import gradio as gr
import pickle
import numpy as np

class QModel():
    def __init__(self):
        self.qtable = qtab
        
    def predict(self,state_n, n_questions=1):
        n_questions = int(n_questions)
        state_n = int(state_n)
        if (n_questions==1):
            return np.argmax(self.qtable[state_n][:])
        return np.argsort(self.qtable[state_n][:])[-n_questions:]

learner = pickle.load(open("q-learn-multiply-game.pkl", "rb"))

def predict_next_questions(state, n_questions):
    
    return learner.predict(state, n_questions)


intf = gr.Interface(fn=predict_next_questions, inputs=["number","number"], outputs="json")

intf.launch()