from otree.api import (
    models,
    widgets,
    BaseConstants,
    BaseSubsession,
    BaseGroup,
    BasePlayer,
    Currency as c,
    currency_range,
)
# Imports for Neural Network
import pandas as pd
from sklearn.neural_network import MLPRegressor
import joblib
import random


author = 'Your name here'

doc = """
Your app description
"""
###### Load Neural Networks for inference
# loaded_model_test = joblib.load(r'C:\Users\Robin\Dropbox\Eigene Dateien_Robin IISM\06 Dissertation\13 P3\02 Erhebung Orga\01 oTree\02 oTree Python\testproject\TestNN_vw2.pkl')

def make_field(label):
    return models.IntegerField(
        choices=[1,2,3,4,5,6,7],
        label=label,
        widget=widgets.RadioSelectHorizontal,
    )

class Constants(BaseConstants):
    name_in_url = 'testapp'
    players_per_group = None
    num_rounds = 1


class Subsession(BaseSubsession):
    pass


class Group(BaseGroup):
    pass


class Player(BasePlayer):
    # Random Szenario Variable 1 bis 4
    exp_zelle = models.IntegerField()

    rat1 = make_field("Finanzentscheidungen treffe ich gerne sachlich, durch Analyse von Vor- und Nachteilen, nicht nach Gefühl.")
    ration_superior = models.IntegerField(widget=widgets.RadioSelect, choices=[1, 2, 3, 4, 5, 6, 7])
    emostab1 = models.IntegerField(widget=widgets.RadioSelect, choices=[1, 2, 3, 4, 5, 6, 7])

    # Algo
    pred_vw2 = models.FloatField()

    def nnet_vw2(self):
        ## Create Input Vector for specific player
        columns = [
            'rat1', 'ration_superior',
            'emostab1'
        ]
        features = {
            'rat1': self.rat1,
            'ration_superior': self.ration_superior,
            'emostab1': self.emostab1
        }
        # Create the Input Vector for prediction (as a Dataframe)
        features_vector = pd.DataFrame([features], columns=columns)
        ## Make Prediction
        pred = loaded_model_test.predict(features_vector)
        # If smaller 0, make it 0.01; if larger make it 9.99
        # pred_final = max(0.01, min(round(pred[0], 2), 9.99))
        pred_final = pred[0]
        ## Save Output in player class variable
        self.pred_vw2 = pred_final
        return(pred_final)
    def rnd(self):
        zelle = random.randint(1, 4)
        self.exp_zelle = zelle
        offer = zelle + 10
        return(offer)