# server.py
from fastapi import FastAPI
from fastapi_mcp import FastApiMCP
from pydantic import BaseModel
#from memory_store import save_message, load_messages, search_memory
from memory_store import save_message, load_logs, search_memory

app = FastAPI()
mcp = FastApiMCP(app, name="aigpt-agent", description="MCP Server for AI memory")

# --- モデル定義 ---
class ChatInput(BaseModel):
    message: str

class MemoryInput(BaseModel):
    sender: str
    message: str

class MemoryQuery(BaseModel):
    query: str

# --- ツール（エンドポイント）定義 ---
@app.post("/chat", operation_id="chat")
async def chat(input: ChatInput):
    save_message("user", input.message)
    response = f"AI: 「{input.message}」を受け取りました！"
    save_message("ai", response)
    return {"response": response}

@app.post("/memory", operation_id="save_memory")
async def memory_post(input: MemoryInput):
    save_message(input.sender, input.message)
    return {"status": "saved"}

@app.get("/memory", operation_id="get_memory")
async def memory_get():
    return {"messages": load_messages()}

@app.post("/ask_message", operation_id="ask_message")
async def ask_message(input: MemoryQuery):
    results = search_memory(input.query)
    return {
        "response": f"🔎 記憶から {len(results)} 件ヒット:\n" + "\n".join([f"{r['sender']}: {r['message']}" for r in results])
    }

@app.post("/memory/search", operation_id="memory")
async def search_memory(input: dict):
    query = input.get("query", "")
    # 適当なキーワード検索ロジックを追加（例: logs.jsonを検索）
    return {"result": f"記憶の中から「{query}」に関するデータを返しました"}

# --- MCP 初期化 ---
mcp.mount()

if __name__ == "__main__":
    import uvicorn
    print("🚀 Starting MCP server...")
    uvicorn.run(app, host="127.0.0.1", port=5000)