from otree.api import *
import random
import pickle
from datetime import datetime
import pandas as pd

c = cu

doc = """
Your app description
"""

class C(BaseConstants):
    NAME_IN_URL = 'risk_attitude'
    PLAYERS_PER_GROUP = None
    NUM_ROUNDS = 1
    NUM_TRIES = 2
    exchange_rate = 0.02


class Subsession(BaseSubsession):
    pass

class Group(BaseGroup):
    pass

class Player(BasePlayer):
    decision_round_one = models.IntegerField(min=0, max=149, label="Für welche Option entscheiden Sie sich? Bitte geben Sie die Nummer der Lotterie an.")
    decision_round_two = models.IntegerField(choices=[[0, "I"],
                                                      [2, "II"],
                                                      [4, "III"],
                                                      [6, "IV"],
                                                      [9, "V"],
                                                      [11, "VI"],
                                                      [15, "VII"],
                                                      [18, "VIII"],
                                                      [22, "IX"],
                                                      [25, "X"],
                                                      [27, "XI"]], label="Für welche Option entscheiden Sie sich? Bitte geben Sie die Nummer der Lotterie an.")
    risk_level_revealed = models.IntegerField()
    risk_prediction_full = models.IntegerField(min=0, max=3)
    risk_prediction_decfs = models.IntegerField(min=0, max=3)
    unfold_list_tracker = models.BooleanField(blank=True)
    #lottery_selected = models.IntegerField()
    #lottery_red = models.BooleanField()
    #lottery_understanding = models.IntegerField(min=0)
    consent = models.BooleanField()
    treatment = models.StringField()
    tries_left = models.IntegerField(initial=C.NUM_TRIES)
    quiz1 = models.IntegerField(label='Wie viele Auszahlungsmöglichkeiten beinhaltet eine Lotterie?',
                                choices=[1,2,3])
    quiz2 = models.IntegerField(label='Welchen Zweck erfüllt das KI-System?',
                               choices=[[1,"Vorhersage Ihrer Hilfsbereitschaft"],
                                        [2,'Vorhersage Ihrer Risikobereitschaft'],
                                        [3,'Vorhersage Ihres Alters']])

    BDM = models.IntegerField(min=0, max=2500, label="Bitte geben Sie an, wie viele Punkte Sie für die Nutzung des KI-Systems ausgeben würden")
    BDM_result = models.BooleanField()

## Prediction variables todo

    # Participants' descriptives
    # Alter
    age = models.IntegerField(label="Ihr Alter:", min=18, max=99, blank=False)
    sex = models.IntegerField(choices=[[1, "Weiblich"], [0, "Männlich"]],widget=widgets.RadioSelect, label="Ihr Geschlecht:", blank=False)
    germborn = models.IntegerField(choices=[[0, 'Nicht in Deutschland geboren'], [1, 'In Deutschland geboren']], label="Sind Sie in Deutschland geboren?", widget=widgets.RadioSelect)
    height = models.IntegerField(label="Ihre Körpergröße <b>in Centimeter</b>", min=100, max=250, blank=False)
    income = models.IntegerField(choices=[[0, '< 1000 €'],[1, '1001 - 1500 €'],[2, '1501 - 2000 €'],
                                          [3, '2001 - 2500 €'],[4, '2501 - 3000 €'],[5, '3001 - 3500 €'],
                                          [6, '3501 - 4000 €'],[7, '4001 - 4500 €'],[8, '4501 - 5000 €'],
                                          [9, '> 5000 €']], label="Ihr monatliches Bruttoeinkommen in Euro:", blank=False)
    education = models.IntegerField(label="Die Gesamtdauer Ihrer Ausbildung in Jahre (Beginn ab der 1. Schulklasse)"
                                          "<br><b>Beispiel 1:</b> Sie haben für 13 Jahre die Schule besucht und anschließend 5 Jahre studiert: Gesamtdauer = 18 Jahre"
                                          "<br><b>Beispiel 2:</b> Sie haben für 8 Jahre die Schule besucht und anschließend eine 3 jährige Ausbildung absolviert: Gesamtdauer = 11 Jahre", blank=False)
    sat_socialLife = models.IntegerField(choices=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10], label="Ihren Freunden und Bekannten?", widget=widgets.RadioSelectHorizontal, blank=False)
    sat_health = models.IntegerField(choices=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10], label="Ihrer Gesundheit?", widget=widgets.RadioSelectHorizontal, blank=False)
    importance_religion = models.IntegerField(choices=[[4, "Ganz unwichtig"], [3, "Weniger wichtig"], [2, "Wichtig"], [1,"Sehr wichtig"]],
                                              label="Glaube und Religion", widget=widgets.RadioSelectHorizontal, blank=False)
    importance_involvement = models.IntegerField(choices=[[4, 'ganz unwichtig'], [3, 'Weniger wichtig'], [2, 'Wichtig'], [1,'Sehr wichtig']],
                                                 label="Politisch und gesellschaftlich engagiert sein", widget=widgets.RadioSelectHorizontal, blank=False)
    check_account = models.IntegerField(choices= [[5, "Nie"], [4, "Seltener als 1 mal pro Monat"], [3, "Mindestens 1 mal pro Monat"], [2, "Mindestens 1 mal pro Woche"], [1, "Täglich"]],
                                              label="Häufigkeit der Überprüfung des Kontostands:",
                                              widget=widgets.RadioSelectHorizontal, blank=False)
    alcohol = models.IntegerField(choices=[[6, "Nie"], [5, "Einmal im Monat oder seltener"], [4, "An zwei bis vier Tagen im Monat"], [3, "An zwei bis drei Tagen in der Woche"],
                                           [2, "An vier bis sechs Tagen in der Woche"], [1, "Täglich"]], label="Häufigkeit des Verzehrs alkoholischer Getränke",
                                              widget=widgets.RadioSelect, blank=False)
    smoke = models.IntegerField(choices=[[1, "Ja"], [0, "Nein"]],widget=widgets.RadioSelect, label="Rauchen Sie derzeit, egal ob Zigaretten, Pfeifen oder Zigarren?", blank=False)

    # - - - - - - - - - - - - - - - - - - - - - -

    # Dummy variables
    age_dummy       = models.IntegerField(choices=[[0, 'Bereitstellen'],[1, 'Zurückhalten']], label="Alter", widget=widgets.RadioSelect, blank=True)###
    sex_dummy       = models.IntegerField(choices=[[0, 'Bereitstellen'],[1, 'Zurückhalten']], label="Geschlecht", widget=widgets.RadioSelect, blank=False)###
    height_dummy    = models.IntegerField(choices=[[0, 'Bereitstellen'],[1, 'Zurückhalten']], label="Körpergröße", widget=widgets.RadioSelect, blank=False)###
    germborn_dummy  = models.IntegerField(choices=[[0, 'Bereitstellen'],[1, 'Zurückhalten']], label="In Deutschland geboren", widget=widgets.RadioSelect, blank=False)###
    income_dummy    = models.IntegerField(choices=[[0, 'Bereitstellen'],[1, 'Zurückhalten']], label="Monats-Bruttoeinkommen", widget=widgets.RadioSelect, blank=False)###
    education_dummy       = models.IntegerField(choices=[[0, 'Bereitstellen'],[1, 'Zurückhalten']], label="Ausbildungs-Jahre", widget=widgets.RadioSelect, blank=False)###
    sat_socialLife_dummy  = models.IntegerField(choices=[[0, 'Bereitstellen'],[1, 'Zurückhalten']], label="Zufriedenheit mit Freunden und Bekannten", widget=widgets.RadioSelect, blank=False)###
    sat_health_dummy = models.IntegerField(choices=[[0, 'Bereitstellen'],[1, 'Zurückhalten']], label="Zufriedenheit mit Ihrer Gesundheit", widget=widgets.RadioSelect, blank=False)
    importance_religion_dummy = models.IntegerField(choices=[[0, 'Bereitstellen'], [1, 'Zurückhalten']],label="Wichtigkeit der Religion / des Glaubens", widget=widgets.RadioSelect, blank=False)###
    importance_involvement_dummy = models.IntegerField(choices=[[0, 'Bereitstellen'], [1, 'Zurückhalten']],label="Wichtigkeit politisch/gesellschaftlich engagiert zu sein", widget=widgets.RadioSelect, blank=False)
    check_account_dummy = models.IntegerField(choices=[[0, 'Bereitstellen'], [1, 'Zurückhalten']],label="Prüfung des Kontos",widget=widgets.RadioSelect, blank=False)###
    alcohol_dummy = models.IntegerField(choices=[[0, 'Bereitstellen'], [1, 'Zurückhalten']],label="Verzehr alkoholischer Getränke", widget=widgets.RadioSelect, blank=False)###
    smoke_dummy = models.IntegerField(choices=[[0, 'Bereitstellen'], [1, 'Zurückhalten']],label="Rauchen", widget=widgets.RadioSelect, blank=False)

    # Feature evaluations
    age_evaluation = models.IntegerField(choices=[1, 2, 3, 4, 5], widget=widgets.RadioSelect, label="Alter", blank=False)
    sex_evaluation = models.IntegerField(choices=[1, 2, 3, 4, 5], widget=widgets.RadioSelect, label="Geschlecht", blank=False)
    height_evaluation = models.IntegerField(choices=[1, 2, 3, 4, 5], label="Körpergröße", widget=widgets.RadioSelect, blank=False)  ###
    germborn_evaluation = models.IntegerField(choices=[1, 2, 3, 4, 5], label="In Deutschland geboren", widget=widgets.RadioSelect, blank=False)  ###
    income_evaluation = models.IntegerField(choices=[1, 2, 3, 4, 5],
                                       label="Monats-Bruttoeinkommen", widget=widgets.RadioSelect, blank=False)  ###
    education_evaluation = models.IntegerField(choices=[1, 2, 3, 4, 5],
                                          label="Ausbildungs-Jahre", widget=widgets.RadioSelect, blank=False)  ###
    sat_socialLife_evaluation = models.IntegerField(choices=[1, 2, 3, 4, 5],
                                               label="Zufriedenheit mit Freunden und Bekannten",
                                               widget=widgets.RadioSelect, blank=False)  ###
    sat_health_evaluation = models.IntegerField(choices=[1, 2, 3, 4, 5],
                                           label="Zufriedenheit mit Ihrer Gesundheit", widget=widgets.RadioSelect,
                                           blank=False)
    importance_religion_evaluation = models.IntegerField(choices=[1, 2, 3, 4, 5],
                                                    label="Wichtigkeit der Religion / des Glaubens",
                                                    widget=widgets.RadioSelect, blank=False)  ###
    importance_involvement_evaluation = models.IntegerField(choices=[1, 2, 3, 4, 5],
                                                       label="Wichtigkeit politisch/gesellschaftlich engagiert zu sein",
                                                       widget=widgets.RadioSelect, blank=False)
    check_account_evaluation = models.IntegerField(choices=[1, 2, 3, 4, 5],
                                              label="Prüfung des Kontos", widget=widgets.RadioSelect, blank=False)  ###
    alcohol_evaluation = models.IntegerField(choices=[1, 2, 3, 4, 5],
                                        label="Verzehr alkoholischer Getränke", widget=widgets.RadioSelect,
                                        blank=False)  ###
    smoke_evaluation = models.IntegerField(choices=[1, 2, 3, 4, 5], label="Rauchen",
                                      widget=widgets.RadioSelect, blank=False)

    ### ------ Secondary measure
    # cognitive trust
    algo_expert = models.IntegerField(choices=list(range(1, 8)),
                                      label="Das KI-System ist ein Experte in der Vorhersage der Risikobereitschaft",
                                      widget=widgets.RadioSelect) # original: his RA is a real expert in assessing products.
    algo_knowledge = models.IntegerField(choices=list(range(1, 8)),
                                         label="Das KI-System hat gute Kenntnisse über die Risikobereitschaft der Teilnehmer",
                                         widget=widgets.RadioSelect) # This RA has good knowledge about products.

    # emotional trust
    feel_secure = models.IntegerField(choices=list(range(1, 8)),
                                      label="Ich fühle mich sicher, wenn ich mich bei meiner Lotterie-Entscheidung auf das KI-System verlasse.",
                                      widget=widgets.RadioSelect) #  I feel secure about relying on this RA for my decision.
    feel_comfortable = models.IntegerField(choices=list(range(1, 8)),
                                           label="Ich fühle mich wohl, wenn ich mich bei meiner Lotterie-Entscheidung auf das KI-System verlasse.",
                                           widget=widgets.RadioSelect)
    feel_content = models.IntegerField(choices=list(range(1, 8)),
                                       label="Ich fühle mich zufrieden, wenn ich mich bei meiner Lotterie-Entscheidung auf das KI-System verlasse.",
                                       widget=widgets.RadioSelect)

    # Transparency
    transparency = models.IntegerField(choices=list(range(1, 8)),
                                       label="Ich verstehe, wie das KI-System die Risikobereitschaft vorhersagt.",
                                       widget=widgets.RadioSelect)

    # Control measures (ATAI)
    ATAI_fear = models.IntegerField(choices=list(range(1,8)), widget=widgets.RadioSelect, label="Ich habe Angst vor künstlicher Intelligenz.", blank=False)
    ATAI_trust = models.IntegerField(choices=list(range(1,8)), widget=widgets.RadioSelect, label="Ich vertraue künstlicher Intelligenz.", blank=False)
    ATAI_destroy_humankind = models.IntegerField(choices=list(range(1,8)), widget=widgets.RadioSelect, label="Künstliche Intelligenz wird die Menschheit zerstören.", blank=False)
    ATAI_benefit = models.IntegerField(choices=list(range(1,8)), widget=widgets.RadioSelect, label="Künstliche Intelligenz wird eine Bereicherung für die Menschheit sein.", blank=False)
    ATAI_job_loss = models.IntegerField(choices=list(range(1,8)), widget=widgets.RadioSelect, label="Künstliche Intelligenz wird für viel Arbeitslosigkeit sorgen.", blank=False)

    # Perceived acc
    perceived_accuracy = models.IntegerField(min=0, max=100)
    perceived_rmse = models.IntegerField(min=0, max=10)

    # Power citation: Spiekermann, S. (2007). Perceived control: Scales for privacy in ubiquitous computing. In Digital Privacy (pp. 289-304). Auerbach Publications.
    power = models.IntegerField(choices=list(range(1, 8)), widget=widgets.RadioSelect,
                               label="Ich habe das Gefühl, dass ich das KI-Systems in eine Richtung lenken kann, die ich für richtig halte",
                               blank=False)

    spec_endowment = models.IntegerField(initial=2500)
    comments = models.LongStringField(blank=True)
    seed = models.IntegerField()
    tries_left = models.IntegerField(initial=C.NUM_TRIES)

# Functions
ml_model = pickle.load(open('risk_model_multi.sav', 'rb'))

def predict_risk_full(player: Player):  # Predict the fairness/reciprocity of dictator based on all attributes
    # Initialize each dummy with 0; extent the number and names of dummies according to the used features
    # Create the input for the ML model; consists of (1) questionnaire attr. and (2) dummies=0
    input_obs_dict = pd.DataFrame({"age": player.age,
                                   "education": player.education,
                                   "height": player.height,
                                   "sex": player.sex,
                                   "germborn": player.germborn,
                                   "smoke": player.smoke,
                                   "sat_health": player.sat_health,
                                   "sat_socialLife": player.sat_socialLife,
                                   "importance_religion": player.importance_religion,
                                   "check_account": player.check_account,
                                   "alcohol": player.alcohol,
                                   "importance_involvement": player.importance_involvement,
                                   "income": player.income,
                                   # ----- Dummies --------
                                   "age_dummy": 0,
                                   "education_dummy": 0,
                                   "height_dummy":0,
                                   "sex_dummy": 0,
                                   "germborn_dummy": 0,
                                   "smoke_dummy": 0,
                                   "sat_health_dummy": 0,
                                   "sat_socialLife_dummy": 0,
                                   "importance_religion_dummy": 0,
                                   "check_account_dummy": 0,
                                   "alcohol_dummy": 0,
                                   "importance_involvement_dummy":0,
                                   "income_dummy": 0
                                   },
                                  index=[0])

    input_obs = pd.DataFrame(input_obs_dict)  # Convert dict to DataFrame (only 1 row since we look at each single participant)
    player.risk_prediction_full = int(ml_model.predict(input_obs))  # Perform the prediction

def set_seed(player: Player):
    player.seed = random.randint(1,10000)

def predict_risk_decfs(player: Player):
    dummies = [player.height_dummy, player.age_dummy, player.sex_dummy, player.germborn_dummy, player.income_dummy, player.education_dummy,
               player.sat_socialLife_dummy, player.sat_health_dummy, player.importance_religion_dummy,
               player.importance_involvement_dummy, player.check_account_dummy, player.alcohol_dummy, player.smoke_dummy]

    player_vars = [player.height, player.age, player.sex, player.germborn, player.income, player.education,
                   player.sat_socialLife, player.sat_health,
                   player.importance_religion, player.importance_involvement,
                   player.check_account, player.alcohol, player.smoke]

    participant = player.participant

    par_fields = ["height","age", "sex", "germborn", "income", "education",
                  "sat_socialLife", "sat_health",
                  "importance_religion","importance_involvement",
                  "check_account", "alcohol", 'smoke']
    """
    dict_medians = {'age': 45.0, 'bgbilzeit': 11.5, 'labgro16': 2200.0,
                    'sex': 0.0, 'germborn': 1.0, 'bgp0108': 8.0,
                    'bgp0106': 7.0, 'bgp0112': 6.0, 'bgp05': 5.0,
                    'bgp0111': 8.0, 'bgp0610': 2.0, 'bgp0702': 3.0, 'bgp115': 5.0}
    """
    for dummy, var, par_field in zip(dummies, player_vars, par_fields):

        if dummy == 1:
            participant.vars[par_field] = -1
        else:
            participant.vars[par_field] = var

    input_obs_dict = pd.DataFrame({"age": participant.age,
                                   "education": participant.education,
                                   "height": participant.height,
                                   "sex": participant.sex,
                                   "germborn": participant.germborn,
                                   "smoke": participant.smoke,
                                   "sat_health": participant.sat_health,
                                   "sat_socialLife": participant.sat_socialLife,
                                   "importance_religion": participant.importance_religion,
                                   "check_account": participant.check_account,
                                   "alcohol": participant.alcohol,
                                   "importance_involvement": participant.importance_involvement,
                                   "income": participant.income,
                                   # ----- Dummies --------
                                   "age_dummy": player.age_dummy,
                                   "education_dummy": player.education_dummy,
                                   "height_dummy": player.height_dummy,
                                   "sex_dummy": player.sex_dummy,
                                   "germborn_dummy": player.germborn_dummy,
                                   "smoke_dummy": player.smoke_dummy,
                                   "sat_health_dummy": player.sat_health_dummy,
                                   "sat_socialLife_dummy": player.sat_socialLife_dummy,
                                   "importance_religion_dummy": player.importance_religion_dummy,
                                   "check_account_dummy": player.check_account_dummy,
                                   "alcohol_dummy": player.alcohol_dummy,
                                   "importance_involvement_dummy":player.importance_involvement_dummy,
                                   "income_dummy": player.income_dummy
                                   },
                                  index=[0])

    input_obs = pd.DataFrame(input_obs_dict)
    player.risk_prediction_decfs = int(ml_model.predict(input_obs))

def creating_session(subsession): # Assigns the experimental groups; itertools.cycle ensures that we have 50/50 distribution of treatment groups
    import itertools
    treatments = itertools.cycle(['baseline', 'treatment'])
    x = 1
    for player in subsession.get_players():
        player.treatment=next(treatments)
        print('player', x, 'is in condition:', player.treatment)
        x += 1
    subsession.group_randomly()

def start_timestamp(player: Player):
    participant = player.participant
    participant.vars['start_time'] = datetime.now()

def end_timestamp(player: Player):
    participant = player.participant
    participant.vars['end_time'] = datetime.now()
    completion_time = participant.vars['end_time'] - participant.vars['start_time']
    participant.vars['completion_time'] = completion_time.seconds

def create_lottery(p1, v1, v2):
    parameter_list = [p1, v1, v2]
    expected_value = p1 * v1 + (1 - p1) * v2

    if (v1 != 1) and (v2 != 1):
        lottery = f"Mit einer Wahrscheinlichkeit von {p1 * 100}% erhalten Sie {v1} Punkte. Mit einer Wahrscheinlichkeit von {(1 - p1) * 100}% erhalten Sie {v2} Punkte."
    elif (v1 == 1) and (v2 != 1):
        lottery = f"Mit einer Wahrscheinlichkeit von {p1 * 100}% erhalten Sie {v1} Punkt. Mit einer Wahrscheinlichkeit von {(1 - p1) * 100}% erhalten Sie {v2} Punkte."
    elif (v1 != 1) and (v2 == 1):
        lottery = f"Mit einer Wahrscheinlichkeit von {p1 * 100}% erhalten Sie {v1} Punkte. Mit einer Wahrscheinlichkeit von {(1 - p1) * 100}% erhalten Sie {v2} Punkt."

    return lottery, parameter_list, expected_value

def personalize_lotteries_risk(player: Player):
    lottery_A_list = pickle.load(open('lotteries_A.sav', 'rb'))
    personalised_lotteries = []
    if player.treatment == 'baseline':
        for lottery in lottery_A_list:
            if lottery['risk_level'] == player.risk_prediction_full:
                ID_found = lottery['ID']
    elif player.treatment == 'treatment':
        for lottery in lottery_A_list:
            if lottery['risk_level'] == player.risk_prediction_decfs:
                ID_found = lottery['ID']

    if ID_found == 0:
        ID_presented = [0, 1, 2, 3, 4]
    elif ID_found == 1:
        ID_presented = [0, 1, 2, 3, 4]
    else:
        ID_presented = [ID_found - 2,
                        ID_found - 1,
                        ID_found,
                        ID_found + 1,
                        ID_found + 2]

    for lottery in lottery_A_list:
        if lottery['ID'] in ID_presented:
            personalised_lotteries.append(lottery)
    return personalised_lotteries

def extract_risk_level(player: Player):
    lottery_A_list = pickle.load(open('lotteries_A.sav', 'rb'))
    player.risk_level_revealed = lottery_A_list[player.decision_round_two]["risk_level"]

def BDM_decision(player: Player):
    random_threshold = random.randint(0,1000)
    player.BDM_result = 1 if player.BDM >= random_threshold else 0

def Calc_spec_endowment(player:Player):
    if player.BDM_result == 1:
        player.spec_endowment = player.spec_endowment - player.BDM
    if player.BDM_result == 0:
        player.spec_endowment = player.spec_endowment

def lottery_payoff_sum(player:Player):
    random.seed(player.seed)
    lotteriesAB = pickle.load(open('lotteries_AB.sav', 'rb'))
    lottery_one_selected = lotteriesAB[player.decision_round_one]
    lottery_one_payoff = random.choices([lottery_one_selected['value1'], lottery_one_selected['value2']],
                                        weights = (lottery_one_selected['prob'], 1-lottery_one_selected['prob']))[0]
    lottery_one_selected_text = lottery_one_selected['text']

    lotteriesA = pickle.load(open('lotteries_A.sav', 'rb'))
    lottery_two_selected = lotteriesA[player.decision_round_two]
    lottery_two_payoff = random.choices([lottery_two_selected['value1'], lottery_two_selected['value2']],
                                        weights=(lottery_two_selected['prob'], 1 - lottery_two_selected['prob']))[0]
    lottery_two_selected_text = lottery_two_selected['text']

    lottery_payoff_both = int(lottery_one_payoff + lottery_two_payoff)


    return dict(lottery_one_payoff=lottery_one_payoff,
                lottery_one_selected_text=lottery_one_selected_text,
                lottery_two_payoff=lottery_two_payoff,
                lottery_two_selected_text=lottery_two_selected_text,
                lottery_payoff_both=lottery_payoff_both)

# PAGES
class Introduction(Page):
    def before_next_page(player: Player, timeout_happened):
        start_timestamp(player)
        set_seed(player)

class questionnaire(Page):
    form_model = 'player'
    form_fields = ["age", "education","height", "sex", "germborn", "smoke",
                  "sat_health","sat_socialLife","importance_religion",
                   "check_account","alcohol","importance_involvement","income"]
    @staticmethod
    def before_next_page(player: Player, timeout_happened):
        predict_risk_full(player)
        player.unfold_list_tracker = 0

    @staticmethod
    def vars_for_template(player: Player):
        descriptives = ["age", "sex", "height", "germborn", "income", "education"]
        Likert_vars_10 = ["sat_socialLife","sat_health"]
        importances = ["importance_religion","importance_involvement"]
        miscellaneous = ["smoke","alcohol","check_account"]
        return dict(descriptives=descriptives,
                    Likert_vars_10=Likert_vars_10)

class Explanation_of_experiment(Page):
    pass

class Lotteries_round_two(Page):
    form_model = "player"
    form_fields = ["decision_round_two"]
    def vars_for_template(player: Player):
        lottery_A_list = pickle.load(open('lotteries_A.sav', 'rb'))
        lotteries_repr = []
        for lottery in lottery_A_list:
            if lottery['representative'] == True:
                lotteries_repr.append(lottery)
        return {"lotteries_repr": lotteries_repr}

    @staticmethod
    def before_next_page(player: Player, timeout_happened):
        extract_risk_level(player)

#class Lotteries_round_two_result(Page):
#    form_model = "player"
#    def vars_for_template(player: Player):
#       lottery_A_list = pickle.load(open('lotteries_A.sav', 'rb'))
#       lottery_two_selected = lottery_A_list[player.decision_round_two-1]['text']
#       return {"lottery_two_selected": lottery_two_selected}

class Lotteries_round_one_presentation(Page):
    form_model = "player"
    #form_fields = ["decision_round_one"]
    def vars_for_template(player: Player):
        lotteries = pickle.load(open('lotteries_AB.sav', 'rb'))
        return {"lotteries": lotteries}

class Explanation_of_AI(Page):
    pass

class Decentralized_feature_selection(Page):
    form_model = 'player'
    form_fields = ["age_dummy", "sex_dummy", "height_dummy", "germborn_dummy", "income_dummy", "education_dummy",
                  "sat_socialLife_dummy", "sat_health_dummy",
                  "importance_religion_dummy","importance_involvement_dummy",
                  "check_account_dummy", "alcohol_dummy", 'smoke_dummy'] #todo: Bei neuen features anpassen!

    @staticmethod
    def is_displayed(player: Player):
        return (player.treatment == 'treatment')

    @staticmethod
    def before_next_page(player: Player, timeout_happened):
        predict_risk_decfs(player)

class BDM(Page):
    form_model = 'player'
    form_fields= ['BDM']
    @staticmethod
    def before_next_page(player: Player, timeout_happened):
        BDM_decision(player)
        Calc_spec_endowment(player)

class Variable_evaluation(Page):
    form_model = 'player'
    form_fields = ["age_evaluation", "sex_evaluation", "height_evaluation", "germborn_evaluation", "income_evaluation",
                   "education_evaluation", "sat_health_evaluation", "sat_socialLife_evaluation", "importance_religion_evaluation", "check_account_evaluation",
                   "importance_involvement_evaluation", "smoke_evaluation",
                   "alcohol_evaluation"]  # todo: Bei neuen features anpassen!

class BDM_result(Page):
    form_model = 'player'

class Lotteries_round_one_with_AI(Page):
    form_model = "player"
    form_fields = ["decision_round_one", "unfold_list_tracker"]
    def vars_for_template(player: Player):
        lotteries_one_personalized = personalize_lotteries_risk(player)
        lotteries_one_full = pickle.load(open('lotteries_AB.sav', 'rb'))
        return {"lotteries_one_personalized":lotteries_one_personalized,
                "lotteries_one_full":lotteries_one_full}

    @staticmethod
    def is_displayed(player: Player):
        return (player.BDM_result == 1)


class Lotteries_round_one_without_AI(Page):
    form_model = "player"
    form_fields = ["decision_round_one"]
    def vars_for_template(player: Player):
        lotteries = pickle.load(open('lotteries_AB.sav', 'rb'))
        return {"lotteries": lotteries}

    @staticmethod
    def is_displayed(player: Player):
        return (player.BDM_result == 0)

class Lotteries_round_one_result(Page):
    form_model = "player"
    def vars_for_template(player: Player):
        lotteries = pickle.load(open('lotteries_AB.sav', 'rb'))
        lottery_one_selected = lotteries[player.decision_round_one]['text']
        return {"lottery_one_selected": lottery_one_selected}

class Elicitation_of_model_beliefs(Page):
    form_model="player"
    form_fields= ["algo_expert", "algo_knowledge",
                   "feel_secure", "feel_comfortable", "feel_content",
                   "transparency", "power"]

class Perceived_accuracy(Page):
    form_model = "player"
    form_fields = ["perceived_accuracy"]

class Perceived_rmse(Page):
    form_model = "player"
    form_fields = ["perceived_rmse"]

class Attitudes_towards_ai(Page):
    form_model="player"
    form_fields=["ATAI_fear", "ATAI_trust", "ATAI_destroy_humankind", "ATAI_benefit", "ATAI_job_loss"]

class Lotteries_round_two_result(Page):
    form_model = "player"
    def vars_for_template(player: Player):
        lottery_A_list = pickle.load(open('lotteries_A.sav', 'rb'))
        lotteries_repr = []
        for lottery in lottery_A_list:
            if (lottery['representative'] == True) and (lottery['ID'] == player.decision_round_two):
                lottery_two_selected = lottery['text']
        return {"lottery_two_selected": lottery_two_selected}

class Results(Page):
    form_model = 'player'
    def vars_for_template(player: Player):

        payoff_list = lottery_payoff_sum(player)
        lottery_one_payoff        = payoff_list["lottery_one_payoff"]
        lottery_one_selected_text = payoff_list["lottery_one_selected_text"]
        lottery_two_payoff        = payoff_list["lottery_two_payoff"]
        lottery_two_selected_text = payoff_list["lottery_two_selected_text"]
        lottery_payoff_both       = payoff_list["lottery_payoff_both"] + player.spec_endowment
        payoff_euro               = round((lottery_payoff_both * C.exchange_rate/100),2)

        return{"lottery_one_payoff":lottery_one_payoff,
               "lottery_one_selected_text":lottery_one_selected_text,
               "lottery_two_payoff":lottery_two_payoff,
               "lottery_two_selected_text":lottery_two_selected_text,
               "lottery_payoff_both":lottery_payoff_both,
               "payoff_euro":payoff_euro}

class Final_comments(Page):
    form_model = 'player'
    form_fields = ['comments']

class Control_page(Page):
    form_model = 'player'
    form_fields = ['quiz1', 'quiz2']

    @staticmethod
    def error_message(player, values):
        if player.tries_left > 0:
            # Add correct solutions to questions in dict below:
            solutions = dict(
                quiz1=2,
                quiz2=2
            )

            error_messages = dict()

            for field_name in solutions:
                if values[field_name] != solutions[field_name]:
                    error_messages = 'Mindestens eine falsche Antwort. Sie haben nur noch einen Versuch!'

            if bool(error_messages):
                player.tries_left -= 1

            return error_messages

    @staticmethod
    def app_after_this_page(player, upcoming_apps):
        if player.tries_left == 0:
            return 'exit_app'


page_sequence = [Introduction,
                 questionnaire,
                 Explanation_of_experiment,
                 Lotteries_round_one_presentation,
                 Explanation_of_AI,
                 Control_page,
                 # attention check
                 Decentralized_feature_selection,
                 Variable_evaluation,
                 BDM,
                 BDM_result, #todo Seiteninhalt
                 Lotteries_round_one_without_AI,
                 Lotteries_round_one_with_AI,
                 Lotteries_round_one_result,
                 Elicitation_of_model_beliefs,
                 Attitudes_towards_ai,
                 Lotteries_round_two,
                 Lotteries_round_two_result,
                 Perceived_accuracy,
                 Perceived_rmse,
                 Results,
                 Final_comments
                 ]