Zhang Jing Xuan
0841b1a103
- 前端：Vue3 + TS + Element Plus，24 个页面路由（课件/组题/教案/动画/思维导图/作文批改/命题/课堂/资源/社区等） - 后端：FastAPI + SQLAlchemy + SQLite，17 个路由模块，AI 服务层含降级模板 - AI：glm-5.x 推理模型已禁用思维链，确保输出真实内容 - 修复：ai_service 两处请求体注入 enable_thinking/thinking disabled - 测试账号：13900999999 / test1234
298 lines
11 KiB
Python
298 lines
11 KiB
Python
import re
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from io import BytesIO
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from urllib.parse import quote
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from docx import Document
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from pptx import Presentation
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from PIL import Image
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from pypdf import PdfReader
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from openpyxl import load_workbook
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from fastapi import APIRouter, Depends, HTTPException, UploadFile, File, Request
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from fastapi.responses import StreamingResponse
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from models.material import Material
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from models.user import User
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from schemas.ai import EssayGradeRequest, ExamExportRequest, ExamGenerateRequest, HtmlExportRequest
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from services.limiter import limiter
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from services.auth import get_current_user
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from services.audit import log_action
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from services.upload_security import validate_upload, sanitize_filename
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from services.ai_service import AIService
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from services.credits import credits_payload, spend_credits
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from database import get_db
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from sqlalchemy.orm import Session
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router = APIRouter(prefix="/api/ai", tags=["AI能力"])
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ai_service = AIService()
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@router.post("/essay-grade", response_model=dict)
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@limiter.limit("10/minute")
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async def grade_essay(
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request: Request,
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data: EssayGradeRequest,
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current_user: User = Depends(get_current_user),
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db: Session = Depends(get_db),
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):
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account = spend_credits(db, current_user, "essay_grade", "作文批改")
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db.commit()
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result = await ai_service.grade_essay(
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essay_text=data.essay_text, grade_level=data.grade_level,
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essay_type=data.essay_type, total_score=data.total_score,
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)
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return {"success": True, "data": result, "credits": credits_payload(account, "essay_grade")}
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@router.post("/exam-generate", response_model=dict)
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@limiter.limit("10/minute")
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async def generate_exam(
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request: Request,
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data: ExamGenerateRequest,
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current_user: User = Depends(get_current_user),
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db: Session = Depends(get_db),
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):
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account = spend_credits(db, current_user, "exam_generate", f"智能命题：{data.subject}")
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db.commit()
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result = await ai_service.generate_exam(
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subject=data.subject, grade=data.grade,
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knowledge_points=data.knowledge_points, difficulty=data.difficulty,
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question_types=data.question_types, count=data.count, total_score=data.total_score,
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)
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return {"success": True, "data": result, "credits": credits_payload(account, "exam_generate")}
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def _safe_filename(name: str, suffix: str) -> str:
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stem = re.sub(r"[\\/:*?\"<>|\r\n]+", "_", name).strip(" .") or "export"
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return f"{stem[:80]}{suffix}"
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def _summarize_text(text: str, limit: int = 4000) -> str:
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normalized = re.sub(r"[ \t]+", " ", text.replace("\r\n", "\n")).strip()
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normalized = re.sub(r"\n{3,}", "\n\n", normalized)
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return normalized[:limit]
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def _title_from_filename(filename: str) -> str:
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stem = filename.rsplit(".", 1)[0] if "." in filename else filename
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title = re.sub(r"[_-]+", " ", stem).strip() or "教学素材"
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return title[:200]
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def _decode_text(content: bytes) -> str:
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for encoding in ("utf-8-sig", "utf-8", "gb18030", "gbk"):
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try:
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return content.decode(encoding)
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except UnicodeDecodeError:
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continue
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return content.decode("utf-8", errors="ignore")
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def _extract_docx(content: bytes) -> str:
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document = Document(BytesIO(content))
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paragraphs = [paragraph.text.strip() for paragraph in document.paragraphs if paragraph.text.strip()]
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table_lines = []
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for table in document.tables:
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for row in table.rows:
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cells = [cell.text.strip() for cell in row.cells if cell.text.strip()]
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if cells:
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table_lines.append(" | ".join(cells))
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return "\n".join([*paragraphs, *table_lines])
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def _extract_pptx(content: bytes) -> str:
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presentation = Presentation(BytesIO(content))
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lines = []
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for index, slide in enumerate(presentation.slides, start=1):
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slide_lines = []
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for shape in slide.shapes:
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text = getattr(shape, "text", "").strip()
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if text:
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slide_lines.append(text)
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if slide_lines:
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lines.append(f"第 {index} 页：\n" + "\n".join(slide_lines))
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return "\n\n".join(lines)
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def _extract_pdf(content: bytes) -> str:
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reader = PdfReader(BytesIO(content))
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pages = []
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for index, page in enumerate(reader.pages, start=1):
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text = (page.extract_text() or "").strip()
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if text:
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pages.append(f"第 {index} 页：\n{text}")
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return "\n\n".join(pages)
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def _extract_xlsx(content: bytes) -> str:
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workbook = load_workbook(BytesIO(content), data_only=True, read_only=True)
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lines = []
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for sheet in workbook.worksheets:
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row_lines = []
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for row in sheet.iter_rows(values_only=True):
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cells = [str(cell).strip() for cell in row if cell is not None and str(cell).strip()]
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if cells:
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row_lines.append(" | ".join(cells))
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if row_lines:
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lines.append(f"工作表 {sheet.title}:\n" + "\n".join(row_lines[:200]))
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return "\n\n".join(lines)
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async def _describe_image(content: bytes, filename: str) -> str:
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try:
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with Image.open(BytesIO(content)) as image:
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width, height = image.size
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mode = image.mode
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except Exception:
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width, height, mode = 0, 0, ""
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meta = f"图片文件：{filename}"
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if width and height:
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meta += f"；尺寸：{width}×{height}px"
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if mode:
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meta += f"；色彩模式：{mode}"
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# Try AI vision OCR first; fall back to metadata-only description
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try:
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ocr_text = await ai_service.ocr_image(content, filename)
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if ocr_text and "[无法识别文字内容]" not in ocr_text:
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return f"{meta}\n\n【图片文字识别结果】\n{ocr_text}"
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except Exception as exc:
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import logging
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logging.getLogger(__name__).warning("图片OCR失败，使用元数据描述: %s", exc)
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return (
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f"{meta}。\n"
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"这是一份拍照/图片教学素材，可能包含教材页、题目、板书、图表、实验现象或课堂场景。"
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"当前系统已保存图片素材类型，可将其带入课件、动画、练习、教案或命题流程；"
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"生成时请教师在提示词中补充图片中的关键文字、题干或知识点，以便产出更准确。"
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)
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@router.post("/materials/parse", response_model=dict)
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@limiter.limit("20/minute")
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async def parse_material(
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request: Request,
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file: UploadFile = File(...),
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current_user: User = Depends(get_current_user),
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db: Session = Depends(get_db),
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):
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content = await file.read()
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check = validate_upload(file, content=content, max_size=50 * 1024 * 1024)
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filename = check.filename
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suffix = check.suffix
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media_type = check.media_type
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try:
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if media_type.startswith("image/"):
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text = await _describe_image(content, filename)
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material_type = "image"
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elif suffix == "docx":
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text = _extract_docx(content)
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material_type = "docx"
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elif suffix == "pptx":
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text = _extract_pptx(content)
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material_type = "pptx"
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elif suffix == "pdf":
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text = _extract_pdf(content)
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material_type = "pdf"
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elif suffix in {"xlsx", "xls"}:
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text = _extract_xlsx(content)
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material_type = "xlsx"
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elif suffix in {"txt", "md", "csv", "json"} or media_type.startswith("text/"):
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text = _decode_text(content)
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material_type = suffix or "text"
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else:
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raise HTTPException(status_code=415, detail="暂不支持该文件类型，请上传 txt、md、csv、json、docx、pptx、pdf、xlsx 或图片")
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except HTTPException:
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raise
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except Exception as exc:
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raise HTTPException(status_code=400, detail=f"材料解析失败：{exc}") from exc
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summary = _summarize_text(text)
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account = spend_credits(db, current_user, "material_parse", f"解析材料：{filename}")
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material = Material(
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user_id=current_user.id,
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filename=filename,
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title=_title_from_filename(filename),
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material_type=material_type,
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summary=summary,
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char_count=len(text),
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size=len(content),
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source="upload",
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)
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db.add(material)
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db.commit()
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db.refresh(material)
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log_action(db, action="material_parse", user=current_user, request=request, target_type="material", target_id=material.id, detail=f"解析 {filename}（{material_type}, {len(content)}B）")
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return {
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"success": True,
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"credits": credits_payload(account, "material_parse"),
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"data": {
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"material_id": material.id,
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"filename": filename,
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"title": material.title,
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"material_type": material_type,
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"size": len(content),
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"summary": summary,
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"char_count": len(text),
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},
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}
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@router.post("/export/html")
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def export_html(data: HtmlExportRequest, current_user: User = Depends(get_current_user)):
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content = f"""<!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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<meta name="viewport" content="width=device-width, initial-scale=1">
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<title>{data.title}</title>
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</head>
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<body>
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{data.html}
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</body>
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</html>
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"""
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filename = _safe_filename(data.title, ".html")
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return StreamingResponse(
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BytesIO(content.encode("utf-8")),
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media_type="text/html; charset=utf-8",
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headers={"Content-Disposition": f"attachment; filename*=UTF-8''{quote(filename)}"},
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)
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@router.post("/export/exam-docx")
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def export_exam_docx(data: ExamExportRequest, current_user: User = Depends(get_current_user)):
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document = Document()
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document.add_heading(data.title or "试卷", level=1)
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meta = " / ".join(part for part in [data.subject, data.grade] if part)
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if meta:
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document.add_paragraph(meta)
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for idx, question in enumerate(data.questions, start=1):
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q_type = question.get("type") or question.get("question_type") or "题目"
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score = question.get("score")
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heading = f"{idx}. [{q_type}]"
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if score:
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heading += f"（{score}分）"
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document.add_paragraph(heading)
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document.add_paragraph(str(question.get("content") or question.get("question") or ""))
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options = question.get("options") or []
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if isinstance(options, list):
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for option in options:
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document.add_paragraph(str(option), style=None)
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if data.answers:
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document.add_page_break()
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document.add_heading("参考答案", level=1)
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for idx, answer in enumerate(data.answers, start=1):
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document.add_paragraph(f"{idx}. {answer}")
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buffer = BytesIO()
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document.save(buffer)
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buffer.seek(0)
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filename = _safe_filename(data.title, ".docx")
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return StreamingResponse(
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buffer,
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media_type="application/vnd.openxmlformats-officedocument.wordprocessingml.document",
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headers={"Content-Disposition": f"attachment; filename*=UTF-8''{quote(filename)}"},
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)
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