finish app develop

2026-05-18 15:50:43 +08:00
parent 8f23a841f0
commit 17decab2fc
20 changed files with 2447 additions and 0 deletions
--- a/app/init.py
+++ b/app/init.py
@@ -0,0 +1 @@
+"""DOCX compliance analysis application."""
--- a/app/analyzer.py
+++ b/app/analyzer.py
@@ -0,0 +1,222 @@
+from __future__ import annotations
+
+import json
+import re
+from collections import Counter
+from typing import Protocol
+
+import requests
+
+from app.config import ProviderConfig
+from app.docx_parser import ParsedDocument
+from app.report_generator import AnalysisReport
+from app.skill_loader import Skill
+
+
+class SupportsPost(Protocol):
+    def post(self, url: str, **kwargs): ...
+
+
+IMPORTANT_TERMS = {
+    "需求",
+    "接口",
+    "测试",
+    "合格性",
+    "追踪",
+    "追溯",
+    "配置",
+    "质量",
+    "部署",
+    "安装",
+    "验收",
+    "设计",
+    "资源",
+    "风险",
+    "计划",
+    "说明",
+    "文档",
+    "CSCI",
+    "GJB",
+}
+
+
+def _tokens(text: str) -> list[str]:
+    ascii_tokens = re.findall(r"[A-Za-z][A-Za-z0-9_-]{2,}", text)
+    chinese_terms = [term for term in IMPORTANT_TERMS if term in text]
+    return [token.lower() for token in ascii_tokens] + chinese_terms
+
+
+def select_relevant_skills(parsed: ParsedDocument, skills: list[Skill], max_skills: int = 6) -> list[Skill]:
+    document_tokens = Counter(_tokens(parsed.text + "\n" + "\n".join(h.text for h in parsed.headings)))
+    scored: list[tuple[int, Skill]] = []
+
+    for skill in skills:
+        skill_text = f"{skill.slug}\n{skill.name}\n{skill.description}\n{skill.use_when}\n{skill.content[:3000]}"
+        score = 0
+        skill_tokens = set(_tokens(skill_text))
+        for token, count in document_tokens.items():
+            if token in skill_tokens:
+                score += count
+        if parsed.filename.lower().endswith(".docx"):
+            score += 1
+        if score > 0:
+            scored.append((score, skill))
+
+    scored.sort(key=lambda item: (-item[0], item[1].slug))
+    if not scored:
+        return skills[:max_skills]
+    return [skill for _, skill in scored[:max_skills]]
+
+
+def build_analysis_prompt(parsed: ParsedDocument, skills: list[Skill]) -> str:
+    skill_sections = []
+    for skill in skills:
+        skill_sections.append(
+            f"## {skill.slug}\n名称: {skill.name}\n描述: {skill.description}\n适用条件: {skill.use_when}\n规范内容:\n{skill.content[:6000]}"
+        )
+
+    document_outline = "\n".join(f"- H{heading.level} {heading.text}" for heading in parsed.headings[:80]) or "未识别到标题。"
+    document_text = parsed.text[:18000]
+
+    return f"""你是军用软件文档符合性审查助手。请依据给定 GJB438C/GJB2786 技能规范，分析上传 DOCX 是否符合规范。
+
+请输出中文 Markdown，必须包含以下小节：
+1. 总体结论
+2. 符合项
+3. 不符合项
+4. 缺失章节或缺失证据
+5. 整改建议
+6. 需人工复核事项
+
+要求：
+- 每个问题尽量引用文档中的标题、关键词或证据摘要。
+- 不要编造未在文档中出现的证据。
+- 如果无法判断，标记为“需人工复核”。
+
+# 文件
+{parsed.filename}
+
+# 文档目录
+{document_outline}
+
+# 待检查技能
+{chr(10).join(skill_sections)}
+
+# 文档正文摘录
+{document_text}
+"""
+
+
+class LLMClient:
+    def __init__(self, provider: ProviderConfig, session: SupportsPost | None = None, timeout: int = 120) -> None:
+        self.provider = provider
+        self.session = session or requests.Session()
+        self.timeout = timeout
+
+    def complete(self, prompt: str) -> str:
+        headers = {"Content-Type": "application/json"}
+        if self.provider.api_key and self.provider.api_key != "EMPTY":
+            headers["Authorization"] = f"Bearer {self.provider.api_key}"
+
+        payload = {
+            "model": self.provider.model,
+            "messages": [{"role": "user", "content": prompt}],
+            "temperature": self.provider.temperature,
+            "max_tokens": self.provider.max_tokens,
+        }
+        response = self.session.post(
+            self.provider.chat_completions_url,
+            headers=headers,
+            json=payload,
+            timeout=self.timeout,
+        )
+        response.raise_for_status()
+        data = response.json()
+        return data["choices"][0]["message"]["content"]
+
+
+def heuristic_analysis(parsed: ParsedDocument, skills: list[Skill]) -> AnalysisReport:
+    headings_text = "\n".join(h.text for h in parsed.headings)
+    full_text = parsed.text
+    findings: list[dict[str, str]] = []
+    recommendations: list[str] = []
+
+    required_terms = ["范围", "引用文档", "需求", "合格性", "追踪", "接口"]
+    for term in required_terms:
+        present = term in full_text or term in headings_text
+        findings.append(
+            {
+                "status": "符合" if present else "需整改",
+                "item": f"检查关键内容：{term}",
+                "evidence": "文档中已发现相关表述" if present else "未在解析文本中发现明确表述",
+            }
+        )
+        if not present:
+            recommendations.append(f"补充或明确“{term}”相关章节与证据。")
+
+    if not parsed.headings:
+        findings.append({"status": "需整改", "item": "章节结构", "evidence": "未识别到 Word 标题样式"})
+        recommendations.append("使用 Word 标题样式组织章节，便于目录和符合性审查。")
+
+    issue_count = sum(1 for item in findings if item["status"] != "符合")
+    summary = "通过" if issue_count == 0 else "部分通过，需人工复核"
+    raw_output = "未调用模型，已使用本地启发式规则生成初步分析。"
+
+    return AnalysisReport(
+        source_filename=parsed.filename,
+        provider_name="local",
+        model_name="heuristic",
+        matched_skills=[skill.slug for skill in skills],
+        summary=summary,
+        findings=findings,
+        recommendations=recommendations or ["保持现有章节结构，并由人工进行最终符合性确认。"],
+        raw_model_output=raw_output,
+    )
+
+
+def report_from_model_output(
+    parsed: ParsedDocument,
+    skills: list[Skill],
+    provider_name: str,
+    model_name: str,
+    output: str,
+) -> AnalysisReport:
+    findings = [{"status": "模型分析", "item": "完整分析结果", "evidence": output[:1200]}]
+    recommendations = _extract_recommendations(output)
+    return AnalysisReport(
+        source_filename=parsed.filename,
+        provider_name=provider_name,
+        model_name=model_name,
+        matched_skills=[skill.slug for skill in skills],
+        summary=_extract_summary(output),
+        findings=findings,
+        recommendations=recommendations,
+        raw_model_output=output,
+    )
+
+
+def _extract_summary(output: str) -> str:
+    for line in output.splitlines():
+        normalized = line.strip(" #：:")
+        if "总体结论" in normalized and len(normalized) > 4:
+            return normalized
+    return "模型已生成分析结果，需人工复核"
+
+
+def _extract_recommendations(output: str) -> list[str]:
+    recommendations: list[str] = []
+    in_section = False
+    for line in output.splitlines():
+        stripped = line.strip()
+        if "整改建议" in stripped or "修改建议" in stripped:
+            in_section = True
+            continue
+        if in_section and stripped.startswith("#"):
+            break
+        if in_section and stripped.lstrip("-0123456789.、 "):
+            recommendations.append(stripped.lstrip("-0123456789.、 "))
+    return recommendations[:10] or ["按模型分析结果逐项整改，并进行人工复核。"]
+
+
+def serialize_prompt_debug(prompt: str) -> str:
+    return json.dumps({"prompt_preview": prompt[:2000]}, ensure_ascii=False, indent=2)
--- a/app/config.py
+++ b/app/config.py
@@ -0,0 +1,50 @@
+from __future__ import annotations
+
+from dataclasses import dataclass
+from pathlib import Path
+
+import yaml
+
+
+@dataclass(frozen=True)
+class ProviderConfig:
+    api_key: str
+    base_url: str
+    max_tokens: int
+    model: str
+    temperature: float
+
+    @property
+    def chat_completions_url(self) -> str:
+        base_url = self.base_url.rstrip("/")
+        if base_url.endswith("/chat/completions"):
+            return base_url
+        return f"{base_url}/chat/completions"
+
+
+@dataclass(frozen=True)
+class ApiSettings:
+    provider_name: str
+    provider: ProviderConfig
+
+
+def load_api_config(path: Path | str = Path("configs/api_config.yaml"), provider_name: str | None = None) -> ApiSettings:
+    config_path = Path(path)
+    data = yaml.safe_load(config_path.read_text(encoding="utf-8")) or {}
+    selected_name = provider_name or data.get("default_provider")
+    providers = data.get("providers", {})
+
+    if not selected_name:
+        raise ValueError("api_config.yaml missing default_provider")
+    if selected_name not in providers:
+        raise ValueError(f"provider not found in api_config.yaml: {selected_name}")
+
+    provider_data = providers[selected_name]
+    provider = ProviderConfig(
+        api_key=str(provider_data.get("api_key", "")),
+        base_url=str(provider_data["base_url"]),
+        max_tokens=int(provider_data.get("max_tokens", 4096)),
+        model=str(provider_data["model"]),
+        temperature=float(provider_data.get("temperature", 0.7)),
+    )
+    return ApiSettings(provider_name=selected_name, provider=provider)
--- a/app/docx_parser.py
+++ b/app/docx_parser.py
@@ -0,0 +1,67 @@
+from __future__ import annotations
+
+from dataclasses import dataclass
+from pathlib import Path
+
+from docx import Document
+
+
+@dataclass(frozen=True)
+class Heading:
+    level: int
+    text: str
+
+
+@dataclass(frozen=True)
+class ParsedDocument:
+    filename: str
+    text: str
+    paragraphs: list[str]
+    headings: list[Heading]
+    tables: list[list[list[str]]]
+
+
+def _heading_level(style_name: str) -> int | None:
+    if not style_name.lower().startswith("heading"):
+        return None
+    parts = style_name.split()
+    if parts and parts[-1].isdigit():
+        return int(parts[-1])
+    return 1
+
+
+def parse_docx(path: Path | str, display_filename: str | None = None) -> ParsedDocument:
+    docx_path = Path(path)
+    document = Document(docx_path)
+    paragraphs: list[str] = []
+    headings: list[Heading] = []
+    tables: list[list[list[str]]] = []
+    text_parts: list[str] = []
+
+    for paragraph in document.paragraphs:
+        text = paragraph.text.strip()
+        if not text:
+            continue
+        paragraphs.append(text)
+        text_parts.append(text)
+        level = _heading_level(paragraph.style.name if paragraph.style else "")
+        if level is not None:
+            headings.append(Heading(level=level, text=text))
+
+    for table in document.tables:
+        rows: list[list[str]] = []
+        for row in table.rows:
+            values = [cell.text.strip() for cell in row.cells]
+            if any(values):
+                rows.append(values)
+                text_parts.append(" | ".join(values))
+        if rows:
+            tables.append(rows)
+
+    return ParsedDocument(
+        filename=display_filename or docx_path.name,
+        text="\n".join(text_parts),
+        paragraphs=paragraphs,
+        headings=headings,
+        tables=tables,
+    )
--- a/app/main.py
+++ b/app/main.py
@@ -0,0 +1,300 @@
+from __future__ import annotations
+
+import shutil
+import threading
+import time
+from dataclasses import dataclass, field
+from pathlib import Path
+from uuid import uuid4
+from typing import Callable
+
+from fastapi import FastAPI, File, Form, HTTPException, Request, UploadFile
+from fastapi.responses import FileResponse, HTMLResponse
+from fastapi.staticfiles import StaticFiles
+from fastapi.templating import Jinja2Templates
+
+from app.analyzer import (
+    LLMClient,
+    build_analysis_prompt,
+    heuristic_analysis,
+    report_from_model_output,
+    select_relevant_skills,
+)
+from app.config import load_api_config
+from app.docx_parser import parse_docx
+from app.report_generator import generate_docx_report, generate_markdown_report
+from app.skill_loader import load_skill_catalog
+
+
+ROOT_DIR = Path(__file__).resolve().parent.parent
+UPLOAD_DIR = ROOT_DIR / "uploads"
+OUTPUT_DIR = ROOT_DIR / "outputs"
+SKILL_DIR = ROOT_DIR / "GJB438C-2021_prd_skills"
+CONFIG_PATH = ROOT_DIR / "configs" / "api_config.yaml"
+MAX_UPLOAD_BYTES = 30 * 1024 * 1024
+ProgressCallback = Callable[[int, str], None]
+
+
+@dataclass
+class AnalysisTask:
+    task_id: str
+    source_filename: str
+    status: str = "queued"
+    progress: int = 0
+    message: str = "任务已创建"
+    summary: str = ""
+    matched_skills: list[str] = field(default_factory=list)
+    downloads: dict[str, str] = field(default_factory=dict)
+    error: str = ""
+    created_at: float = field(default_factory=time.time)
+    updated_at: float = field(default_factory=time.time)
+
+    def to_dict(self) -> dict[str, object]:
+        return {
+            "task_id": self.task_id,
+            "source_filename": self.source_filename,
+            "status": self.status,
+            "progress": self.progress,
+            "message": self.message,
+            "summary": self.summary,
+            "matched_skills": self.matched_skills,
+            "downloads": self.downloads,
+            "error": self.error,
+            "created_at": self.created_at,
+            "updated_at": self.updated_at,
+        }
+
+
+class AnalysisTaskStore:
+    def __init__(self) -> None:
+        self._tasks: dict[str, AnalysisTask] = {}
+        self._lock = threading.Lock()
+
+    def create(self, source_filename: str) -> AnalysisTask:
+        task = AnalysisTask(task_id=uuid4().hex, source_filename=source_filename)
+        with self._lock:
+            self._tasks[task.task_id] = task
+        return task
+
+    def update(
+        self,
+        task_id: str,
+        *,
+        status: str | None = None,
+        progress: int | None = None,
+        message: str | None = None,
+        summary: str | None = None,
+        matched_skills: list[str] | None = None,
+        downloads: dict[str, str] | None = None,
+        error: str | None = None,
+    ) -> AnalysisTask:
+        with self._lock:
+            task = self._tasks[task_id]
+            if status is not None:
+                task.status = status
+            if progress is not None:
+                task.progress = progress
+            if message is not None:
+                task.message = message
+            if summary is not None:
+                task.summary = summary
+            if matched_skills is not None:
+                task.matched_skills = matched_skills
+            if downloads is not None:
+                task.downloads = downloads
+            if error is not None:
+                task.error = error
+            task.updated_at = time.time()
+            return task
+
+    def get(self, task_id: str) -> AnalysisTask | None:
+        with self._lock:
+            return self._tasks.get(task_id)
+
+
+TASK_STORE = AnalysisTaskStore()
+
+app = FastAPI(title="GJB438C DOCX 规范分析")
+templates = Jinja2Templates(directory=str(ROOT_DIR / "app" / "templates"))
+app.mount("/static", StaticFiles(directory=str(ROOT_DIR / "app" / "static")), name="static")
+
+
+def analyze_saved_docx(
+    upload_path: Path,
+    provider: str | None = None,
+    use_model: bool = True,
+    display_filename: str | None = None,
+    progress_callback: ProgressCallback | None = None,
+) -> dict[str, object]:
+    def progress(percent: int, message: str) -> None:
+        if progress_callback is not None:
+            progress_callback(percent, message)
+
+    progress(5, "正在解析 DOCX 文档")
+    parsed = parse_docx(upload_path, display_filename=display_filename)
+    progress(20, "DOCX 解析完成，正在加载技能规范")
+    skills = load_skill_catalog(SKILL_DIR)
+    progress(35, "技能规范已加载，正在匹配候选技能")
+    selected_skills = select_relevant_skills(parsed, skills)
+    progress(50, f"已匹配 {len(selected_skills)} 项技能，正在读取模型配置")
+    settings = load_api_config(CONFIG_PATH, provider_name=provider or None)
+
+    if use_model:
+        progress(65, f"正在调用 {settings.provider.model} 进行分析")
+        prompt = build_analysis_prompt(parsed, selected_skills)
+        try:
+            output = LLMClient(settings.provider).complete(prompt)
+            report = report_from_model_output(
+                parsed,
+                selected_skills,
+                settings.provider_name,
+                settings.provider.model,
+                output,
+            )
+        except Exception as exc:
+            report = heuristic_analysis(parsed, selected_skills)
+            report = report.__class__(
+                source_filename=report.source_filename,
+                provider_name=settings.provider_name,
+                model_name=f"{settings.provider.model} (调用失败，已降级)",
+                matched_skills=report.matched_skills,
+                summary=f"{report.summary}；模型调用失败：{exc}",
+                findings=report.findings,
+                recommendations=report.recommendations,
+                raw_model_output=f"模型调用失败：{exc}\n\n{report.raw_model_output}",
+            )
+    else:
+        progress(70, "已关闭模型分析，正在使用本地规则生成结果")
+        report = heuristic_analysis(parsed, selected_skills)
+
+    progress(85, "正在生成 Markdown 分析文档")
+    markdown_path = generate_markdown_report(report, OUTPUT_DIR)
+    progress(100, "分析完成")
+
+    return {
+        "source_filename": parsed.filename,
+        "summary": report.summary,
+        "matched_skills": report.matched_skills,
+        "downloads": {"markdown": f"/download/{markdown_path.name}"},
+        "markdown_filename": markdown_path.name,
+    }
+
+
+def _run_analysis_task(
+    task_id: str,
+    upload_path: Path,
+    provider: str | None,
+    use_model: bool,
+    display_filename: str,
+) -> None:
+    def on_progress(progress: int, message: str) -> None:
+        TASK_STORE.update(task_id, status="running", progress=progress, message=message)
+
+    try:
+        TASK_STORE.update(task_id, status="running", progress=1, message="任务已启动")
+        result = analyze_saved_docx(
+            upload_path,
+            provider=provider,
+            use_model=use_model,
+            display_filename=display_filename,
+            progress_callback=on_progress,
+        )
+        TASK_STORE.update(
+            task_id,
+            status="completed",
+            progress=100,
+            message="分析完成",
+            summary=str(result["summary"]),
+            matched_skills=list(result["matched_skills"]),
+            downloads=dict(result["downloads"]),
+        )
+    except Exception as exc:
+        TASK_STORE.update(task_id, status="error", progress=100, message="分析失败", error=str(exc))
+
+
+@app.get("/", response_class=HTMLResponse)
+def index(request: Request) -> HTMLResponse:
+    settings = load_api_config(CONFIG_PATH)
+    skills = load_skill_catalog(SKILL_DIR)
+    return templates.TemplateResponse(
+        request,
+        "index.html",
+        {
+            "default_provider": settings.provider_name,
+            "skill_count": len(skills),
+        },
+    )
+
+
+@app.post("/analyze")
+async def analyze_docx(
+    file: UploadFile = File(...),
+    provider: str | None = Form(None),
+    use_model: str = Form("true"),
+):
+    if not file.filename or not file.filename.lower().endswith(".docx"):
+        raise HTTPException(status_code=400, detail="仅支持上传 .docx 文件")
+
+    UPLOAD_DIR.mkdir(parents=True, exist_ok=True)
+    OUTPUT_DIR.mkdir(parents=True, exist_ok=True)
+    upload_path = UPLOAD_DIR / f"{uuid4().hex}_{Path(file.filename).name}"
+
+    content = await file.read()
+    if len(content) > MAX_UPLOAD_BYTES:
+        raise HTTPException(status_code=413, detail="文件超过 30MB 限制")
+    upload_path.write_bytes(content)
+
+    should_use_model = use_model.lower() in {"1", "true", "yes", "on"}
+    task = TASK_STORE.create(Path(file.filename).name)
+    threading.Thread(
+        target=_run_analysis_task,
+        args=(task.task_id, upload_path, provider, should_use_model, Path(file.filename).name),
+        daemon=True,
+    ).start()
+    return {
+        "task_id": task.task_id,
+        "status_url": f"/tasks/{task.task_id}",
+        "status": task.status,
+        "progress": task.progress,
+        "message": "任务已提交",
+    }
+
+
+@app.get("/tasks/{task_id}")
+def get_task(task_id: str) -> dict[str, object]:
+    task = TASK_STORE.get(task_id)
+    if task is None:
+        raise HTTPException(status_code=404, detail="任务不存在")
+    return task.to_dict()
+
+
+@app.get("/download/{filename}")
+def download_report(filename: str):
+    safe_name = Path(filename).name
+    path = OUTPUT_DIR / safe_name
+    if not path.exists() or not path.is_file():
+        raise HTTPException(status_code=404, detail="报告不存在")
+
+    media_type = "application/octet-stream"
+    if path.suffix == ".docx":
+        media_type = "application/vnd.openxmlformats-officedocument.wordprocessingml.document"
+    elif path.suffix == ".md":
+        media_type = "text/markdown; charset=utf-8"
+
+    return FileResponse(path, filename=path.name, media_type=media_type)
+
+
+@app.post("/cleanup")
+def cleanup_runtime_files() -> dict[str, int]:
+    removed = 0
+    for directory in (UPLOAD_DIR, OUTPUT_DIR):
+        if not directory.exists():
+            continue
+        for path in directory.iterdir():
+            if path.is_file():
+                path.unlink()
+                removed += 1
+            elif path.is_dir():
+                shutil.rmtree(path)
+                removed += 1
+    return {"removed": removed}
--- a/app/report_generator.py
+++ b/app/report_generator.py
@@ -0,0 +1,108 @@
+from __future__ import annotations
+
+from dataclasses import dataclass
+from datetime import datetime
+from pathlib import Path
+from uuid import uuid4
+
+from docx import Document
+
+
+@dataclass(frozen=True)
+class AnalysisReport:
+    source_filename: str
+    provider_name: str
+    model_name: str
+    matched_skills: list[str]
+    summary: str
+    findings: list[dict[str, str]]
+    recommendations: list[str]
+    raw_model_output: str
+
+
+def _safe_stem(filename: str) -> str:
+    stem = Path(filename).stem or "analysis"
+    safe = "".join(ch if ch.isalnum() or ch in ("-", "_") else "_" for ch in stem)
+    return safe[:60] or "analysis"
+
+
+def _report_base_path(report: AnalysisReport, output_dir: Path, suffix: str) -> Path:
+    output_dir.mkdir(parents=True, exist_ok=True)
+    return output_dir / f"{_safe_stem(report.source_filename)}_{uuid4().hex[:8]}_analysis.{suffix}"
+
+
+def generate_markdown_report(report: AnalysisReport, output_dir: Path | str) -> Path:
+    path = _report_base_path(report, Path(output_dir), "md")
+    lines = [
+        "# DOCX 规范分析报告",
+        "",
+        "## 基本信息",
+        "",
+        f"- 源文件：{report.source_filename}",
+        f"- 分析时间：{datetime.now().strftime('%Y-%m-%d %H:%M:%S')}",
+        f"- 模型供应商：{report.provider_name}",
+        f"- 模型名称：{report.model_name}",
+        f"- 命中技能：{', '.join(report.matched_skills) or '无'}",
+        "",
+        "## 总体结论",
+        "",
+        report.summary,
+        "",
+        "## 技能符合性矩阵",
+        "",
+        "| 状态 | 检查项 | 证据或说明 |",
+        "| --- | --- | --- |",
+    ]
+    for finding in report.findings:
+        lines.append(
+            f"| {finding.get('status', '')} | {finding.get('item', '')} | {finding.get('evidence', '').replace('|', '/')} |"
+        )
+    lines.extend(["", "## 修改建议", ""])
+    for item in report.recommendations:
+        lines.append(f"- {item}")
+    lines.extend(["", "## 模型分析原文", "", report.raw_model_output])
+    path.write_text("\n".join(lines), encoding="utf-8")
+    return path
+
+
+def generate_docx_report(report: AnalysisReport, output_dir: Path | str) -> Path:
+    path = _report_base_path(report, Path(output_dir), "docx")
+    document = Document()
+    document.add_heading("DOCX 规范分析报告", level=0)
+    document.add_heading("基本信息", level=1)
+    for label, value in [
+        ("源文件", report.source_filename),
+        ("分析时间", datetime.now().strftime("%Y-%m-%d %H:%M:%S")),
+        ("模型供应商", report.provider_name),
+        ("模型名称", report.model_name),
+        ("命中技能", ", ".join(report.matched_skills) or "无"),
+    ]:
+        document.add_paragraph(f"{label}：{value}")
+
+    document.add_heading("总体结论", level=1)
+    document.add_paragraph(report.summary)
+
+    document.add_heading("技能符合性矩阵", level=1)
+    table = document.add_table(rows=1, cols=3)
+    table.style = "Table Grid"
+    headers = table.rows[0].cells
+    headers[0].text = "状态"
+    headers[1].text = "检查项"
+    headers[2].text = "证据或说明"
+    for finding in report.findings:
+        row = table.add_row().cells
+        row[0].text = finding.get("status", "")
+        row[1].text = finding.get("item", "")
+        row[2].text = finding.get("evidence", "")
+
+    document.add_heading("修改建议", level=1)
+    for item in report.recommendations:
+        document.add_paragraph(item, style="List Bullet")
+
+    document.add_heading("模型分析原文", level=1)
+    for line in report.raw_model_output.splitlines() or ["无"]:
+        document.add_paragraph(line)
+
+    document.add_paragraph("说明：模型分析结果需人工复核，不应直接作为正式审查结论。")
+    document.save(path)
+    return path
--- a/app/skill_loader.py
+++ b/app/skill_loader.py
@@ -0,0 +1,77 @@
+from __future__ import annotations
+
+import re
+from dataclasses import dataclass
+from pathlib import Path
+
+
+@dataclass(frozen=True)
+class Skill:
+    slug: str
+    name: str
+    description: str
+    use_when: str
+    content: str
+    path: Path | None
+
+
+INDEX_ROW_RE = re.compile(
+    r"^\|\s*\[([^\]]+)\]\(([^)]+)\)\s*\|\s*([^|]+?)\s*\|\s*([^|]+?)\s*\|"
+)
+FRONT_MATTER_RE = re.compile(r"^---\n(.*?)\n---\n", re.DOTALL)
+
+
+def _front_matter_value(content: str, key: str) -> str | None:
+    match = FRONT_MATTER_RE.match(content)
+    if not match:
+        return None
+    for line in match.group(1).splitlines():
+        if line.startswith(f"{key}:"):
+            return line.split(":", 1)[1].strip()
+    return None
+
+
+def load_skill_catalog(root: Path | str = Path("GJB438C-2021_prd_skills")) -> list[Skill]:
+    root_path = Path(root)
+    index_path = root_path / "index.md"
+    skills: list[Skill] = []
+    seen: set[str] = set()
+
+    if index_path.exists():
+        for line in index_path.read_text(encoding="utf-8").splitlines():
+            match = INDEX_ROW_RE.match(line)
+            if not match:
+                continue
+            slug, relative_path, description, use_when = [part.strip() for part in match.groups()]
+            skill_path = root_path / relative_path
+            content = skill_path.read_text(encoding="utf-8") if skill_path.exists() else ""
+            name = _front_matter_value(content, "name") or slug
+            skills.append(
+                Skill(
+                    slug=slug,
+                    name=name,
+                    description=description,
+                    use_when=use_when,
+                    content=content,
+                    path=skill_path,
+                )
+            )
+            seen.add(slug)
+
+    for skill_path in sorted(root_path.glob("*/SKILL.md")):
+        slug = skill_path.parent.name
+        if slug in seen:
+            continue
+        content = skill_path.read_text(encoding="utf-8")
+        skills.append(
+            Skill(
+                slug=slug,
+                name=_front_matter_value(content, "name") or slug,
+                description=_front_matter_value(content, "description") or "",
+                use_when="",
+                content=content,
+                path=skill_path,
+            )
+        )
+
+    return skills
--- a/app/static/app.js
+++ b/app/static/app.js
@@ -0,0 +1,72 @@
+const form = document.querySelector("#upload-form");
+const result = document.querySelector("#result");
+const summary = document.querySelector("#summary");
+const skills = document.querySelector("#skills");
+const mdLink = document.querySelector("#download-md");
+const progressBar = document.querySelector("#analysis-progress");
+const statusText = document.querySelector("#analysis-status");
+const button = form.querySelector("button");
+
+async function pollTask(statusUrl) {
+  while (true) {
+    const response = await fetch(statusUrl);
+    const payload = await response.json();
+
+    progressBar.style.width = `${payload.progress || 0}%`;
+    statusText.textContent = payload.message || "分析中";
+
+    if (payload.status === "completed") {
+      return payload;
+    }
+
+    if (payload.status === "error") {
+      throw new Error(payload.error || "分析失败");
+    }
+
+    await new Promise((resolve) => setTimeout(resolve, 1000));
+  }
+}
+
+form.addEventListener("submit", async (event) => {
+  event.preventDefault();
+  button.disabled = true;
+  button.textContent = "分析中...";
+  result.hidden = true;
+  progressBar.style.width = "0%";
+  statusText.textContent = "任务提交中...";
+
+  const data = new FormData(form);
+  if (!data.has("use_model")) {
+    data.set("use_model", "false");
+  }
+
+  try {
+    const response = await fetch("/analyze", {
+      method: "POST",
+      body: data,
+    });
+    const payload = await response.json();
+    if (!response.ok) {
+      throw new Error(payload.detail || "分析失败");
+    }
+
+    result.hidden = false;
+    const task = await pollTask(payload.status_url);
+    summary.textContent = task.summary;
+    skills.innerHTML = "";
+    task.matched_skills.forEach((name) => {
+      const item = document.createElement("span");
+      item.textContent = name;
+      skills.appendChild(item);
+    });
+    mdLink.href = task.downloads.markdown;
+  } catch (error) {
+    summary.textContent = error.message;
+    skills.innerHTML = "";
+    result.hidden = false;
+    statusText.textContent = "分析失败";
+  } finally {
+    button.disabled = false;
+    button.textContent = "开始分析";
+  }
+});
--- a/app/static/styles.css
+++ b/app/static/styles.css
@@ -0,0 +1,218 @@
+:root {
+  color-scheme: light;
+  font-family: "Inter", "Segoe UI", Arial, sans-serif;
+  background: #f5f7fb;
+  color: #172033;
+}
+
+* {
+  box-sizing: border-box;
+}
+
+body {
+  margin: 0;
+}
+
+.shell {
+  min-height: 100vh;
+  display: grid;
+  place-items: center;
+  padding: 32px;
+}
+
+.panel {
+  width: min(880px, 100%);
+  background: #ffffff;
+  border: 1px solid #dbe2ee;
+  border-radius: 8px;
+  box-shadow: 0 20px 60px rgba(23, 32, 51, 0.08);
+  padding: 32px;
+}
+
+.header {
+  display: flex;
+  justify-content: space-between;
+  gap: 24px;
+  align-items: flex-start;
+  margin-bottom: 28px;
+}
+
+.eyebrow {
+  margin: 0 0 8px;
+  color: #5d6d83;
+  font-size: 13px;
+  letter-spacing: 0;
+}
+
+h1,
+h2 {
+  margin: 0;
+}
+
+h1 {
+  font-size: 32px;
+  font-weight: 700;
+}
+
+h2 {
+  font-size: 20px;
+}
+
+.meta {
+  display: flex;
+  gap: 8px;
+  flex-wrap: wrap;
+  justify-content: flex-end;
+}
+
+.meta span,
+.skills span {
+  border: 1px solid #cfd8e6;
+  border-radius: 6px;
+  padding: 6px 9px;
+  color: #44546a;
+  background: #f8fafc;
+  font-size: 13px;
+}
+
+.form {
+  display: grid;
+  gap: 20px;
+}
+
+.drop-zone {
+  border: 1px dashed #8aa1bd;
+  border-radius: 8px;
+  padding: 34px;
+  background: #fbfcfe;
+  display: grid;
+  gap: 8px;
+  cursor: pointer;
+}
+
+.drop-zone input {
+  width: 100%;
+}
+
+.drop-title {
+  font-size: 18px;
+  font-weight: 700;
+}
+
+.drop-subtitle {
+  color: #5d6d83;
+}
+
+.controls {
+  display: grid;
+  grid-template-columns: 1fr auto;
+  gap: 16px;
+  align-items: end;
+}
+
+label {
+  display: grid;
+  gap: 8px;
+  color: #344054;
+  font-size: 14px;
+}
+
+select {
+  height: 42px;
+  border: 1px solid #cfd8e6;
+  border-radius: 6px;
+  padding: 0 12px;
+  background: #ffffff;
+}
+
+.checkbox {
+  display: flex;
+  align-items: center;
+  gap: 8px;
+  height: 42px;
+}
+
+button,
+.downloads a {
+  border: 0;
+  border-radius: 6px;
+  background: #1f6feb;
+  color: #ffffff;
+  min-height: 44px;
+  padding: 0 18px;
+  font-weight: 700;
+  cursor: pointer;
+  text-decoration: none;
+  display: inline-flex;
+  align-items: center;
+  justify-content: center;
+}
+
+button:disabled {
+  background: #8aa1bd;
+  cursor: wait;
+}
+
+.result {
+  margin-top: 28px;
+  border-top: 1px solid #dbe2ee;
+  padding-top: 24px;
+}
+
+.progress-wrap {
+  display: grid;
+  gap: 8px;
+  margin: 14px 0 18px;
+}
+
+.progress-track {
+  height: 10px;
+  border-radius: 999px;
+  background: #e8edf4;
+  overflow: hidden;
+}
+
+.progress-bar {
+  height: 100%;
+  border-radius: inherit;
+  background: linear-gradient(90deg, #1f6feb, #4f8df5);
+  transition: width 0.2s ease;
+}
+
+.status-text {
+  margin: 0;
+  color: #5d6d83;
+  font-size: 14px;
+}
+
+.skills,
+.downloads {
+  display: flex;
+  flex-wrap: wrap;
+  gap: 8px;
+  margin-top: 16px;
+}
+
+.downloads a:last-child {
+  background: #334155;
+}
+
+@media (max-width: 680px) {
+  .shell {
+    padding: 16px;
+  }
+
+  .panel {
+    padding: 22px;
+  }
+
+  .header,
+  .controls {
+    grid-template-columns: 1fr;
+    display: grid;
+  }
+
+  .meta {
+    justify-content: flex-start;
+  }
+}
--- a/app/templates/index.html
+++ b/app/templates/index.html
@@ -0,0 +1,67 @@
+<!doctype html>
+<html lang="zh-CN">
+  <head>
+    <meta charset="utf-8">
+    <meta name="viewport" content="width=device-width, initial-scale=1">
+    <title>GJB438C DOCX 规范分析</title>
+    <link rel="stylesheet" href="/static/styles.css">
+  </head>
+  <body>
+    <main class="shell">
+      <section class="panel">
+        <div class="header">
+          <div>
+            <p class="eyebrow">GJB438C / GJB2786</p>
+            <h1>DOCX 规范分析</h1>
+          </div>
+          <div class="meta">
+            <span>{{ skill_count }} 项技能</span>
+            <span>默认 {{ default_provider }}</span>
+          </div>
+        </div>
+
+        <form id="upload-form" class="form">
+          <label class="drop-zone">
+            <input id="file" name="file" type="file" accept=".docx" required>
+            <span class="drop-title">选择 DOCX 文件</span>
+            <span class="drop-subtitle">上传后自动匹配技能规范并生成报告</span>
+          </label>
+
+          <div class="controls">
+            <label>
+              模型供应商
+              <select name="provider">
+                <option value="{{ default_provider }}">默认：{{ default_provider }}</option>
+                <option value="intranet">intranet / qwen3-coder</option>
+                <option value="deepseek">deepseek / deepseek-chat</option>
+              </select>
+            </label>
+            <label class="checkbox">
+              <input type="checkbox" name="use_model" value="true" checked>
+              调用模型分析
+            </label>
+          </div>
+
+          <button type="submit">开始分析</button>
+        </form>
+
+        <section id="result" class="result" hidden>
+          <h2>分析结果</h2>
+          <div class="progress-wrap">
+            <div class="progress-track">
+              <div id="analysis-progress" class="progress-bar" style="width: 0%"></div>
+            </div>
+            <p id="analysis-status" class="status-text">等待提交文件</p>
+          </div>
+          <p id="summary"></p>
+          <div id="skills" class="skills"></div>
+          <div class="downloads">
+            <!-- <a id="download-docx" href="#">下载 DOCX 报告</a> -->
+            <a id="download-md" href="#">下载 Markdown 报告</a>
+          </div>
+        </section>
+      </section>
+    </main>
+    <script src="/static/app.js"></script>
+  </body>
+</html>
--- a/plant.md
+++ b/plant.md
@@ -0,0 +1,75 @@
+# 内网离线移植步骤
+
+## 1. 迁移原则
+
+不要直接拷贝现有 `.venv` 到内网服务器。Ubuntu 20.04 默认 Python 版本偏低，而本项目要求 `python >= 3.10`，且虚拟环境对路径和系统 ABI 敏感。推荐迁移“项目源代码 + `uv.lock` + 离线 wheel 包 + Python 运行时”。
+
+## 2. 在线准备机打包
+
+在一台可联网机器上完成一次完整解析与下载：
+
+```bash
+uv sync
+uv export --frozen --no-dev -o requirements.txt
+uv pip download -r requirements.txt -d wheelhouse
+```
+
+同时打包以下内容：
+
+- `app/`
+- `scripts/`
+- `tests/`
+- `GJB438C-2021_prd_skills/`
+- `configs/`
+- `pyproject.toml`
+- `uv.lock`
+- `requirements.txt`
+- `wheelhouse/`
+- `uv` 可执行文件（Linux x86_64）
+
+如果目标机没有 Python 3.10+，还要额外准备一个可移植的 Python 3.10/3.12 运行时目录。
+
+## 3. 目标机落地
+
+将打包内容拷贝到 Ubuntu 20.04 内网服务器，例如 `/opt/docx-analyzer`。
+
+```bash
+cd /opt/docx-analyzer
+chmod +x uv
+```
+
+如果系统已有 Python 3.10+：
+
+```bash
+UV_CACHE_DIR=/opt/docx-analyzer/.uv-cache ./uv sync --frozen --offline
+```
+
+如果系统没有满足版本的 Python：
+
+```bash
+./uv venv .venv --python /opt/python/bin/python3.12
+UV_CACHE_DIR=/opt/docx-analyzer/.uv-cache ./uv sync --frozen --offline
+```
+
+## 4. 启动与验证
+
+启动服务：
+
+```bash
+UV_CACHE_DIR=/opt/docx-analyzer/.uv-cache ./uv run uvicorn app.main:app --host 0.0.0.0 --port 8010
+```
+
+验证命令：
+
+```bash
+UV_CACHE_DIR=/opt/docx-analyzer/.uv-cache ./uv run pytest
+curl http://127.0.0.1:8010/
+```
+
+分析样例文件时，确认 `configs/api_config.yaml` 中的 `intranet` 配置指向内网模型地址，且网络可访问。
+
+## 5. 运维建议
+
+- 用 `systemd` 把 `uvicorn` 注册成服务。
+- 把 `uploads/`、`outputs/`、`.uv-cache/` 放在独立目录并定期清理。
+- 首次上线先用 `use_model=false` 验证本地流程，再切到 `intranet` 模型。
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -0,0 +1,25 @@
+[project]
+name = "linux-format-docs-check"
+version = "0.1.0"
+description = "Offline-friendly DOCX compliance analysis web app for GJB438C skill documents."
+readme = "plan.md"
+requires-python = ">=3.10"
+dependencies = [
+    "fastapi>=0.116.0",
+    "jinja2>=3.1.0",
+    "python-docx>=1.1.0",
+    "python-multipart>=0.0.9",
+    "pyyaml>=6.0.0",
+    "requests>=2.32.0",
+    "uvicorn>=0.35.0",
+]
+
+[dependency-groups]
+dev = [
+    "httpx>=0.28.0",
+    "pytest>=8.3.0",
+]
+
+[tool.pytest.ini_options]
+testpaths = ["tests"]
+pythonpath = ["."]
--- a/scripts/analyze_docx.py
+++ b/scripts/analyze_docx.py
@@ -0,0 +1,32 @@
+from __future__ import annotations
+
+import argparse
+import sys
+from pathlib import Path
+
+ROOT_DIR = Path(__file__).resolve().parent.parent
+sys.path.insert(0, str(ROOT_DIR))
+
+from app.main import analyze_saved_docx
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser(description="Analyze a DOCX file and generate downloadable reports.")
+    parser.add_argument("docx", type=Path, help="Path to the DOCX file.")
+    parser.add_argument("--provider", default=None, help="Provider name from configs/api_config.yaml.")
+    parser.add_argument("--use-model", action="store_true", help="Call the configured LLM API.")
+    args = parser.parse_args()
+
+    result = analyze_saved_docx(args.docx, provider=args.provider, use_model=args.use_model)
+    print(f"source: {result['source_filename']}")
+    print(f"summary: {result['summary']}")
+    print("matched_skills:")
+    for skill in result["matched_skills"]:
+        print(f"- {skill}")
+    print("downloads:")
+    print(f"- docx: {result['downloads']['docx']}")
+    print(f"- markdown: {result['downloads']['markdown']}")
+
+
+if __name__ == "__main__":
+    main()
--- a/tests/test_analyzer.py
+++ b/tests/test_analyzer.py
@@ -0,0 +1,71 @@
+from app.analyzer import LLMClient, build_analysis_prompt, select_relevant_skills
+from app.config import ProviderConfig
+from app.docx_parser import ParsedDocument
+from app.skill_loader import Skill
+
+
+class DummyResponse:
+    def raise_for_status(self) -> None:
+        return None
+
+    def json(self) -> dict:
+        return {"choices": [{"message": {"content": "分析结果"}}]}
+
+
+class DummySession:
+    def __init__(self) -> None:
+        self.calls = []
+
+    def post(self, url: str, **kwargs):
+        self.calls.append((url, kwargs))
+        return DummyResponse()
+
+
+def test_select_relevant_skills_prefers_matching_text() -> None:
+    parsed = ParsedDocument(
+        filename="srs.docx",
+        text="软件需求规格说明 CSCI 能力需求 接口需求 合格性规定",
+        paragraphs=[],
+        headings=[],
+        tables=[],
+    )
+    skills = [
+        Skill("requirements", "软件需求规格说明", "需求文档", "编写软件需求", "需求能力接口", None),
+        Skill("deployment", "部署准备", "部署文档", "软件部署交付", "安装部署", None),
+    ]
+
+    selected = select_relevant_skills(parsed, skills, max_skills=1)
+
+    assert [skill.slug for skill in selected] == ["requirements"]
+
+
+def test_llm_client_posts_openai_compatible_payload() -> None:
+    provider = ProviderConfig(
+        api_key="secret",
+        base_url="https://api.deepseek.com/v1/chat/completions",
+        max_tokens=99,
+        model="deepseek-chat",
+        temperature=0.1,
+    )
+    session = DummySession()
+    client = LLMClient(provider, session=session)
+
+    content = client.complete("检查文档")
+
+    assert content == "分析结果"
+    url, kwargs = session.calls[0]
+    assert url == "https://api.deepseek.com/v1/chat/completions"
+    assert kwargs["headers"]["Authorization"] == "Bearer secret"
+    assert kwargs["json"]["model"] == "deepseek-chat"
+    assert kwargs["json"]["messages"][0]["content"] == "检查文档"
+
+
+def test_build_analysis_prompt_contains_document_and_skill() -> None:
+    parsed = ParsedDocument("a.docx", "正文", [], [], [])
+    skill = Skill("s1", "技能一", "描述", "使用条件", "规范内容", None)
+
+    prompt = build_analysis_prompt(parsed, [skill])
+
+    assert "a.docx" in prompt
+    assert "技能一" in prompt
+    assert "符合项" in prompt
--- a/tests/test_config.py
+++ b/tests/test_config.py
@@ -0,0 +1,47 @@
+from pathlib import Path
+
+from app.config import load_api_config
+
+
+def test_load_api_config_selects_default_provider(tmp_path: Path) -> None:
+    config_path = tmp_path / "api_config.yaml"
+    config_path.write_text(
+        """
+default_provider: intranet
+providers:
+  intranet:
+    api_key: EMPTY
+    base_url: http://model.local/v1
+    max_tokens: 1024
+    model: qwen3-coder
+    temperature: 0.2
+""".strip(),
+        encoding="utf-8",
+    )
+
+    settings = load_api_config(config_path)
+
+    assert settings.provider_name == "intranet"
+    assert settings.provider.model == "qwen3-coder"
+    assert settings.provider.chat_completions_url == "http://model.local/v1/chat/completions"
+
+
+def test_load_api_config_accepts_full_chat_completions_url(tmp_path: Path) -> None:
+    config_path = tmp_path / "api_config.yaml"
+    config_path.write_text(
+        """
+default_provider: deepseek
+providers:
+  deepseek:
+    api_key: key
+    base_url: https://api.deepseek.com/v1/chat/completions
+    max_tokens: 4000
+    model: deepseek-chat
+    temperature: 0.7
+""".strip(),
+        encoding="utf-8",
+    )
+
+    settings = load_api_config(config_path, provider_name="deepseek")
+
+    assert settings.provider.chat_completions_url == "https://api.deepseek.com/v1/chat/completions"
--- a/tests/test_docx_parser.py
+++ b/tests/test_docx_parser.py
@@ -0,0 +1,24 @@
+from pathlib import Path
+
+from docx import Document
+
+from app.docx_parser import parse_docx
+
+
+def test_parse_docx_extracts_headings_paragraphs_and_tables(tmp_path: Path) -> None:
+    docx_path = tmp_path / "sample.docx"
+    document = Document()
+    document.add_heading("软件需求规格说明", level=1)
+    document.add_paragraph("本文档描述 CSCI 的能力需求和接口需求。")
+    table = document.add_table(rows=1, cols=2)
+    table.cell(0, 0).text = "需求编号"
+    table.cell(0, 1).text = "REQ-001"
+    document.save(docx_path)
+
+    parsed = parse_docx(docx_path)
+
+    assert parsed.filename == "sample.docx"
+    assert "软件需求规格说明" in parsed.text
+    assert "REQ-001" in parsed.text
+    assert parsed.headings[0].text == "软件需求规格说明"
+    assert parsed.tables[0][0] == ["需求编号", "REQ-001"]
--- a/tests/test_report_generator.py
+++ b/tests/test_report_generator.py
@@ -0,0 +1,29 @@
+from pathlib import Path
+
+from docx import Document
+
+from app.report_generator import AnalysisReport, generate_docx_report, generate_markdown_report
+
+
+def test_generate_reports_create_markdown_and_docx(tmp_path: Path) -> None:
+    report = AnalysisReport(
+        source_filename="sample.docx",
+        provider_name="deepseek",
+        model_name="deepseek-chat",
+        matched_skills=["gjb438c-software-requirements-spec-structure"],
+        summary="部分通过",
+        findings=[{"status": "需整改", "item": "缺少追溯性矩阵", "evidence": "未发现相关章节"}],
+        recommendations=["补充需求双向追溯矩阵。"],
+        raw_model_output="模型输出",
+    )
+
+    md_path = generate_markdown_report(report, tmp_path)
+    docx_path = generate_docx_report(report, tmp_path)
+
+    assert md_path.exists()
+    assert "缺少追溯性矩阵" in md_path.read_text(encoding="utf-8")
+    assert docx_path.exists()
+    parsed = Document(docx_path)
+    text = "\n".join(p.text for p in parsed.paragraphs)
+    assert "DOCX 规范分析报告" in text
+    assert "部分通过" in text
--- a/tests/test_skill_loader.py
+++ b/tests/test_skill_loader.py
@@ -0,0 +1,14 @@
+from pathlib import Path
+
+from app.skill_loader import load_skill_catalog
+
+
+def test_load_skill_catalog_reads_index_and_skill_files() -> None:
+    skills = load_skill_catalog(Path("GJB438C-2021_prd_skills"))
+
+    assert len(skills) >= 30
+    skill_names = {skill.slug for skill in skills}
+    assert "gjb438c-software-requirements-spec-structure" in skill_names
+    target = next(skill for skill in skills if skill.slug == "gjb438c-software-requirements-spec-structure")
+    assert "软件需求规格说明" in target.content
+    assert target.path.name == "SKILL.md"
--- a/tests/test_web.py
+++ b/tests/test_web.py
@@ -0,0 +1,55 @@
+from pathlib import Path
+
+from docx import Document
+
+from app.main import OUTPUT_DIR, ROOT_DIR, analyze_saved_docx
+
+
+def test_index_template_contains_upload_ui() -> None:
+    html = (ROOT_DIR / "app" / "templates" / "index.html").read_text(encoding="utf-8")
+    js = (ROOT_DIR / "app" / "static" / "app.js").read_text(encoding="utf-8")
+
+    assert "DOCX 规范分析" in html
+    assert 'type="file"' in html
+    assert "analysis-progress" in html
+    assert "analysis-status" in html
+    assert "下载 Markdown 报告" in html
+    assert "<!-- <a id=\"download-docx\"" in html
+    assert "download-md" in js
+    assert "pollTask" in js
+
+
+def test_analyze_saved_docx_reports_progress(tmp_path: Path) -> None:
+    updates: list[tuple[int, str]] = []
+    docx_path = tmp_path / "progress.docx"
+    document = Document()
+    document.add_heading("软件需求规格说明", level=1)
+    document.add_paragraph("能力需求、接口需求、合格性规定。")
+    document.save(docx_path)
+
+    payload = analyze_saved_docx(
+        docx_path,
+        provider="deepseek",
+        use_model=False,
+        progress_callback=lambda progress, message: updates.append((progress, message)),
+    )
+
+    assert updates[0][0] == 5
+    assert updates[-1] == (100, "分析完成")
+    assert any("技能" in message for _, message in updates)
+    assert payload["downloads"]["markdown"].endswith(".md")
+
+
+def test_analyze_saved_docx_creates_downloadable_report(tmp_path: Path) -> None:
+    docx_path = tmp_path / "upload.docx"
+    document = Document()
+    document.add_heading("软件需求规格说明", level=1)
+    document.add_paragraph("能力需求、接口需求、合格性规定。")
+    document.save(docx_path)
+
+    payload = analyze_saved_docx(docx_path, provider="deepseek", use_model=False)
+
+    assert payload["source_filename"] == "upload.docx"
+    assert "docx" not in payload["downloads"]
+    assert payload["downloads"]["markdown"].endswith(".md")
+    assert (OUTPUT_DIR / Path(payload["downloads"]["markdown"]).name).exists()
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				`@@ -0,0 +1 @@`
				`"""DOCX compliance analysis application."""`