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aCogSphere
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import huggingface_hub
import requests
from gradio_client import Client
client = Client("https://cogsphere-acogsphere.hf.space/")
#client = Client("https://cognitivescience-acogspherea.hf.space/--replicas/k5l86/")
#result = client.predict(
#		fn_index=1
#)
#print(result)


from python_actr import *
log=log()

class RPSChoice(Model):
    choice=None
    font='Arial 20'
    waiting=True

    def start(self):
        self.visible=True
        self.text=self.instructions
    
    def choose(self,option):
        if not self.waiting: return
        if option not in ['rock','paper','scissors']: return
        self.choice=option
        self.visible=False
        self.waiting=False

        # check to see if both players have made a choice
        if self.parent.choice1.choice is not None and self.parent.choice2.choice is not None:
            self.parent.determine_winner()            

    def reset(self):
        self.text=self.instructions
        self.waiting=True
        self.visible=True
        self.choice=None


class RockPaperScissors(Model):
    choice1=RPSChoice(x=0.5,y=0.2,instructions='Choose: Rock(1) Paper(2) Scissors(3)')
    choice2=RPSChoice(x=0.5,y=0.8,instructions='Choose: Rock(Z) Paper(X) Scissors(C)')

    result=Model(x=0.5,y=0.5,visible=False)

    score1=Model(text=0,x=0.9,y=0.1)
    score2=Model(text=0,x=0.9,y=0.9)

    trials=0

    def key_pressed(self,key):
        if key=='1': self.choice1.choose('rock')
        if key=='2': self.choice1.choose('paper')
        if key=='3': self.choice1.choose('scissors')
        
        if key=='z': self.choice2.choose('rock')
        if key=='x': self.choice2.choose('paper')
        if key=='c': self.choice2.choose('scissors')

    def determine_winner(self):
        self.choice1.text=self.choice1.choice
        self.choice2.text=self.choice2.choice
        self.choice1.visible=True
        self.choice2.visible=True

        c1=self.choice1.choice
        c2=self.choice2.choice
        if c1==c2:
            self.result.text="Tie!"
        elif (c1=='rock' and c2=='scissors') or (c1=='paper' and c2=='rock') or (c1=='scissors' and c2=='paper'):
            self.result.text="Player 1 wins!"
            self.score1.text+=1
        else:
            self.result.text="Player 2 wins!"
            self.score2.text+=1
        self.result.visible=True
            

        yield 1

        self.result.visible=False
        
        self.choice1.reset()
        self.choice2.reset()

        self.trials+=1
        if self.trials>=100:
            scora=self.score1.text
            scorb=self.score2.text
            result = client.predict(
		"ACT-R RPS"
        "100",		
		"Model 1: " + str(scora) + " Model 2: " + str(scorb),
		fn_index=0)
            log.score1=self.score1.text
            log.score2=self.score2.text
            self.stop()
        

#from ccm.lib.actr import *
class ProceduralPlayer(ACTR):
    goal=Buffer()
    goal.set('play rps')

    def play_rock(goal='play rps',choice='waiting:True'):
        choice.choose('rock')
    def play_paper(goal='play rps',choice='waiting:True'):
        choice.choose('paper')
    def play_scissors(goal='play rps',choice='waiting:True'):
        choice.choose('scissors')

#env=RockPaperScissors()
#env.model1=ProceduralPlayer()
#env.model1.choice=env.choice1
#env.model2=ProceduralPlayer()
#env.model2.choice=env.choice2
#env.run()
#log_data = env.log
#return log_data[0]['score1']