from fastapi import FastAPI, HTTPException
from fastapi.responses import HTMLResponse
from fastapi.staticfiles import StaticFiles
import numpy as np
from tensorflow import keras
import json
import os


# Ustawienie prefiksu aplikacji
app = FastAPI(root_path="/spaces/labapawel/tictactoe")

# Montowanie katalogu publicznego
app.mount("/static", StaticFiles(directory="public"), name="static")

@app.get("/", response_class=HTMLResponse)
async def read_root():
    try:
        with open("public/index.html", "r") as file:
            return HTMLResponse(content=file.read(), status_code=200)
    except FileNotFoundError:
        return HTMLResponse(content="Plik index.html nie został znaleziony", status_code=404)



class TicTacToeAI:
    def __init__(self, model_path="model/model.keras"):
        self.model_path = model_path
        self.model = None
        self.load_model()

    def load_model(self):
        """Wczytuje model z pliku"""
        if os.path.exists(self.model_path):
            self.model = keras.models.load_model(self.model_path)
            return True
        return False

    def get_move(self, board):
        """Zwraca najlepszy ruch dla danego stanu planszy"""
        if self.model is None:
            raise ValueError("Model nie został wczytany")

        board_array = np.array(board)
        predictions = self.model.predict(board_array.reshape(1, -1), verbose=0)[0]
        valid_moves = np.where(board_array == 0)[0]

        if len(valid_moves) == 0:
            raise ValueError("Brak dostępnych ruchów")

        valid_predictions = [(i, pred) for i, pred in enumerate(predictions) if i in valid_moves]
        return int(max(valid_predictions, key=lambda x: x[1])[0])


# Inicjalizacja AI
ai = TicTacToeAI()

@app.post("/move")
async def get_move(data: dict):
    """
    Endpoint do wykonania ruchu AI
    """
    try:
        board = data.get("board")

        if not board:
            raise HTTPException(status_code=400, detail="Brak planszy w żądaniu")

        if len(board) != 9 or not all(x in [0, 1, -1] for x in board):
            raise HTTPException(status_code=400, detail="Nieprawidłowe dane planszy")

        move = ai.get_move(board)

        return {"status": "success", "move": move}
    except Exception as e:
        raise HTTPException(status_code=500, detail=str(e))

@app.post("/game")
async def add_game(data: dict):
    """
    Endpoint do dodania gry do pliku
    """
    try:
        board = data.get("board")
        win = data.get("win")

        if not board or win is None:
            raise HTTPException(status_code=400, detail="Brak planszy lub informacji o wygranej w żądaniu")

        if len(board) != 9 or not all(x in [0, 1, -1] for x in board):
            raise HTTPException(status_code=400, detail="Nieprawidłowe dane planszy")

        if os.path.exists("gry.json"):
            with open("gry.json", "r") as file:
                games_data = json.load(file)
        else:
            games_data = []

        games_data.append({"board": board, "win": win})

        with open("gry.json", "w") as file:
            json.dump(games_data, file)

        return {"status": "success"}
    except Exception as e:
        raise HTTPException(status_code=500, detail=str(e))


@app.get("/status")
async def get_status():
    """Sprawdza status modelu"""
    return {
        "status": "success",
        "model_loaded": ai.model is not None,
        "model_path": ai.model_path,
    }