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2026-04-22 11:31:11 +08:00
parent 2f420e3348
commit 61633b21b4
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+# FastAPI 应用主入口
+from fastapi import FastAPI, Request
+from fastapi.responses import HTMLResponse, JSONResponse, StreamingResponse, FileResponse
+from fastapi.staticfiles import StaticFiles
+from fastapi.templating import Jinja2Templates
+import sys
+import os
+import json
+import subprocess
+import uuid
+import asyncio
+import zipfile
+from datetime import datetime
+
+# 创建 FastAPI 应用实例
+app = FastAPI(title="Alpha Transformer", version="1.0.0")
+
+# Chrome DevTools 健康检查端点（可选，阻止 404 日志）
+@app.get("/.well-known/appspecific/com.chrome.devtools.json")
+async def chrome_devtools_check():
+    """Chrome DevTools 健康检查"""
+    return {"status": "ok"}
+
+# 提供模板目录中的 CSS 和 JS 文件
+@app.get("/styles.css")
+async def get_styles():
+    """提供 styles.css 文件"""
+    from fastapi.responses import FileResponse
+    return FileResponse("templates/styles.css")
+
+@app.get("/app.js")
+async def get_app_js():
+    """提供 app.js 文件"""
+    from fastapi.responses import FileResponse
+    return FileResponse("templates/app.js")
+
+# 配置 Jinja2 模板引擎，用于渲染 HTML 页面
+templates = Jinja2Templates(directory="templates")
+
+# 存储正在运行的任务信息（task_id -> 任务状态）
+transformer_tasks = {}
+
+# 全局配置变量
+app_config = {}
+
+def load_config():
+    """
+    加载配置文件
+    读取 config.json 文件，如果有则加载，否则返回空字典
+    """
+    config_path = os.path.join(os.path.dirname(__file__), 'config.json')
+    if os.path.exists(config_path):
+        try:
+            with open(config_path, 'r', encoding='utf-8') as f:
+                config = json.load(f)
+                print(f"✓ 已加载配置文件: {config_path}")
+                return config
+        except Exception as e:
+            print(f"⚠ 加载配置文件失败: {e}")
+            return {}
+    else:
+        print(f"⚠ 配置文件不存在: {config_path}")
+        return {}
+
+# 启动时加载配置
+app_config = load_config()
+
+
+@app.get("/", response_class=HTMLResponse)
+async def home():
+    # 读取并返回前端首页 HTML
+    with open("templates/index.html", "r", encoding="utf-8") as f:
+        return f.read()
+
+
+@app.get("/api/config/defaults")
+async def get_config_defaults():
+    """
+    获取默认配置
+    返回 config.json 中的配置作为表单默认值
+    """
+    return JSONResponse(content={
+        "success": True,
+        "config": app_config
+    })
+
+
+@app.post("/api/config/save")
+async def save_config(request: Request):
+    """
+    保存配置到 config.json
+    用于在页面上修改配置后保存
+    """
+    try:
+        data = await request.json()
+        
+        # 更新全局配置
+        global app_config
+        app_config = data
+        
+        # 写入文件
+        config_path = os.path.join(os.path.dirname(__file__), 'config.json')
+        with open(config_path, 'w', encoding='utf-8') as f:
+            json.dump(data, f, indent=4, ensure_ascii=False)
+        
+        return JSONResponse(content={
+            "success": True,
+            "message": "配置已保存"
+        })
+    except Exception as e:
+        return JSONResponse(
+            status_code=500,
+            content={"success": False, "error": str(e)}
+        )
+
+
+@app.post("/api/generate")
+async def generate_alpha(request: Request):
+    """
+    生成 Alpha 变种的 API 端点
+    接收前端表单数据，启动 Transformer 脚本执行 Alpha 生成任务
+    """
+    print("=" * 50)
+    print("收到生成变种请求")
+    
+    try:
+        # 解析请求数据
+        data = await request.json()
+        print(f"请求数据: alpha_id={data.get('alpha_id')}, llm_model={data.get('llm_model')}")
+        
+        # 生成唯一任务 ID
+        task_id = str(uuid.uuid4())
+        print(f"生成任务 ID: {task_id}")
+
+        # 定义必须提交的字段
+        required_fields = [
+            "alpha_id",
+            "llm_api_key",
+            "llm_base_url",
+            "llm_model",
+            "brain_username",
+            "brain_password"
+        ]
+
+        # 检查必填字段是否完整
+        for field in required_fields:
+            if not data.get(field):
+                print(f"缺少必填字段: {field}")
+                return JSONResponse(
+                    status_code=400,
+                    content={"success": False, "error": f"Missing required field: {field}"}
+                )
+
+        # 获取脚本所在目录和 Transformer 子目录
+        script_dir = os.path.dirname(os.path.abspath(__file__))
+        transformer_dir = os.path.join(script_dir, 'Tranformer')
+        print(f"Transformer 目录: {transformer_dir}")
+
+        # 构建传递给 Transformer 脚本的配置
+        config = {
+            "LLM_model_name": data.get('llm_model'),
+            "LLM_API_KEY": data.get('llm_api_key'),
+            "llm_base_url": data.get('llm_base_url'),
+            "username": data.get('brain_username'),
+            "password": data.get('brain_password'),
+            "alpha_id": data.get('alpha_id'),
+            "top_n_datafield": int(data.get('top_n_datafield', 50)),
+            "user_region": data.get('user_region'),
+            "user_universe": data.get('user_universe'),
+            "user_delay": int(data.get('user_delay')) if data.get('user_delay') else None,
+            "user_category": data.get('user_category'),
+            "user_data_type": data.get('user_data_type', 'MATRIX')
+        }
+        print(f"配置已构建: LLM_model={config['LLM_model_name']}, alpha_id={config['alpha_id']}")
+
+        # 将配置写入临时 JSON 文件，供 Transformer 脚本读取
+        config_path = os.path.join(transformer_dir, f'config_{task_id}.json')
+        with open(config_path, 'w', encoding='utf-8') as f:
+            json.dump(config, f, indent=4)
+        print(f"配置文件已写入: {config_path}")
+
+        try:
+            # 启动 Transformer.py 子进程执行 Alpha 生成
+            print(f"启动 Transformer 脚本...")
+            process = subprocess.run(
+                [sys.executable, '-u', os.path.join(transformer_dir, 'Transformer.py'), config_path],
+                cwd=transformer_dir,
+                capture_output=True,
+                text=True,
+                timeout=600,
+                env={**os.environ, "PYTHONIOENCODING": "utf-8"}
+            )
+            print(f"Transformer 脚本执行完成，返回码: {process.returncode}")
+
+            # 定义输出文件路径
+            output_file = os.path.join(transformer_dir, 'output', 'Alpha_generated_expressions_success.json')
+            candidates_file = os.path.join(transformer_dir, 'output', 'Alpha_candidates.json')
+            error_file = os.path.join(transformer_dir, 'output', 'Alpha_generated_expressions_error.json')
+
+            # 构建响应数据
+            result = {
+                "success": True,
+                "alpha_id": data.get('alpha_id'),
+                "stdout": process.stdout,
+                "stderr": process.stderr,
+                "return_code": process.returncode
+            }
+
+            # 读取成功生成的表达式
+            if os.path.exists(output_file):
+                print(f"读取成功表达式文件: {output_file}")
+                with open(output_file, 'r', encoding='utf-8') as f:
+                    result['expressions_success'] = json.load(f)
+            else:
+                print(f"成功表达式文件不存在: {output_file}")
+                result['expressions_success'] = []
+
+            # 读取候选表达式
+            if os.path.exists(candidates_file):
+                print(f"读取候选表达式文件: {candidates_file}")
+                with open(candidates_file, 'r', encoding='utf-8') as f:
+                    result['candidates'] = json.load(f)
+            else:
+                print(f"候选表达式文件不存在: {candidates_file}")
+                result['candidates'] = []
+
+            # 读取生成失败的表达式
+            if os.path.exists(error_file):
+                print(f"读取错误表达式文件: {error_file}")
+                with open(error_file, 'r', encoding='utf-8') as f:
+                    result['expressions_error'] = json.load(f)
+            else:
+                print(f"错误表达式文件不存在: {error_file}")
+                result['expressions_error'] = []
+
+            print(f"成功: {len(result['expressions_success'])} 个, 候选: {len(result['candidates'])} 个, 错误: {len(result['expressions_error'])} 个")
+            print("=" * 50)
+            return JSONResponse(content=result)
+
+        finally:
+            # 清理临时配置文件
+            if os.path.exists(config_path):
+                os.remove(config_path)
+                print(f"已清理临时配置文件: {config_path}")
+
+    except subprocess.TimeoutExpired:
+        print("任务执行超时 (600秒)")
+        return JSONResponse(
+            status_code=408,
+            content={"success": False, "error": "Task timeout (600s)"}
+        )
+    except Exception as e:
+        print(f"执行异常: {str(e)}")
+        return JSONResponse(
+                status_code=500,
+                content={"success": False, "error": str(e)}
+            )
+
+
+@app.post("/api/transformer/login-and-fetch-options")
+async def login_and_fetch_options(request: Request):
+    """
+    登录 BRAIN 并获取地区、Delay、Universe、类别等选项
+    用于填充高级选项表单
+    """
+    try:
+        data = await request.json()
+        username = data.get('username')
+        password = data.get('password')
+        
+        if not username or not password:
+            return JSONResponse(
+                status_code=400,
+                content={'success': False, 'error': 'Username and password are required'}
+            )
+        
+        # 添加 Transformer 目录到 sys.path
+        script_dir = os.path.dirname(os.path.abspath(__file__))
+        transformer_dir = os.path.join(script_dir, 'Tranformer')
+        if transformer_dir not in sys.path:
+            sys.path.append(transformer_dir)
+        
+        # 导入必要的模块
+        from Tranformer.ace_lib import SingleSession, get_instrument_type_region_delay
+        import pandas as pd
+        
+        # 创建新的会话实例
+        session = SingleSession()
+        session.auth = (username, password)
+        
+        brain_api_url = "https://api.worldquantbrain.com"
+        response = session.post(brain_api_url + "/authentication")
+        
+        if response.status_code == 201:
+            # 认证成功
+            pass
+        elif response.status_code == 401:
+            return JSONResponse(
+                status_code=401,
+                content={'success': False, 'error': 'Authentication failed: Invalid credentials'}
+            )
+        else:
+            return JSONResponse(
+                status_code=400,
+                content={'success': False, 'error': f'Authentication failed: {response.status_code}'}
+            )
+        
+        # 获取 region/delay/universe 选项
+        df = get_instrument_type_region_delay(session)
+        
+        # 获取数据类别
+        categories_resp = session.get(brain_api_url + "/data-categories")
+        categories = []
+        if categories_resp.status_code == 200:
+            categories_data = categories_resp.json()
+            if isinstance(categories_data, list):
+                categories = categories_data
+            elif isinstance(categories_data, dict):
+                categories = categories_data.get('results', [])
+        
+        # 转换 DataFrame 为前端需要的嵌套字典结构
+        # 结构: Region -> Delay -> Universe
+        df_equity = df[df['InstrumentType'] == 'EQUITY']
+        
+        options = {}
+        for _, row in df_equity.iterrows():
+            region = row['Region']
+            delay = row['Delay']
+            universes = row['Universe']  # 这是一个列表
+            
+            if region not in options:
+                options[region] = {}
+            
+            # 将 delay 转换为字符串作为字典的键
+            delay_str = str(delay)
+            if delay_str not in options[region]:
+                options[region][delay_str] = universes
+        
+        return JSONResponse(content={
+            'success': True,
+            'options': options,
+            'categories': categories
+        })
+        
+    except Exception as e:
+        print(f"登录获取选项失败: {str(e)}")
+        return JSONResponse(
+            status_code=500,
+            content={'success': False, 'error': str(e)}
+        )
+
+
+@app.get("/api/health")
+async def health_check():
+    """健康检查端点，用于验证服务是否正常运行"""
+    return {"status": "healthy", "service": "alpha-transformer"}
+
+
+@app.get("/api/download/{alpha_id}")
+async def download_results(alpha_id: str):
+    """
+    下载生成结果的 zip 压缩包
+    包含三个 JSON 文件：success, candidates, error
+    """
+    try:
+        transformer_dir = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'Tranformer')
+        output_dir = os.path.join(transformer_dir, 'output')
+        
+        # 检查文件是否存在
+        files_to_zip = {
+            'Alpha_generated_expressions_success.json': os.path.join(output_dir, 'Alpha_generated_expressions_success.json'),
+            'Alpha_candidates.json': os.path.join(output_dir, 'Alpha_candidates.json'),
+            'Alpha_generated_expressions_error.json': os.path.join(output_dir, 'Alpha_generated_expressions_error.json')
+        }
+        
+        # 生成时间戳
+        timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+        zip_filename = f"{alpha_id}_{timestamp}.zip"
+        zip_path = os.path.join(output_dir, zip_filename)
+        
+        # 创建 zip 文件
+        with zipfile.ZipFile(zip_path, 'w', zipfile.ZIP_DEFLATED) as zipf:
+            for arcname, filepath in files_to_zip.items():
+                if os.path.exists(filepath):
+                    zipf.write(filepath, arcname)
+        
+        # 返回文件
+        return FileResponse(
+            zip_path,
+            media_type='application/zip',
+            filename=zip_filename
+        )
+        
+    except Exception as e:
+        return JSONResponse(
+            status_code=500,
+            content={"success": False, "error": str(e)}
+        )
+
+
+if __name__ == "__main__":
+    # 启动 FastAPI 应用，使用 uvicorn 作为 ASGI 服务器
+    import uvicorn
+    uvicorn.run(app, host="0.0.0.0", port=8000)