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from fastapi import FastAPI, HTTPException |
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from fastapi.responses import HTMLResponse |
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from fastapi.staticfiles import StaticFiles |
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import numpy as np |
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from tensorflow import keras |
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import json |
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import os |
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app = FastAPI(root_path="/spaces/labapawel/tictactoe") |
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app.mount("/static", StaticFiles(directory="public"), name="static") |
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@app.get("/", response_class=HTMLResponse) |
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async def read_root(): |
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try: |
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with open("public/index.html", "r") as file: |
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return HTMLResponse(content=file.read(), status_code=200) |
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except FileNotFoundError: |
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return HTMLResponse(content="Plik index.html nie został znaleziony", status_code=404) |
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class TicTacToeAI: |
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def __init__(self, model_path="model/model.keras"): |
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self.model_path = model_path |
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self.model = None |
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self.load_model() |
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def load_model(self): |
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"""Wczytuje model z pliku""" |
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if os.path.exists(self.model_path): |
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self.model = keras.models.load_model(self.model_path) |
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return True |
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return False |
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def get_move(self, board): |
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"""Zwraca najlepszy ruch dla danego stanu planszy""" |
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if self.model is None: |
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raise ValueError("Model nie został wczytany") |
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board_array = np.array(board) |
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predictions = self.model.predict(board_array.reshape(1, -1), verbose=0)[0] |
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valid_moves = np.where(board_array == 0)[0] |
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if len(valid_moves) == 0: |
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raise ValueError("Brak dostępnych ruchów") |
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valid_predictions = [(i, pred) for i, pred in enumerate(predictions) if i in valid_moves] |
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return int(max(valid_predictions, key=lambda x: x[1])[0]) |
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ai = TicTacToeAI() |
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@app.post("/move") |
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async def get_move(data: dict): |
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""" |
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Endpoint do wykonania ruchu AI |
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""" |
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try: |
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board = data.get("board") |
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if not board: |
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raise HTTPException(status_code=400, detail="Brak planszy w żądaniu") |
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if len(board) != 9 or not all(x in [0, 1, -1] for x in board): |
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raise HTTPException(status_code=400, detail="Nieprawidłowe dane planszy") |
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move = ai.get_move(board) |
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return {"status": "success", "move": move} |
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except Exception as e: |
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raise HTTPException(status_code=500, detail=str(e)) |
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@app.post("/game") |
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async def add_game(data: dict): |
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""" |
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Endpoint do dodania gry do pliku |
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""" |
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try: |
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board = data.get("board") |
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win = data.get("win") |
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if not board or win is None: |
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raise HTTPException(status_code=400, detail="Brak planszy lub informacji o wygranej w żądaniu") |
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if len(board) != 9 or not all(x in [0, 1, -1] for x in board): |
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raise HTTPException(status_code=400, detail="Nieprawidłowe dane planszy") |
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if os.path.exists("gry.json"): |
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with open("gry.json", "r") as file: |
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games_data = json.load(file) |
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else: |
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games_data = [] |
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games_data.append({"board": board, "win": win}) |
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with open("gry.json", "w") as file: |
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json.dump(games_data, file) |
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return {"status": "success"} |
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except Exception as e: |
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raise HTTPException(status_code=500, detail=str(e)) |
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@app.get("/status") |
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async def get_status(): |
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"""Sprawdza status modelu""" |
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return { |
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"status": "success", |
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"model_loaded": ai.model is not None, |
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"model_path": ai.model_path, |
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} |
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