图片模糊，confidence设为较低值
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- 必须返回严格的JSON格式，不要包含任何其他文字"""
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USER_PROMPT_TEMPLATE = """请分析这张错题图片。
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@ -32,7 +35,8 @@ USER_PROMPT_TEMPLATE = """请分析这张错题图片。
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{{
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"subject": "数学/语文/英语",
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"question_type": "题型",
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"question_text": "完整题目内容",
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"question_text": "完整题目内容（纯文字版，用于搜索和列表显示）",
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"question_html": "题目的完整HTML排版代码（包含图形、表格等所有视觉元素，直接可用于浏览器渲染）",
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"student_answer": "学生写的错误答案（如果能看到）",
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"correct_answer": "正确答案（如果能判断）",
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"error_analysis": "错误原因分析",
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@ -26,6 +26,7 @@ export interface Mistake {
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question_type: string | null;
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grade_level: string | null;
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question_text: string;
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question_html: string | null;
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correct_answer: string | null;
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student_answer: string | null;
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error_analysis: string | null;
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