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from enum import Enum
from typing import Any
import gradio as gr
from constants.app_constants import PAPER, ROCK, SCISSORS
from factory.game_strategy_factory import GameStrategyFactory
from factory.hand_factory import HandFactory
from hands.hand_interface import Hand
hand_labels = [ROCK, PAPER, SCISSORS]
# Use hand names to add logic for the game
# Define a User enum
class User(Enum):
HUMAN = 1
AI = 2
from fastai.vision.all import * # type: ignore
learn = load_learner("export.pkl")
class RockPaperScissors:
def __init__(self):
self.hand_factory: HandFactory = HandFactory(GameStrategyFactory())
def getRCPWinner(self, human_hand_image_path) -> str:
human_hand_image = PILImage.create(human_hand_image_path)
user_hand = self.__get_hand_for_user(User.HUMAN, human_hand_image) # type: ignore
ai_hand = self.__get_hand_for_user(User.AI, human_hand_image) # type: ignore
winner = user_hand.getWinner(ai_hand)
return f"<p>I have {ai_hand.getName()} <br> {winner}</p>"
def __get_hand_for_user(
self,
user: User,
hand_image: PILImage,
) -> Hand:
if user == User.AI:
return self.hand_factory.get_hand(random.choice(hand_labels)) # type: ignore
else:
pred = learn.predict(hand_image)
return self.hand_factory.get_hand(pred[0])
if __name__ == "__main__":
title = "Rock Paper Scissors AI"
description = "A simple rock paper scissors game with AI"
examples = [
"example_rock.png",
"example_scissors.png",
"example_paper2.png",
]
interpretation = "default"
enable_queue = True
webcam = gr.inputs.Image(shape=(640, 480), source="webcam")
rock_paper_scissors = RockPaperScissors()
gr.Interface(
fn=rock_paper_scissors.getRCPWinner,
inputs=webcam,
outputs=gr.outputs.HTML(),
title=title,
description=description,
examples=examples,
interpretation=interpretation,
enable_queue=enable_queue,
).launch()
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