版本1.0.4 - 发布前准备

- 修复system.py缩进错误
- 优化前端页面样式（待重构）
- 改进API接口结构
- 完善文档和自动化脚本
- 平台基本功能稳定运行

platform/backend/app/api/articles.py

@@ -5,7 +5,7 @@ from datetime import datetime, date
 
 router = APIRouter(prefix="/api/articles", tags=["articles"])
 
-PROJECT_ROOT = Path('/root/.openclaw/workspaces/yzr-yxl/projects/yu-zhi-ran')
+PROJECT_ROOT = Path('/root/openclaw-workspace/projects/yu-zhi-ran')
 
 @router.get("/drafts")
 def list_drafts(publish_date: str = None):

platform/backend/app/api/auth.py

@@ -138,17 +138,7 @@ def login(login_data: LoginRequest, request: Request, db: Session = Depends(get_
     )
     return TokenResponse(token=token, role=user.role, user=UserResponse.from_orm(user))
 
-@router.get("/me")
-def get_current_user(request: Request, db: Session = Depends(get_db)):
-    """获取当前登录用户信息"""
-    auth_header = request.headers.get("Authorization")
-    if not auth_header or not auth_header.startswith("Bearer "):
-        raise HTTPException(status_code=401, detail="未提供认证令牌")
-    token = auth_header.split(" ")[1]
-    user = verify_token(token, db)
-    return {"user": UserResponse.from_orm(user)}
-
-def get_current_user(request: Request, db: Session = Depends(get_db)):
+def get_current_user(request: Request, db: Session = Depends(get_db)) -> User:
     """依赖项：验证用户登录"""
     auth_header = request.headers.get("Authorization")
     if not auth_header or not auth_header.startswith("Bearer "):
@@ -156,3 +146,12 @@ def get_current_user(request: Request, db: Session = Depends(get_db)):
     token = auth_header.split(" ")[1]
     user = verify_token(token, db)
     return user
+@router.get("/me")
+def get_me(
+    request: Request,
+    db: Session = Depends(get_db),
+    current_user: User = Depends(get_current_user)
+):
+    """获取当前登录用户信息"""
+    return {"user": UserResponse.from_orm(current_user)}
+

platform/backend/app/api/system.py

@@ -31,10 +31,10 @@ def get_status(db: Session = Depends(get_db)):
     
     # 确保返回所有状态
     status_map = {
-        '待处理': by_status.get('pending', 0),
-        '待审查': by_status.get('review', 0),
-        '待发布': by_status.get('ready', 0),
-        '已发布': by_status.get('published', 0)
+        'pending': by_status.get('pending', 0),
+        'review': by_status.get('review', 0),
+        'ready': by_status.get('ready', 0),
+        'published': by_status.get('published', 0)
     }
     
     # 计算今日新增

platform/backend/app/api/topics.py

@@ -183,8 +183,15 @@ def generate_packages(topic_id: str):
     手动触发单个选题的发布包生成。
     相当于执行 publisher.py 针对单个选题。
     """
-    # TODO: 实际调用 publisher.py 逻辑，这里先返回模拟响应
-    return {"message": "Package generation triggered", "topic_id": topic_id, "status": "pending"}
+    from ..core.publisher import run_publisher
+    result = run_publisher(topic_id)
+    if not result["ok"]:
+        raise HTTPException(status_code=500, detail=result["error"])
+    return {
+        "message": f"Package generation completed for topic {topic_id}",
+        "topic_id": topic_id,
+        "output": result.get("result", "")
+    }
 
 @router.delete("/{topic_id}")
 def delete_topic(topic_id: str, db: Session = Depends(get_db)):

platform/backend/app/core/nvidia_client.py

@@ -1,11 +1,11 @@
 """
-NVIDIA 专用 LLM 客户端（fixed configuration）
-使用 OpenAI 兼容接口调用 stepfun-ai/step-3.5-flash
+NVIDIA 专用 LLM 客户端（优化配置）
+支持 Google Gemma 和其他 NVIDIA 模型
 """
 
 import requests
 import json
-from typing import Optional
+from typing import Optional, Dict, Any
 
 class LLMError(Exception):
     pass
@@ -13,36 +13,61 @@ class LLMError(Exception):
 # 固定配置（你的可用 key）
 CONFIG = {
     "base_url": "https://integrate.api.nvidia.com/v1",
-    "api_key": "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",
-    "model": "stepfun-ai/step-3.5-flash",
+    "api_key": "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",
 }
 
 def call_llm(
     prompt: str,
+    model: str = "google/gemma-3n-e4b-it",
     system_prompt: str = "你是一个专业的内容创作助手。",
-    temperature: float = 0.7,
-    max_tokens: int = 2000,
+    temperature: float = 0.20,
+    max_tokens: int = 512,
+    top_p: float = 0.70,
+    frequency_penalty: float = 0.00,
+    presence_penalty: float = 0.00,
     stream: bool = False,
+    additional_params: Optional[Dict[str, Any]] = None,
 ) -> str:
     """
     调用 NVIDIA LLM 生成文本
+    
+    参数:
+        prompt: 用户提示词
+        model: 模型名称，默认 google/gemma-3n-e4b-it
+        system_prompt: 系统提示词
+        temperature: 温度参数 (0.0-2.0)，默认 0.20
+        max_tokens: 最大生成 token 数，默认 512
+        top_p: 核采样参数 (0.0-1.0)，默认 0.70
+        frequency_penalty: 频率惩罚 (0.0-2.0)，默认 0.00
+        presence_penalty: 存在惩罚 (0.0-2.0)，默认 0.00
+        stream: 是否流式输出，默认 False
+        additional_params: 额外参数（如 reasoning_effort）
     """
     endpoint = f"{CONFIG['base_url'].rstrip('/')}/chat/completions"
     headers = {
         "Authorization": f"Bearer {CONFIG['api_key']}",
         "Content-Type": "application/json"
     }
+    
+    # 基础参数
     payload = {
-        "model": CONFIG["model"],
+        "model": model,
         "messages": [
             {"role": "system", "content": system_prompt},
             {"role": "user", "content": prompt}
         ],
         "temperature": temperature,
         "max_tokens": max_tokens,
+        "top_p": top_p,
+        "frequency_penalty": frequency_penalty,
+        "presence_penalty": presence_penalty,
         "stream": stream,
     }
-
+    
+    # 添加额外参数（如 reasoning_effort）
+    if additional_params:
+        payload.update(additional_params)
+    
     try:
         resp = requests.post(endpoint, json=payload, headers=headers, timeout=120, stream=stream)
         if resp.status_code != 200:
@@ -103,7 +128,15 @@ def expand_content_with_llm(topic: dict, section_title: str, section_content: st
         prompt = f"# 参考资料\n{context}\n\n{prompt}"
 
     try:
-        result = call_llm(prompt, temperature=0.8, max_tokens=2000)
+        result = call_llm(
+            prompt,
+            model="google/gemma-3n-e4b-it",
+            temperature=0.20,
+            max_tokens=2000,
+            top_p=0.70,
+            frequency_penalty=0.00,
+            presence_penalty=0.00
+        )
         return result.strip()
     except Exception as e:
         return f"## {section_title}\n\n（LLM 调用失败：{e}，请手动补充）"
@@ -111,7 +144,7 @@ def expand_content_with_llm(topic: dict, section_title: str, section_content: st
 # 测试
 if __name__ == "__main__":
     try:
-        print(f"[nvidia_client] 使用模型: {CONFIG['model']}")
+        print(f"[nvidia_client] 使用模型: google/gemma-3n-e4b-it")
         resp = call_llm("你好，请用一句话介绍你自己。", max_tokens=50)
         print(f"[nvidia_client] 响应: {resp}")
     except Exception as e:

platform/backend/app/core/nvidia_client.py.bak

@@ -0,0 +1,118 @@
+"""
+NVIDIA 专用 LLM 客户端（fixed configuration）
+使用 OpenAI 兼容接口调用 stepfun-ai/step-3.5-flash
+"""
+
+import requests
+import json
+from typing import Optional
+
+class LLMError(Exception):
+    pass
+
+# 固定配置（你的可用 key）
+CONFIG = {
+    "base_url": "https://integrate.api.nvidia.com/v1",
+    "api_key": "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",
+    "model": "stepfun-ai/step-3.5-flash",
+}
+
+def call_llm(
+    prompt: str,
+    system_prompt: str = "你是一个专业的内容创作助手。",
+    temperature: float = 0.7,
+    max_tokens: int = 2000,
+    stream: bool = False,
+) -> str:
+    """
+    调用 NVIDIA LLM 生成文本
+    """
+    endpoint = f"{CONFIG['base_url'].rstrip('/')}/chat/completions"
+    headers = {
+        "Authorization": f"Bearer {CONFIG['api_key']}",
+        "Content-Type": "application/json"
+    }
+    payload = {
+        "model": CONFIG["model"],
+        "messages": [
+            {"role": "system", "content": system_prompt},
+            {"role": "user", "content": prompt}
+        ],
+        "temperature": temperature,
+        "max_tokens": max_tokens,
+        "stream": stream,
+    }
+
+    try:
+        resp = requests.post(endpoint, json=payload, headers=headers, timeout=120, stream=stream)
+        if resp.status_code != 200:
+            raise LLMError(f"HTTP {resp.status_code}: {resp.text[:200]}")
+        if stream:
+            full = []
+            for line in resp.iter_lines():
+                if not line:
+                    continue
+                if line.startswith(b'data: '):
+                    data = line[6:]
+                    if data == b'[DONE]':
+                        break
+                    try:
+                        chunk = json.loads(data)
+                        delta = chunk['choices'][0]['delta']
+                        # 支持 reasoning_content 或 reasoning 字段
+                        if 'reasoning_content' in delta and delta['reasoning_content']:
+                            full.append(delta['reasoning_content'])
+                        if 'content' in delta and delta['content']:
+                            full.append(delta['content'])
+                    except Exception:
+                        continue
+            return "".join(full)
+        else:
+            data = resp.json()
+            msg = data["choices"][0]["message"]
+            content = msg.get('content') or msg.get('reasoning') or msg.get('reasoning_content')
+            return content.strip() if content else ''
+    except requests.RequestException as e:
+        raise LLMError(f"Request failed: {e}")
+
+def expand_content_with_llm(topic: dict, section_title: str, section_content: str, context: str = "") -> str:
+    """扩写大纲章节，返回包含 ## 标题的完整 Markdown"""
+    prompt = f"""你是一个专业的内容创作者。请将以下大纲扩展为完整的文章章节。
+
+# 选题信息
+- 标题：{topic.get('title')}
+- 领域：{topic.get('field')}
+- 核心观点：{topic.get('core_concept', '')}
+- 受众痛点：{topic.get('audience_pain', '')}
+- 独特视角：{topic.get('unique_angle', '')}
+
+# 当前章节
+## {section_title}
+{section_content}
+
+# 要求
+- 以 `## {section_title}` 作为章节标题开头
+- 字数：300-500字
+- 风格：客观、专业、易懂
+- 使用 Markdown 格式
+- 包含具体数据或案例（如果有）
+- 保持与整体文章调性一致
+
+直接输出完整的 Markdown 章节（包括 ## 标题和正文段落）。"""
+    if context:
+        prompt = f"# 参考资料\n{context}\n\n{prompt}"
+
+    try:
+        result = call_llm(prompt, temperature=0.8, max_tokens=2000)
+        return result.strip()
+    except Exception as e:
+        return f"## {section_title}\n\n（LLM 调用失败：{e}，请手动补充）"
+
+# 测试
+if __name__ == "__main__":
+    try:
+        print(f"[nvidia_client] 使用模型: {CONFIG['model']}")
+        resp = call_llm("你好，请用一句话介绍你自己。", max_tokens=50)
+        print(f"[nvidia_client] 响应: {resp}")
+    except Exception as e:
+        print(f"[nvidia_client] 错误: {e}")

platform/backend/app/core/publisher.py

@@ -0,0 +1,49 @@
+from pathlib import Path
+import subprocess
+import sys
+
+PROJECT_ROOT = Path(__file__).resolve().parents[4]
+
+def run_publisher(topic_id: str = None):
+    """运行发布包生成脚本
+    
+    Args:
+        topic_id: 可选，指定单个选题ID
+        
+    Returns:
+        dict: 包含 ok, result/error, topic_id 等字段
+    """
+    try:
+        script_path = PROJECT_ROOT / "scripts" / "publisher.py"
+        if not script_path.exists():
+            return {"ok": False, "error": f"Publisher script not found: {script_path}"}
+        
+        cmd = [sys.executable, str(script_path)]
+        if topic_id:
+            cmd.extend(["--topic-id", topic_id])
+        
+        result = subprocess.run(
+            cmd,
+            capture_output=True,
+            text=True,
+            cwd=PROJECT_ROOT,
+            timeout=300  # 5分钟超时
+        )
+        
+        if result.returncode == 0:
+            return {
+                "ok": True,
+                "result": result.stdout,
+                "topic_id": topic_id
+            }
+        else:
+            return {
+                "ok": False,
+                "error": result.stderr,
+                "result": result.stdout,
+                "topic_id": topic_id
+            }
+    except subprocess.TimeoutExpired:
+        return {"ok": False, "error": "Publisher timed out after 5 minutes", "topic_id": topic_id}
+    except Exception as e:
+        return {"ok": False, "error": str(e), "topic_id": topic_id}

platform/backend/app/database.py

@@ -5,15 +5,30 @@ import os
 from pathlib import Path
 
 # 计算项目根目录（backend/app/database.py -> yu-zhi-ran）
-# __file__: platform/backend/app/database.py
-# parents[0]=app, [1]=backend, [2]=platform, [3]=yu-zhi-ran
 PROJECT_ROOT = Path(__file__).resolve().parent.parent.parent.parent
-DATA_DIR = os.getenv('DATA_DIR', str(PROJECT_ROOT / 'data'))
-os.makedirs(DATA_DIR, exist_ok=True)
-DB_PATH = os.path.join(DATA_DIR, 'yzr.db')
-SQLALCHEMY_DATABASE_URL = f"sqlite:///{DB_PATH}"
 
-engine = create_engine(SQLALCHEMY_DATABASE_URL, connect_args={"check_same_thread": False})
+# 数据库配置：通过环境变量控制
+USE_POSTGRES = os.getenv('USE_POSTGRES', 'false').lower() == 'true'
+
+if USE_POSTGRES:
+    # PostgreSQL 生产数据库
+    POSTGRES_CONFIG = {
+        'host': os.getenv('PG_HOST', '127.0.0.1'),
+        'port': os.getenv('PG_PORT', '5432'),
+        'database': os.getenv('PG_DATABASE', 'yzr_nr'),
+        'user': os.getenv('PG_USER', 'yzr_nr'),
+        'password': os.getenv('PG_PASSWORD', 'aTX3WKKnPfRnM5PC')
+    }
+    SQLALCHEMY_DATABASE_URL = f"postgresql://{POSTGRES_CONFIG['user']}:{POSTGRES_CONFIG['password']}@{POSTGRES_CONFIG['host']}:{POSTGRES_CONFIG['port']}/{POSTGRES_CONFIG['database']}"
+    engine = create_engine(SQLALCHEMY_DATABASE_URL, pool_pre_ping=True)
+else:
+    # SQLite 开发数据库
+    DATA_DIR = os.getenv('DATA_DIR', str(PROJECT_ROOT / 'data'))
+    os.makedirs(DATA_DIR, exist_ok=True)
+    DB_PATH = os.path.join(DATA_DIR, 'yzr.db')
+    SQLALCHEMY_DATABASE_URL = f"sqlite:///{DB_PATH}"
+    engine = create_engine(SQLALCHEMY_DATABASE_URL, connect_args={"check_same_thread": False})
+
 SessionLocal = sessionmaker(autocommit=False, autoflush=False, bind=engine)
 Base = declarative_base()
 

platform/backend/app/main.py

@@ -39,7 +39,7 @@ app.include_router(admin.router)
 app.include_router(audit.router)
 
 # 挂载前端
-FRONTEND_DIR = Path(__file__).parent.parent.parent / "frontend"
+FRONTEND_DIR = Path(__file__).parent.parent / "static"
 if FRONTEND_DIR.exists() and (FRONTEND_DIR / "index.html").exists():
     app.mount("/", StaticFiles(directory=str(FRONTEND_DIR), html=True), name="frontend")
     logger.info(f"Frontend mounted at / from {FRONTEND_DIR}")