238 lines
7.6 KiB
Python
238 lines
7.6 KiB
Python
"""
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A股交易分析系统 - FastAPI 后端服务
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基于现代 Web 架构的全新实现
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"""
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import os
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import sys
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from datetime import datetime
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from typing import Any, Dict, List, Optional
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from fastapi import FastAPI, HTTPException
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from fastapi.middleware.cors import CORSMiddleware
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from pydantic import BaseModel
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# 添加 TradingShared 路径
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SHARED_PATH = os.path.join(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))), 'TradingShared')
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if SHARED_PATH not in sys.path:
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sys.path.insert(0, SHARED_PATH)
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if os.path.join(SHARED_PATH, 'api') not in sys.path:
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sys.path.insert(0, os.path.join(SHARED_PATH, 'api'))
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# 创建 FastAPI 应用
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app = FastAPI(
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title="A股交易分析系统 API",
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description="提供股票分析、智能推荐、竞价排行等功能",
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version="2.0.0"
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)
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# 配置 CORS
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app.add_middleware(
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CORSMiddleware,
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allow_origins=["http://localhost:3000", "http://localhost:5173", "*"],
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allow_credentials=True,
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allow_methods=["*"],
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allow_headers=["*"],
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)
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# ==================== 数据模型 ====================
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class StockInfo(BaseModel):
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code: str
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name: str
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price: Optional[float] = None
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change_percent: Optional[float] = None
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score: Optional[int] = None
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recommendation: Optional[str] = None
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class StockAnalysisRequest(BaseModel):
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stock_code: str
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llm_model: str = "none"
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period: str = "short" # short, medium, long
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class StockAnalysisResponse(BaseModel):
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stock_code: str
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stock_name: str
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technical_score: int
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fundamental_score: int
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combined_score: int
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recommendation: str
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analysis_detail: str
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timestamp: str
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class MarketOverview(BaseModel):
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total_stocks: int
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rising_stocks: int
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falling_stocks: int
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ai_recommendations: int
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last_update: str
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class SystemStatus(BaseModel):
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data_source: str
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kline_data: str
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choice_terminal: str
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ai_model: str
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# ==================== 模拟数据 ====================
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MOCK_STOCKS = [
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{"code": "600519", "name": "贵州茅台", "price": 1856.00, "change_percent": 5.23, "score": 92},
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{"code": "000858", "name": "五粮液", "price": 168.50, "change_percent": 4.87, "score": 88},
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{"code": "601318", "name": "中国平安", "price": 48.20, "change_percent": 3.56, "score": 85},
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{"code": "600036", "name": "招商银行", "price": 35.80, "change_percent": -1.24, "score": 72},
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{"code": "600000", "name": "浦发银行", "price": 8.65, "change_percent": 2.18, "score": 78},
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{"code": "300750", "name": "宁德时代", "price": 215.00, "change_percent": 6.32, "score": 89},
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{"code": "002594", "name": "比亚迪", "price": 268.00, "change_percent": 4.15, "score": 87},
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{"code": "600276", "name": "恒瑞医药", "price": 42.30, "change_percent": -0.85, "score": 75},
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]
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# ==================== API 路由 ====================
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@app.get("/")
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async def root():
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"""API 根路径"""
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return {
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"message": "A股交易分析系统 API",
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"version": "2.0.0",
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"docs": "/docs"
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}
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@app.get("/api/market/overview", response_model=MarketOverview)
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async def get_market_overview():
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"""获取市场概览数据"""
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rising = len([s for s in MOCK_STOCKS if s["change_percent"] > 0])
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falling = len([s for s in MOCK_STOCKS if s["change_percent"] < 0])
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high_score = len([s for s in MOCK_STOCKS if s["score"] >= 80])
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return MarketOverview(
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total_stocks=4396,
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rising_stocks=2847,
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falling_stocks=1243,
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ai_recommendations=86,
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last_update=datetime.now().strftime("%Y-%m-%d %H:%M:%S")
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)
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@app.get("/api/system/status", response_model=SystemStatus)
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async def get_system_status():
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"""获取系统状态"""
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return SystemStatus(
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data_source="正常",
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kline_data="已更新",
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choice_terminal="待连接",
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ai_model="DeepSeek"
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)
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@app.get("/api/stocks/ranking", response_model=List[StockInfo])
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async def get_stock_ranking(limit: int = 10):
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"""获取竞价排行榜"""
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sorted_stocks = sorted(MOCK_STOCKS, key=lambda x: x["change_percent"], reverse=True)
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return [
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StockInfo(
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code=s["code"],
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name=s["name"],
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price=s["price"],
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change_percent=s["change_percent"],
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score=s["score"]
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)
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for s in sorted_stocks[:limit]
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]
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@app.get("/api/stocks/recommendations", response_model=List[StockInfo])
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async def get_recommendations(limit: int = 5):
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"""获取 AI 智能推荐"""
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sorted_stocks = sorted(MOCK_STOCKS, key=lambda x: x["score"], reverse=True)
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recommendations = ["短期强势", "技术突破", "成长潜力", "价值低估", "趋势向好"]
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return [
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StockInfo(
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code=s["code"],
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name=s["name"],
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price=s["price"],
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change_percent=s["change_percent"],
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score=s["score"],
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recommendation=recommendations[i] if i < len(recommendations) else "潜力股"
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)
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for i, s in enumerate(sorted_stocks[:limit])
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]
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@app.get("/api/stocks/{stock_code}", response_model=StockInfo)
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async def get_stock_info(stock_code: str):
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"""获取单只股票信息"""
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stock = next((s for s in MOCK_STOCKS if s["code"] == stock_code), None)
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if not stock:
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raise HTTPException(status_code=404, detail=f"股票 {stock_code} 未找到")
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return StockInfo(
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code=stock["code"],
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name=stock["name"],
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price=stock["price"],
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change_percent=stock["change_percent"],
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score=stock["score"]
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)
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@app.post("/api/stocks/analyze", response_model=StockAnalysisResponse)
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async def analyze_stock(request: StockAnalysisRequest):
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"""分析单只股票"""
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stock = next((s for s in MOCK_STOCKS if s["code"] == request.stock_code), None)
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if not stock:
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# 如果不在模拟数据中，返回一个默认分析
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stock = {"code": request.stock_code, "name": f"股票{request.stock_code}", "score": 75}
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# 模拟分析结果
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technical_score = min(100, stock.get("score", 75) + 5)
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fundamental_score = max(0, stock.get("score", 75) - 5)
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combined_score = stock.get("score", 75)
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period_map = {
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"short": "短期（1-5天）",
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"medium": "中期（1-4周）",
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"long": "长期（1-6个月）"
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}
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return StockAnalysisResponse(
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stock_code=request.stock_code,
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stock_name=stock["name"],
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technical_score=technical_score,
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fundamental_score=fundamental_score,
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combined_score=combined_score,
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recommendation="买入" if combined_score >= 80 else "持有" if combined_score >= 60 else "观望",
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analysis_detail=f"基于{period_map.get(request.period, '短期')}分析，该股票技术面评分{technical_score}分，"
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f"基本面评分{fundamental_score}分，综合评分{combined_score}分。"
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f"当前市场表现{'强势' if combined_score >= 80 else '稳健' if combined_score >= 60 else '偏弱'}。",
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timestamp=datetime.now().strftime("%Y-%m-%d %H:%M:%S")
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)
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@app.get("/api/llm/models")
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async def get_llm_models():
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"""获取可用的 LLM 模型列表"""
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return {
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"models": [
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{"id": "none", "name": "无 AI 分析", "status": "available"},
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{"id": "deepseek", "name": "DeepSeek", "status": "available"},
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{"id": "minimax", "name": "MiniMax", "status": "available"},
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{"id": "openrouter", "name": "OpenRouter", "status": "available"},
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{"id": "gemini", "name": "Gemini", "status": "available"},
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],
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"default": "deepseek"
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}
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if __name__ == "__main__":
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import uvicorn
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uvicorn.run(app, host="0.0.0.0", port=8000)
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