app.py

import os 
HF_TOKEN = os.getenv("HF_TOKEN")

import numpy as np
import pandas as pd 
import sklearn
import sklearn.metrics
from sklearn.metrics import roc_auc_score, roc_curve, precision_recall_curve, auc, precision_score, recall_score, f1_score, classification_report, accuracy_score, confusion_matrix, ConfusionMatrixDisplay, matthews_corrcoef
from sklearn.model_selection import train_test_split
from sklearn.calibration import calibration_curve
from math import sqrt
from scipy import stats as st
from random import randrange
from matplotlib import pyplot as plt
import xgboost as xgb
import lightgbm as lgb
import catboost as cb
from catboost import Pool
from sklearn.ensemble import RandomForestClassifier
import optuna
from optuna.samplers import TPESampler
import shap
import gradio as gr
import random
import re
import textwrap
from datasets import load_dataset


#Read data.
x1 = load_dataset("mertkarabacak/TQP-atEDH", data_files="mortality_data_train.csv", use_auth_token = HF_TOKEN)
x1 = pd.DataFrame(x1['train'])
variables1 = ['Age', 'Sex', 'Ethnicity', 'Weight', 'Height', 'Systolic_Blood_Pressure', 'Pulse_Rate', 'Supplemental_Oxygen', 'Pulse_Oximetry', 'Respiratory_Assistance', 'Respiratory_Rate', 'Temperature', 'GCS__Eye', 'GCS__Verbal', 'GCS__Motor', 'Total_GCS', 'Pupillary_Response', 'Midline_Shift', 'Bleeding_Localization', 'Bleeding_Size', 'Current_Smoker', 'Alcohol_Use_Disorder', 'Substance_Abuse_Disorder', 'Diabetes_Mellitus', 'Hypertension', 'Congestive_Heart_Failure', 'History_of_Myocardial_Infarction', 'Angina_Pectoris', 'History_of_Cerebrovascular_Accident', 'Peripheral_Arterial_Disease', 'Chronic_Obstructive_Pulmonary_Disease', 'Chronic_Renal_Failure', 'Cirrhosis', 'Bleeding_Disorder', 'Disseminated_Cancer', 'Currently_Receiving_Chemotherapy_for_Cancer', 'Dementia', 'Attention_Deficit_Disorder_or_Attention_Deficit_Hyperactivity_Disorder', 'Mental_or_Personality_Disorder', 'Ability_to_Complete_AgeAppropriate_ADL', 'Pregnancy', 'Anticoagulant_Therapy', 'Steroid_Use', 'Days_from_Incident_to_ED_or_Hospital_Arrival', 'Transport_Mode', 'InterFacility_Transfer', 'Trauma_Type', 'Injury_Intent', 'Mechanism_of_Injury', 'WorkRelated', 'Blood_Transfusion', 'Neurosurgical_Intervention', 'Alcohol_Screen', 'Alcohol_Screen_Result', 'Drug_Screen__Amphetamine', 'Drug_Screen__Barbiturate', 'Drug_Screen__Benzodiazepines', 'Drug_Screen__Cannabinoid', 'Drug_Screen__Cocaine', 'Drug_Screen__MDMA_or_Ecstasy', 'Drug_Screen__Methadone', 'Drug_Screen__Methamphetamine', 'Drug_Screen__Opioid', 'Drug_Screen__Oxycodone', 'Drug_Screen__Phencyclidine', 'Drug_Screen__Tricyclic_Antidepressant', 'ACS_Verification_Level', 'Hospital_Type', 'Facility_Bed_Size', 'Primary_Method_of_Payment', 'Race', 'Protective_Device', 'Cerebral_Monitoring', 'OUTCOME']
x1 = x1[variables1]

x2 = load_dataset("mertkarabacak/TQP-atEDH", data_files="discharge_data_train.csv", use_auth_token = HF_TOKEN)
x2 = pd.DataFrame(x2['train'])
variables2= ['Age', 'Sex', 'Ethnicity', 'Weight', 'Height', 'Systolic_Blood_Pressure', 'Pulse_Rate', 'Supplemental_Oxygen', 'Pulse_Oximetry', 'Respiratory_Assistance', 'Respiratory_Rate', 'Temperature', 'GCS__Eye', 'GCS__Verbal', 'GCS__Motor', 'Total_GCS', 'Pupillary_Response', 'Midline_Shift', 'Bleeding_Localization', 'Bleeding_Size', 'Current_Smoker', 'Alcohol_Use_Disorder', 'Substance_Abuse_Disorder', 'Diabetes_Mellitus', 'Hypertension', 'Congestive_Heart_Failure', 'History_of_Myocardial_Infarction', 'Angina_Pectoris', 'History_of_Cerebrovascular_Accident', 'Peripheral_Arterial_Disease', 'Chronic_Obstructive_Pulmonary_Disease', 'Chronic_Renal_Failure', 'Cirrhosis', 'Bleeding_Disorder', 'Disseminated_Cancer', 'Currently_Receiving_Chemotherapy_for_Cancer', 'Dementia', 'Attention_Deficit_Disorder_or_Attention_Deficit_Hyperactivity_Disorder', 'Mental_or_Personality_Disorder', 'Ability_to_Complete_AgeAppropriate_ADL', 'Pregnancy', 'Anticoagulant_Therapy', 'Steroid_Use', 'Days_from_Incident_to_ED_or_Hospital_Arrival', 'Transport_Mode', 'InterFacility_Transfer', 'Trauma_Type', 'Injury_Intent', 'Mechanism_of_Injury', 'WorkRelated', 'Blood_Transfusion', 'Neurosurgical_Intervention', 'Alcohol_Screen', 'Alcohol_Screen_Result', 'Drug_Screen__Amphetamine', 'Drug_Screen__Barbiturate', 'Drug_Screen__Benzodiazepines', 'Drug_Screen__Cannabinoid', 'Drug_Screen__Cocaine', 'Drug_Screen__MDMA_or_Ecstasy', 'Drug_Screen__Methadone', 'Drug_Screen__Methamphetamine', 'Drug_Screen__Opioid', 'Drug_Screen__Oxycodone', 'Drug_Screen__Phencyclidine', 'Drug_Screen__Tricyclic_Antidepressant', 'ACS_Verification_Level', 'Hospital_Type', 'Facility_Bed_Size', 'Primary_Method_of_Payment', 'Race', 'Protective_Device', 'Cerebral_Monitoring', 'OUTCOME']
x2 = x2[variables2]

x3 = load_dataset("mertkarabacak/TQP-atEDH", data_files="los_data_train.csv", use_auth_token = HF_TOKEN)
x3 = pd.DataFrame(x3['train'])
variables3 = ['Age', 'Sex', 'Ethnicity', 'Weight', 'Height', 'Systolic_Blood_Pressure', 'Pulse_Rate', 'Supplemental_Oxygen', 'Pulse_Oximetry', 'Respiratory_Assistance', 'Respiratory_Rate', 'Temperature', 'GCS__Eye', 'GCS__Verbal', 'GCS__Motor', 'Total_GCS', 'Pupillary_Response', 'Midline_Shift', 'Bleeding_Localization', 'Bleeding_Size', 'Current_Smoker', 'Alcohol_Use_Disorder', 'Substance_Abuse_Disorder', 'Diabetes_Mellitus', 'Hypertension', 'Congestive_Heart_Failure', 'History_of_Myocardial_Infarction', 'Angina_Pectoris', 'History_of_Cerebrovascular_Accident', 'Peripheral_Arterial_Disease', 'Chronic_Obstructive_Pulmonary_Disease', 'Chronic_Renal_Failure', 'Cirrhosis', 'Bleeding_Disorder', 'Disseminated_Cancer', 'Currently_Receiving_Chemotherapy_for_Cancer', 'Dementia', 'Attention_Deficit_Disorder_or_Attention_Deficit_Hyperactivity_Disorder', 'Mental_or_Personality_Disorder', 'Ability_to_Complete_AgeAppropriate_ADL', 'Pregnancy', 'Anticoagulant_Therapy', 'Steroid_Use', 'Days_from_Incident_to_ED_or_Hospital_Arrival', 'Transport_Mode', 'InterFacility_Transfer', 'Trauma_Type', 'Injury_Intent', 'Mechanism_of_Injury', 'WorkRelated', 'Blood_Transfusion', 'Neurosurgical_Intervention', 'Alcohol_Screen', 'Alcohol_Screen_Result', 'Drug_Screen__Amphetamine', 'Drug_Screen__Barbiturate', 'Drug_Screen__Benzodiazepines', 'Drug_Screen__Cannabinoid', 'Drug_Screen__Cocaine', 'Drug_Screen__MDMA_or_Ecstasy', 'Drug_Screen__Methadone', 'Drug_Screen__Methamphetamine', 'Drug_Screen__Opioid', 'Drug_Screen__Oxycodone', 'Drug_Screen__Phencyclidine', 'Drug_Screen__Tricyclic_Antidepressant', 'ACS_Verification_Level', 'Hospital_Type', 'Facility_Bed_Size', 'Primary_Method_of_Payment', 'Race', 'Protective_Device', 'Cerebral_Monitoring', 'OUTCOME']
x3 = x3[variables3]

x4 = load_dataset("mertkarabacak/TQP-atEDH", data_files="iculos_data_train.csv", use_auth_token = HF_TOKEN)
x4 = pd.DataFrame(x4['train'])
variables4 = ['Age', 'Sex', 'Ethnicity', 'Weight', 'Height', 'Systolic_Blood_Pressure', 'Pulse_Rate', 'Supplemental_Oxygen', 'Pulse_Oximetry', 'Respiratory_Assistance', 'Respiratory_Rate', 'Temperature', 'GCS__Eye', 'GCS__Verbal', 'GCS__Motor', 'Total_GCS', 'Pupillary_Response', 'Midline_Shift', 'Bleeding_Localization', 'Bleeding_Size', 'Current_Smoker', 'Alcohol_Use_Disorder', 'Substance_Abuse_Disorder', 'Diabetes_Mellitus', 'Hypertension', 'Congestive_Heart_Failure', 'History_of_Myocardial_Infarction', 'Angina_Pectoris', 'History_of_Cerebrovascular_Accident', 'Peripheral_Arterial_Disease', 'Chronic_Obstructive_Pulmonary_Disease', 'Chronic_Renal_Failure', 'Cirrhosis', 'Bleeding_Disorder', 'Disseminated_Cancer', 'Currently_Receiving_Chemotherapy_for_Cancer', 'Dementia', 'Attention_Deficit_Disorder_or_Attention_Deficit_Hyperactivity_Disorder', 'Mental_or_Personality_Disorder', 'Ability_to_Complete_AgeAppropriate_ADL', 'Pregnancy', 'Anticoagulant_Therapy', 'Steroid_Use', 'Days_from_Incident_to_ED_or_Hospital_Arrival', 'Transport_Mode', 'InterFacility_Transfer', 'Trauma_Type', 'Injury_Intent', 'Mechanism_of_Injury', 'WorkRelated', 'Blood_Transfusion', 'Neurosurgical_Intervention', 'Alcohol_Screen', 'Alcohol_Screen_Result', 'Drug_Screen__Amphetamine', 'Drug_Screen__Barbiturate', 'Drug_Screen__Benzodiazepines', 'Drug_Screen__Cannabinoid', 'Drug_Screen__Cocaine', 'Drug_Screen__MDMA_or_Ecstasy', 'Drug_Screen__Methadone', 'Drug_Screen__Methamphetamine', 'Drug_Screen__Opioid', 'Drug_Screen__Oxycodone', 'Drug_Screen__Phencyclidine', 'Drug_Screen__Tricyclic_Antidepressant', 'ACS_Verification_Level', 'Hospital_Type', 'Facility_Bed_Size', 'Primary_Method_of_Payment', 'Race', 'Protective_Device', 'Cerebral_Monitoring', 'OUTCOME']
x4 = x4[variables4]

x5 = load_dataset("mertkarabacak/TQP-atEDH", data_files="complications_data_train.csv", use_auth_token = HF_TOKEN)
x5 = pd.DataFrame(x5['train'])
variables5 = ['Age', 'Sex', 'Ethnicity', 'Weight', 'Height', 'Systolic_Blood_Pressure', 'Pulse_Rate', 'Supplemental_Oxygen', 'Pulse_Oximetry', 'Respiratory_Assistance', 'Respiratory_Rate', 'Temperature', 'GCS__Eye', 'GCS__Verbal', 'GCS__Motor', 'Total_GCS', 'Pupillary_Response', 'Midline_Shift', 'Bleeding_Localization', 'Bleeding_Size', 'Current_Smoker', 'Alcohol_Use_Disorder', 'Substance_Abuse_Disorder', 'Diabetes_Mellitus', 'Hypertension', 'Congestive_Heart_Failure', 'History_of_Myocardial_Infarction', 'Angina_Pectoris', 'History_of_Cerebrovascular_Accident', 'Peripheral_Arterial_Disease', 'Chronic_Obstructive_Pulmonary_Disease', 'Chronic_Renal_Failure', 'Cirrhosis', 'Bleeding_Disorder', 'Disseminated_Cancer', 'Currently_Receiving_Chemotherapy_for_Cancer', 'Dementia', 'Attention_Deficit_Disorder_or_Attention_Deficit_Hyperactivity_Disorder', 'Mental_or_Personality_Disorder', 'Ability_to_Complete_AgeAppropriate_ADL', 'Pregnancy', 'Anticoagulant_Therapy', 'Steroid_Use', 'Days_from_Incident_to_ED_or_Hospital_Arrival', 'Transport_Mode', 'InterFacility_Transfer', 'Trauma_Type', 'Injury_Intent', 'Mechanism_of_Injury', 'WorkRelated', 'Blood_Transfusion', 'Neurosurgical_Intervention', 'Alcohol_Screen', 'Alcohol_Screen_Result', 'Drug_Screen__Amphetamine', 'Drug_Screen__Barbiturate', 'Drug_Screen__Benzodiazepines', 'Drug_Screen__Cannabinoid', 'Drug_Screen__Cocaine', 'Drug_Screen__MDMA_or_Ecstasy', 'Drug_Screen__Methadone', 'Drug_Screen__Methamphetamine', 'Drug_Screen__Opioid', 'Drug_Screen__Oxycodone', 'Drug_Screen__Phencyclidine', 'Drug_Screen__Tricyclic_Antidepressant', 'ACS_Verification_Level', 'Hospital_Type', 'Facility_Bed_Size', 'Primary_Method_of_Payment', 'Race', 'Protective_Device', 'Cerebral_Monitoring', 'OUTCOME']
x5 = x5[variables5]

#Define feature names.
f1_names = list(x1.columns)
f1_names = [f1.replace('__', ' - ') for f1 in f1_names]
f1_names = [f1.replace('_', ' ') for f1 in f1_names]

f2_names = list(x2.columns)
f2_names = [f2.replace('__', ' - ') for f2 in f2_names]
f2_names = [f2.replace('_', ' ') for f2 in f2_names]

f3_names = list(x3.columns)
f3_names = [f3.replace('__', ' - ') for f3 in f3_names]
f3_names = [f3.replace('_', ' ') for f3 in f3_names]

f4_names = list(x4.columns)
f4_names = [f4.replace('__', ' - ') for f4 in f4_names]
f4_names = [f4.replace('_', ' ') for f4 in f4_names]

f5_names = list(x5.columns)
f5_names = [f5.replace('__', ' - ') for f5 in f5_names]
f5_names = [f5.replace('_', ' ') for f5 in f5_names]


#Assign unique values as answer options.
unique_SEX = ['Male', 'Female', 'Unknown']
unique_RACE = ['White', 'Black', 'Asian', 'American Indian', 'Pacific Islander', 'Other', 'Unknown']
unique_ETHNICITY = ['Not Hispanic or Latino', 'Hispanic or Latino', 'Unknown']
unique_SUPPLEMENTALOXYGEN = ['No supplemental oxygen', 'Supplemental oxygen', 'Unknown']
unique_RESPIRATORYASSISTANCE = ['Unassisted respiratory rate', 'Assisted respiratory rate', 'Unknown']
unique_TBIPUPILLARYRESPONSE = ['Both reactive', 'One reactive', 'Neither reactive', 'Unknown']
unique_TBIMIDLINESHIFT = ['No', 'Yes', 'Not imaged/unknown']
unique_LOCALIZATION = ['Supratentorial', 'Infratentorial']
unique_SIZE = ['Large, massive, or extensive (more than 30cc, more than 1cm thick', 'Small or moderate (less than 30cc or 0.6-1cm thick)', 'Tiny (less than 0.6cm thick)', 'Bilateral small or moderate (less than 30cc or 0.6-1cm thick)', 'Bilateral large, massive, or extensive (more than 30cc, more than 1cm thick)']
unique_CC_SMOKING = ['No', 'Yes', 'Unknown']
unique_CC_ALCOHOLISM = ['No', 'Yes', 'Unknown']
unique_CC_SUBSTANCEABUSE = ['No', 'Yes', 'Unknown']
unique_CC_DIABETES = ['No', 'Yes', 'Unknown']
unique_CC_HYPERTENSION = ['No', 'Yes', 'Unknown']
unique_CC_CHF = ['No', 'Yes', 'Unknown']
unique_CC_MI = ['No', 'Yes', 'Unknown']
unique_CC_ANGINAPECTORIS = ['No', 'Yes', 'Unknown']
unique_CC_CVA = ['No', 'Yes', 'Unknown']
unique_CC_PAD = ['No', 'Yes', 'Unknown']
unique_CC_COPD = ['No', 'Yes', 'Unknown']
unique_CC_RENAL = ['No', 'Yes', 'Unknown']
unique_CC_CIRRHOSIS = ['No', 'Yes', 'Unknown']
unique_CC_BLEEDING = ['No', 'Yes', 'Unknown']
unique_CC_DISCANCER = ['No', 'Yes', 'Unknown']
unique_CC_CHEMO = ['No', 'Yes', 'Unknown']
unique_CC_DEMENTIA = ['No', 'Yes', 'Unknown']
unique_CC_ADHD = ['No', 'Yes', 'Unknown']
unique_CC_MENTALPERSONALITY = ['No', 'Yes', 'Unknown']
unique_CC_FUNCTIONAL = ['No', 'Yes', 'Unknown']
unique_CC_PREGNANCY = ['No', 'Yes', 'Unknown', 'Not applicable (male patient)']
unique_CC_ANTICOAGULANT = ['No', 'Yes', 'Unknown']
unique_CC_STEROID = ['No', 'Yes', 'Unknown']
unique_TRANSPORTMODE = ['Ground ambulance', 'Private vehicle/public vehicle/walk-in', 'Air ambulance', 'Other/police/unknown/etc.']
unique_INTERFACILITYTRANSFER = ['No', 'Yes']
unique_TRAUMATYPE = ['Blunt', 'Penetrating', 'Other/unknown']
unique_INTENT = ['Unintentional', 'Assault', 'Self-inflicted', 'Other/unknown']
unique_MECHANISM = ['Fall', 'Struck by or against', 'MVT occupant', 'MVT pedestrian', 'MVT motorcyclist', 'MVT pedal cyclist', 'Other MVT', 'Other transport', 'Other pedestrian', 'Other pedal cyclist', 'Firearm', 'Cut/pierce', 'Natural/environmental', 'Machinery', 'Overexertion', 'Other/unspecified/unknown']
unique_PROTDEV = ['None', 'Belt', 'Airbag present', 'Helmet', 'Protective clothing', 'Protective non-clothing gear', 'Eye protection', 'Other']
unique_WORKRELATED = ['No', 'Yes']
unique_INTERVENTION = ['No', 'Yes']
unique_ICP = ['None', 'Intraventricular drain/catheter', 'Intraparenchymal oxygen/pressure monitor', 'Jugular venous bulb', 'Unknown']
unique_ALCOHOLSCREEN = ['Yes', 'No', 'Unknown']
unique_ANTIBIOTICTHERAPY = ['Yes', 'No', 'Unknown']
unique_DRGSCR_AMPHETAMINE = ['Not tested', 'No', 'Yes']
unique_DRGSCR_BARBITURATE = ['Not tested', 'No', 'Yes']
unique_DRGSCR_BENZODIAZEPINES = ['Not tested', 'No', 'Yes']
unique_DRGSCR_CANNABINOID = ['Not tested', 'No', 'Yes']
unique_DRGSCR_COCAINE = ['Not tested', 'No', 'Yes']
unique_DRGSCR_ECSTASY = ['Not tested', 'No', 'Yes']
unique_DRGSCR_METHADONE = ['Not tested', 'No', 'Yes']
unique_DRGSCR_METHAMPHETAMINE = ['Not tested', 'No', 'Yes']
unique_DRGSCR_OPIOID = ['Not tested', 'No', 'Yes']
unique_DRGSCR_OXYCODONE = ['Not tested', 'No', 'Yes']
unique_DRGSCR_PHENCYCLIDINE = ['Not tested', 'No', 'Yes']
unique_DRGSCR_TRICYCLICDEPRESS = ['Not tested', 'No', 'Yes']
unique_VERIFICATIONLEVEL = ['Level I Trauma Center', 'Level II Trauma Center', 'Level III Trauma Center', 'Unknown']
unique_HOSPITALTYPE = ['Non-profit', 'For profit', 'Government', 'Unknown']
unique_BEDSIZE = ['More than 600', '401 to 600', '201 to 400', '200 or fewer']
unique_PRIMARYMETHODPAYMENT = ['Private/commercial insurance', 'Medicaid', 'Medicare', 'Other government', 'Self-pay',  'Other/Unknown']


#Prepare data for the outcome 1 (mortality).
y1 = x1.pop('OUTCOME')
categorical_columns1 = list(x1.select_dtypes('object').columns)
le = sklearn.preprocessing.LabelEncoder()
x1[categorical_columns1] = x1[categorical_columns1].apply(le.fit_transform)

#Prepare data for the outcome 2 (discharge).
y2 = x2.pop('OUTCOME')
categorical_columns2 = list(x2.select_dtypes('object').columns)
le = sklearn.preprocessing.LabelEncoder()
x2[categorical_columns2] = x2[categorical_columns2].apply(le.fit_transform)

#Prepare data for the outcome 3 (LOS).
y3 = x3.pop('OUTCOME')
categorical_columns3 = list(x3.select_dtypes('object').columns)
le = sklearn.preprocessing.LabelEncoder()
x3[categorical_columns3] = x3[categorical_columns3].apply(le.fit_transform)

#Prepare data for the outcome 4 (ICU LOS).
y4 = x4.pop('OUTCOME')
categorical_columns4 = list(x4.select_dtypes('object').columns)
le = sklearn.preprocessing.LabelEncoder()
x4[categorical_columns4] = x4[categorical_columns4].apply(le.fit_transform)

#Prepare data for the outcome 5 (complications).
y5 = x5.pop('OUTCOME')
categorical_columns5 = list(x5.select_dtypes('object').columns)
le = sklearn.preprocessing.LabelEncoder()
x5[categorical_columns5] = x5[categorical_columns5].apply(le.fit_transform)

#Assign hyperparameters.
y1_params = {'objective': 'binary', 'boosting_type': 'gbdt', 'lambda_l1': 0.002295655265750986, 'lambda_l2': 1.2775068086216506, 'num_leaves': 247, 'feature_fraction': 0.8441006608783833, 'bagging_fraction': 0.43832387797183897, 'bagging_freq': 1, 'min_child_samples': 100, 'metric': 'binary_logloss', 'verbosity': -1, 'random_state': 31}
y2_params = {'criterion': 'gini', 'max_features': None, 'max_depth': 5, 'n_estimators': 1700, 'min_samples_leaf': 2, 'min_samples_split': 2, 'random_state': 31}
y3_params =  {'objective': 'CrossEntropy', 'colsample_bylevel': 0.055268360804468515, 'depth': 12, 'boosting_type': 'Ordered', 'bootstrap_type': 'Bayesian', 'bagging_temperature': 6.4211945230724465, 'used_ram_limit': '3gb', 'eval_metric': 'AUC', 'random_state': 31}
y4_params =  {'objective': 'binary', 'boosting_type': 'gbdt', 'lambda_l1': 0.00026039217865088984, 'lambda_l2': 3.4197219088950787e-08, 'num_leaves': 90, 'feature_fraction': 0.4993893704314224, 'bagging_fraction': 0.45102286812852366, 'bagging_freq': 7, 'min_child_samples': 91, 'metric': 'binary_logloss', 'verbosity': -1, 'random_state': 31}
y5_params =  {'objective': 'binary', 'boosting_type': 'gbdt', 'lambda_l1': 0.0016190622681086678, 'lambda_l2': 0.00041749233000407354, 'num_leaves': 2, 'feature_fraction': 0.5730231365909909, 'bagging_fraction': 0.6964002116636187, 'bagging_freq': 6, 'min_child_samples': 44, 'metric': 'binary_logloss', 'verbosity': -1, 'random_state': 31}

#Training models.
from lightgbm import LGBMClassifier
lgb = LGBMClassifier(**y1_params)
y1_model_lgb = lgb.fit(x1, y1)
y1_explainer_lgb = shap.TreeExplainer(y1_model_lgb)

from sklearn.ensemble import RandomForestClassifier
rf = RandomForestClassifier(**y2_params)
y2_model_rf = rf.fit(x2, y2)
y2_explainer_rf = shap.TreeExplainer(y2_model_rf)

from catboost import CatBoostClassifier
cb = CatBoostClassifier(**y3_params)
y3_model_cb = cb.fit(x3, y3)
y3_explainer_cb = shap.TreeExplainer(y3_model_cb)

from lightgbm import LGBMClassifier
lgb = LGBMClassifier(**y4_params)
y4_model_lgb = lgb.fit(x4, y4)
y4_explainer_lgb = shap.TreeExplainer(y4_model_lgb)

from lightgbm import LGBMClassifier
lgb = LGBMClassifier(**y5_params)
y5_model_lgb = lgb.fit(x5, y5)
y5_explainer_lgb = shap.TreeExplainer(y5_model_lgb)

#Define predict for y1 (mortality).
def y1_predict_xgb(*args):
    df1 = pd.DataFrame([args], columns=x1.columns)
    df1 = df1.astype({col: "category" for col in categorical_columns1})
    d1 = dict.fromkeys(df1.select_dtypes(np.int64).columns, np.int32)
    df1 = df1.astype(d1)
    pos_pred = y1_model_xgb.predict_proba(df1)
    return {"Mortality": float(pos_pred[0][1]), "No Mortality": float(pos_pred[0][0])}

def y1_predict_lgb(*args):
    df1 = pd.DataFrame([args], columns=x1.columns)
    df1 = df1.astype({col: "category" for col in categorical_columns1})
    d1 = dict.fromkeys(df1.select_dtypes(np.int64).columns, np.int32)
    df1 = df1.astype(d1)
    pos_pred = y1_model_lgb.predict_proba(df1)
    return {"Mortality": float(pos_pred[0][1]), "No Mortality": float(pos_pred[0][0])}

def y1_predict_cb(*args):
    df1 = pd.DataFrame([args], columns=x1.columns)
    df1 = df1.astype({col: "category" for col in categorical_columns1})
    pos_pred = y1_model_cb.predict(Pool(df1, cat_features = categorical_columns1), prediction_type='Probability')
    return {"Mortality": float(pos_pred[0][1]), "No Mortality": float(pos_pred[0][0])}

def y1_predict_rf(*args):
    df1 = pd.DataFrame([args], columns=x1_rf.columns)
    df1 = df1.astype({col: "category" for col in categorical_columns1})
    d1 = dict.fromkeys(df1.select_dtypes(np.int64).columns, np.int32)
    df1 = df1.astype(d1)
    pos_pred = y1_model_rf.predict_proba(df1)
    return {"Mortality": float(pos_pred[0][1]), "No Mortality": float(pos_pred[0][0])}

#Define predict for y2 (discharge).
def y2_predict_xgb(*args):
    df2 = pd.DataFrame([args], columns=x2.columns)
    df2 = df2.astype({col: "category" for col in categorical_columns2})
    d2 = dict.fromkeys(df2.select_dtypes(np.int64).columns, np.int32)
    df2 = df2.astype(d2)
    pos_pred = y2_model_xgb.predict_proba(df2)
    return {"Non-home Discharge": float(pos_pred[0][1]), "Home Discharge": float(pos_pred[0][0])}

def y2_predict_lgb(*args):
    df2 = pd.DataFrame([args], columns=x2.columns)
    df2 = df2.astype({col: "category" for col in categorical_columns2})
    d2 = dict.fromkeys(df2.select_dtypes(np.int64).columns, np.int32)
    df2 = df2.astype(d2)
    pos_pred = y2_model_lgb.predict_proba(df2)
    return {"Non-home Discharge": float(pos_pred[0][1]), "Home Discharge": float(pos_pred[0][0])}

def y2_predict_cb(*args):
    df2 = pd.DataFrame([args], columns=x2.columns)
    df2 = df2.astype({col: "category" for col in categorical_columns2})
    pos_pred = y2_model_cb.predict(Pool(df2, cat_features = categorical_columns2), prediction_type='Probability')
    return {"Non-home Discharge": float(pos_pred[0][1]), "Home Discharge": float(pos_pred[0][0])}

def y2_predict_rf(*args):
    df2 = pd.DataFrame([args], columns=x2.columns)
    df2 = df2.astype({col: "category" for col in categorical_columns2})
    d2 = dict.fromkeys(df2.select_dtypes(np.int64).columns, np.int32)
    df2 = df2.astype(d2)
    pos_pred = y2_model_rf.predict_proba(df2)
    return {"Non-home Discharge": float(pos_pred[0][1]), "Home Discharge": float(pos_pred[0][0])}

#Define predict for y3 (LOS).
def y3_predict_xgb(*args):
    df3 = pd.DataFrame([args], columns=x3.columns)
    df3 = df3.astype({col: "category" for col in categorical_columns3})
    d3 = dict.fromkeys(df3.select_dtypes(np.int64).columns, np.int32)
    df3 = df3.astype(d3)
    pos_pred = y3_model_xgb.predict_proba(df3)
    return {"Prolonged LOS": float(pos_pred[0][1]), "No Prolonged LOS": float(pos_pred[0][0])}

def y3_predict_lgb(*args):
    df3 = pd.DataFrame([args], columns=x3.columns)
    df3 = df3.astype({col: "category" for col in categorical_columns3})
    d3 = dict.fromkeys(df3.select_dtypes(np.int64).columns, np.int32)
    df3 = df3.astype(d3)
    pos_pred = y3_model_lgb.predict_proba(df3)
    return {"Prolonged LOS": float(pos_pred[0][1]), "No Prolonged LOS": float(pos_pred[0][0])}

def y3_predict_cb(*args):
    df3 = pd.DataFrame([args], columns=x3.columns)
    df3 = df3.astype({col: "category" for col in categorical_columns3})
    pos_pred = y3_model_cb.predict(Pool(df3, cat_features = categorical_columns3), prediction_type='Probability')
    return {"Prolonged LOS": float(pos_pred[0][1]), "No Prolonged LOS": float(pos_pred[0][0])}

def y3_predict_rf(*args):
    df3 = pd.DataFrame([args], columns=x3.columns)
    df3 = df3.astype({col: "category" for col in categorical_columns3})
    d3 = dict.fromkeys(df3.select_dtypes(np.int64).columns, np.int32)
    df3 = df3.astype(d3)
    pos_pred = y3_model_rf.predict_proba(df3)
    return {"Prolonged LOS": float(pos_pred[0][1]), "No Prolonged LOS": float(pos_pred[0][0])}

#Define predict for y4 (ICU LOS).
def y4_predict_xgb(*args):
    df4 = pd.DataFrame([args], columns=x4.columns)
    df4 = df4.astype({col: "category" for col in categorical_columns4})
    d4 = dict.fromkeys(df4.select_dtypes(np.int64).columns, np.int32)
    df4 = df4.astype(d4)
    pos_pred = y4_model_xgb.predict_proba(df4)
    return {"Prolonged ICU-LOS": float(pos_pred[0][1]), "No Prolonged ICU-LOS": float(pos_pred[0][0])}

def y4_predict_lgb(*args):
    df4 = pd.DataFrame([args], columns=x4.columns)
    df4 = df4.astype({col: "category" for col in categorical_columns4})
    d4 = dict.fromkeys(df4.select_dtypes(np.int64).columns, np.int32)
    df4 = df4.astype(d4)
    pos_pred = y4_model_lgb.predict_proba(df4)
    return {"Prolonged ICU-LOS": float(pos_pred[0][1]), "No Prolonged ICU-LOS": float(pos_pred[0][0])}

def y4_predict_cb(*args):
    df4 = pd.DataFrame([args], columns=x4.columns)
    df4 = df4.astype({col: "category" for col in categorical_columns4})
    pos_pred = y4_model_cb.predict(Pool(df4, cat_features = categorical_columns4), prediction_type='Probability')
    return {"Prolonged ICU-LOS": float(pos_pred[0][1]), "No Prolonged ICU-LOS": float(pos_pred[0][0])}

def y4_predict_rf(*args):
    df4 = pd.DataFrame([args], columns=x4.columns)
    df4 = df4.astype({col: "category" for col in categorical_columns4})
    d4 = dict.fromkeys(df4.select_dtypes(np.int64).columns, np.int32)
    df4 = df4.astype(d4)
    pos_pred = y4_model_rf.predict_proba(df4)
    return {"Prolonged ICU-LOS": float(pos_pred[0][1]), "No Prolonged ICU-LOS": float(pos_pred[0][0])}

#Define predict for y5 (complications).
def y5_predict_xgb(*args):
    df5 = pd.DataFrame([args], columns=x5.columns)
    df5 = df5.astype({col: "category" for col in categorical_columns5})
    d5 = dict.fromkeys(df5.select_dtypes(np.int64).columns, np.int32)
    df5 = df5.astype(d5)
    pos_pred = y5_model_xgb.predict_proba(df5)
    return {"Major Complicatons": float(pos_pred[0][1]), "No Major Complications": float(pos_pred[0][0])}

def y5_predict_lgb(*args):
    df5 = pd.DataFrame([args], columns=x5.columns)
    df5 = df5.astype({col: "category" for col in categorical_columns5})
    d5 = dict.fromkeys(df5.select_dtypes(np.int64).columns, np.int32)
    df5 = df5.astype(d5)
    pos_pred = y5_model_lgb.predict_proba(df5)
    return {"Major Complicatons": float(pos_pred[0][1]), "No Major Complications": float(pos_pred[0][0])}

def y5_predict_cb(*args):
    df5 = pd.DataFrame([args], columns=x5.columns)
    df5 = df5.astype({col: "category" for col in categorical_columns5})
    pos_pred = y5_model_cb.predict(Pool(df5, cat_features = categorical_columns5), prediction_type='Probability')
    return {"Major Complicatons": float(pos_pred[0][1]), "No Major Complications": float(pos_pred[0][0])}

def y5_predict_rf(*args):
    df5 = pd.DataFrame([args], columns=x5_rf.columns)
    df5 = df5.astype({col: "category" for col in categorical_columns5})
    d5 = dict.fromkeys(df5.select_dtypes(np.int64).columns, np.int32)
    df5 = df5.astype(d5)
    pos_pred = y5_model_rf.predict_proba(df5)
    return {"Major Complicatons": float(pos_pred[0][1]), "No Major Complications": float(pos_pred[0][0])}

#Define function for wrapping feature labels.
def wrap_labels(ax, width, break_long_words=False):
    labels = []
    for label in ax.get_yticklabels():
        text = label.get_text()
        labels.append(textwrap.fill(text, width=width, break_long_words=break_long_words))
    ax.set_yticklabels(labels, rotation=0)
    

#Define interpret for y1 (mortality).
def y1_interpret_xgb(*args):
    df1 = pd.DataFrame([args], columns=x1.columns)
    df1 = df1.astype({col: "category" for col in categorical_columns1})
    shap_values1 = y1_explainer_xgb.shap_values(xgb.DMatrix(df1, enable_categorical=True))
    shap_values1 = np.abs(shap_values1)
    shap.bar_plot(shap_values1[0], max_display = 10, show = False, feature_names = f1_names)
    fig = plt.gcf()
    ax = plt.gca()
    wrap_labels(ax, 20)
    ax.figure
    plt.tight_layout()
    fig.set_figheight(7)
    fig.set_figwidth(9)
    plt.xlabel("SHAP value (impact on model output)", fontsize =12, fontweight = 'heavy', labelpad = 8)
    plt.tick_params(axis="y",direction="out", labelsize = 12)
    plt.tick_params(axis="x",direction="out", labelsize = 12)
    return fig

def y1_interpret_lgb(*args):
    df1 = pd.DataFrame([args], columns=x1.columns)
    df1 = df1.astype({col: "category" for col in categorical_columns1})
    shap_values1 = y1_explainer_lgb.shap_values(df1)
    shap_values1 = np.abs(shap_values1)
    shap.bar_plot(shap_values1[0][0], max_display = 10, show = False, feature_names = f1_names)
    fig = plt.gcf()
    ax = plt.gca()
    wrap_labels(ax, 20)
    ax.figure
    plt.tight_layout()
    fig.set_figheight(7)
    fig.set_figwidth(9)
    plt.xlabel("SHAP value (impact on model output)", fontsize =12, fontweight = 'heavy', labelpad = 8)
    plt.tick_params(axis="y",direction="out", labelsize = 12)
    plt.tick_params(axis="x",direction="out", labelsize = 12)
    return fig

def y1_interpret_cb(*args):
    df1 = pd.DataFrame([args], columns=x1.columns)
    df1 = df1.astype({col: "category" for col in categorical_columns1})
    shap_values1 = y1_explainer_cb.shap_values(Pool(df1, cat_features = categorical_columns1))
    shap_values1 = np.abs(shap_values1)
    shap.bar_plot(shap_values1[0], max_display = 10, show = False, feature_names = f1_names)
    scores_desc = sorted(scores_desc)
    fig = plt.gcf()
    ax = plt.gca()
    wrap_labels(ax, 20)
    ax.figure
    plt.tight_layout()
    fig.set_figheight(7)
    fig.set_figwidth(9)
    plt.xlabel("SHAP value (impact on model output)", fontsize =12, fontweight = 'heavy', labelpad = 8)
    plt.tick_params(axis="y",direction="out", labelsize = 12)
    plt.tick_params(axis="x",direction="out", labelsize = 12)
    return fig

def y1_interpret_rf(*args):
    df1 = pd.DataFrame([args], columns=x1.columns)
    df1 = df1.astype({col: "category" for col in categorical_columns1})
    shap_values1 = y1_explainer_rf.shap_values(df1)
    shap_values1 = np.abs(shap_values1)
    shap.bar_plot(shap_values1[0][0], max_display = 10, show = False, feature_names = f1_names)
    fig = plt.gcf()
    ax = plt.gca()
    wrap_labels(ax, 20)
    ax.figure
    plt.tight_layout()
    fig.set_figheight(7)
    fig.set_figwidth(9)
    plt.xlabel("SHAP value (impact on model output)", fontsize =12, fontweight = 'heavy', labelpad = 8)
    plt.tick_params(axis="y",direction="out", labelsize = 12)
    plt.tick_params(axis="x",direction="out", labelsize = 12)
    return fig

#Define interpret for y2 (discharge).
def y2_interpret_xgb(*args):
    df2 = pd.DataFrame([args], columns=x2.columns)
    df2 = df2.astype({col: "category" for col in categorical_columns2})
    shap_values2 = y2_explainer_xgb.shap_values(xgb.DMatrix(df2, enable_categorical=True))
    shap_values2 = np.abs(shap_values2)
    shap.bar_plot(shap_values2[0], max_display = 10, show = False, feature_names = f2_names)
    fig = plt.gcf()
    ax = plt.gca()
    wrap_labels(ax, 20)
    ax.figure
    plt.tight_layout()
    fig.set_figheight(7)
    fig.set_figwidth(9)
    plt.xlabel("SHAP value (impact on model output)", fontsize =12, fontweight = 'heavy', labelpad = 8)
    plt.tick_params(axis="y",direction="out", labelsize = 12)
    plt.tick_params(axis="x",direction="out", labelsize = 12)
    return fig

def y2_interpret_lgb(*args):
    df2 = pd.DataFrame([args], columns=x2.columns)
    df2 = df2.astype({col: "category" for col in categorical_columns2})
    shap_values2 = y2_explainer_lgb.shap_values(df2)
    shap_values2 = np.abs(shap_values2)
    shap.bar_plot(shap_values2[0][0], max_display = 10, show = False, feature_names = f2_names)
    fig = plt.gcf()
    ax = plt.gca()
    wrap_labels(ax, 20)
    ax.figure
    plt.tight_layout()
    fig.set_figheight(7)
    fig.set_figwidth(9)
    plt.xlabel("SHAP value (impact on model output)", fontsize =12, fontweight = 'heavy', labelpad = 8)
    plt.tick_params(axis="y",direction="out", labelsize = 12)
    plt.tick_params(axis="x",direction="out", labelsize = 12)
    return fig

def y2_interpret_cb(*args):
    df2 = pd.DataFrame([args], columns=x2.columns)
    df2 = df2.astype({col: "category" for col in categorical_columns2})
    shap_values2 = y2_explainer_cb.shap_values(Pool(df2, cat_features = categorical_columns2))
    shap_values2 = np.abs(shap_values2)
    shap.bar_plot(shap_values2[0], max_display = 10, show = False, feature_names = f2_names)
    fig = plt.gcf()
    ax = plt.gca()
    wrap_labels(ax, 20)
    ax.figure
    plt.tight_layout()
    fig.set_figheight(7)
    fig.set_figwidth(9)
    plt.xlabel("SHAP value (impact on model output)", fontsize =12, fontweight = 'heavy', labelpad = 8)
    plt.tick_params(axis="y",direction="out", labelsize = 12)
    plt.tick_params(axis="x",direction="out", labelsize = 12)
    return fig

def y2_interpret_rf(*args):
    df2 = pd.DataFrame([args], columns=x2.columns)
    df2 = df2.astype({col: "category" for col in categorical_columns2})
    shap_values2 = y2_explainer_rf.shap_values(df2)
    shap_values2 = np.abs(shap_values2)
    shap.bar_plot(shap_values2[0][0], max_display = 10, show = False, feature_names = f2_names)
    fig = plt.gcf()
    ax = plt.gca()
    wrap_labels(ax, 20)
    ax.figure
    plt.tight_layout()
    fig.set_figheight(7)
    fig.set_figwidth(9)
    plt.xlabel("SHAP value (impact on model output)", fontsize =12, fontweight = 'heavy', labelpad = 8)
    plt.tick_params(axis="y",direction="out", labelsize = 12)
    plt.tick_params(axis="x",direction="out", labelsize = 12)
    return fig

#Define interpret for y3 (LOS).
def y3_interpret_xgb(*args):
    df3 = pd.DataFrame([args], columns=x3.columns)
    df3 = df3.astype({col: "category" for col in categorical_columns3})
    shap_values3 = y3_explainer_xgb.shap_values(xgb.DMatrix(df3, enable_categorical=True))
    shap_values3 = np.abs(shap_values3)
    shap.bar_plot(shap_values3[0], max_display = 10, show = False, feature_names = f3_names)
    fig = plt.gcf()
    ax = plt.gca()
    wrap_labels(ax, 20)
    ax.figure
    plt.tight_layout()
    fig.set_figheight(7)
    fig.set_figwidth(9)
    plt.xlabel("SHAP value (impact on model output)", fontsize =12, fontweight = 'heavy', labelpad = 8)
    plt.tick_params(axis="y",direction="out", labelsize = 12)
    plt.tick_params(axis="x",direction="out", labelsize = 12)
    return fig

def y3_interpret_lgb(*args):
    df3 = pd.DataFrame([args], columns=x3.columns)
    df3 = df3.astype({col: "category" for col in categorical_columns3})
    shap_values3 = y3_explainer_lgb.shap_values(df3)
    shap_values3 = np.abs(shap_values3)
    shap.bar_plot(shap_values3[0][0], max_display = 10, show = False, feature_names = f3_names)
    fig = plt.gcf()
    ax = plt.gca()
    wrap_labels(ax, 20)
    ax.figure
    plt.tight_layout()
    fig.set_figheight(7)
    fig.set_figwidth(9)
    plt.xlabel("SHAP value (impact on model output)", fontsize =12, fontweight = 'heavy', labelpad = 8)
    plt.tick_params(axis="y",direction="out", labelsize = 12)
    plt.tick_params(axis="x",direction="out", labelsize = 12)
    return fig

def y3_interpret_cb(*args):
    df3 = pd.DataFrame([args], columns=x3.columns)
    df3 = df3.astype({col: "category" for col in categorical_columns3})
    shap_values3 = y3_explainer_cb.shap_values(Pool(df3, cat_features = categorical_columns3))
    shap_values3 = np.abs(shap_values3)
    shap.bar_plot(shap_values3[0], max_display = 10, show = False, feature_names = f3_names)
    fig = plt.gcf()
    ax = plt.gca()
    wrap_labels(ax, 20)
    ax.figure
    plt.tight_layout()
    fig.set_figheight(7)
    fig.set_figwidth(9)
    plt.xlabel("SHAP value (impact on model output)", fontsize =12, fontweight = 'heavy', labelpad = 8)
    plt.tick_params(axis="y",direction="out", labelsize = 12)
    plt.tick_params(axis="x",direction="out", labelsize = 12)
    return fig

def y3_interpret_rf(*args):
    df3 = pd.DataFrame([args], columns=x3.columns)
    df3 = df3.astype({col: "category" for col in categorical_columns3})
    shap_values3 = y3_explainer_rf.shap_values(df3)
    shap_values3 = np.abs(shap_values3)
    shap.bar_plot(shap_values3[0][0], max_display = 10, show = False, feature_names = f3_names)
    fig = plt.gcf()
    ax = plt.gca()
    wrap_labels(ax, 20)
    ax.figure
    plt.tight_layout()
    fig.set_figheight(7)
    fig.set_figwidth(9)
    plt.xlabel("SHAP value (impact on model output)", fontsize =12, fontweight = 'heavy', labelpad = 8)
    plt.tick_params(axis="y",direction="out", labelsize = 12)
    plt.tick_params(axis="x",direction="out", labelsize = 12)
    return fig

#Define interpret for y4 (ICU LOS).
def y4_interpret_xgb(*args):
    df4 = pd.DataFrame([args], columns=x4.columns)
    df4 = df4.astype({col: "category" for col in categorical_columns4})
    shap_values4 = y4_explainer_xgb.shap_values(xgb.DMatrix(df4, enable_categorical=True))
    shap_values4 = np.abs(shap_values4)
    shap.bar_plot(shap_values4[0], max_display = 10, show = False, feature_names = f4_names)
    fig = plt.gcf()
    ax = plt.gca()
    wrap_labels(ax, 20)
    ax.figure
    plt.tight_layout()
    fig.set_figheight(7)
    fig.set_figwidth(9)
    plt.xlabel("SHAP value (impact on model output)", fontsize =12, fontweight = 'heavy', labelpad = 8)
    plt.tick_params(axis="y",direction="out", labelsize = 12)
    plt.tick_params(axis="x",direction="out", labelsize = 12)
    return fig

def y4_interpret_lgb(*args):
    df4 = pd.DataFrame([args], columns=x4.columns)
    df4 = df4.astype({col: "category" for col in categorical_columns4})
    shap_values4 = y4_explainer_lgb.shap_values(df4)
    shap_values4 = np.abs(shap_values4)
    shap.bar_plot(shap_values4[0][0], max_display = 10, show = False, feature_names = f4_names)
    fig = plt.gcf()
    ax = plt.gca()
    wrap_labels(ax, 20)
    ax.figure
    plt.tight_layout()
    fig.set_figheight(7)
    fig.set_figwidth(9)
    plt.xlabel("SHAP value (impact on model output)", fontsize =12, fontweight = 'heavy', labelpad = 8)
    plt.tick_params(axis="y",direction="out", labelsize = 12)
    plt.tick_params(axis="x",direction="out", labelsize = 12)
    return fig

def y4_interpret_cb(*args):
    df4 = pd.DataFrame([args], columns=x4.columns)
    df4 = df4.astype({col: "category" for col in categorical_columns4})
    shap_values4 = y4_explainer_cb.shap_values(Pool(df4, cat_features = categorical_columns4))
    shap_values4 = np.abs(shap_values4)
    shap.bar_plot(shap_values4[0], max_display = 10, show = False, feature_names = f4_names)
    fig = plt.gcf()
    ax = plt.gca()
    wrap_labels(ax, 20)
    ax.figure
    plt.tight_layout()
    fig.set_figheight(7)
    fig.set_figwidth(9)
    plt.xlabel("SHAP value (impact on model output)", fontsize =12, fontweight = 'heavy', labelpad = 8)
    plt.tick_params(axis="y",direction="out", labelsize = 12)
    plt.tick_params(axis="x",direction="out", labelsize = 12)
    return fig

def y4_interpret_rf(*args):
    df4 = pd.DataFrame([args], columns=x4.columns)
    df4 = df4.astype({col: "category" for col in categorical_columns4})
    shap_values4 = y4_explainer.shap_values(df4)
    shap_values4 = np.abs(shap_values4)
    shap.bar_plot(shap_values4[0][0], max_display = 10, show = False, feature_names = f4_names)
    fig = plt.gcf()
    ax = plt.gca()
    wrap_labels(ax, 20)
    ax.figure
    plt.tight_layout()
    fig.set_figheight(7)
    fig.set_figwidth(9)
    plt.xlabel("SHAP value (impact on model output)", fontsize =12, fontweight = 'heavy', labelpad = 8)
    plt.tick_params(axis="y",direction="out", labelsize = 12)
    plt.tick_params(axis="x",direction="out", labelsize = 12)
    return fig

#Define interpret for y5 (complications).
def y5_interpret_xgb(*args):
    df5 = pd.DataFrame([args], columns=x5.columns)
    df5 = df5.astype({col: "category" for col in categorical_columns5})
    shap_values5 = y5_explainer_xgb.shap_values(xgb.DMatrix(df5, enable_categorical=True))
    shap_values5 = np.abs(shap_values5)
    shap.bar_plot(shap_values5[0], max_display = 10, show = False, feature_names = f5_names)
    fig = plt.gcf()
    ax = plt.gca()
    wrap_labels(ax, 20)
    ax.figure
    plt.tight_layout()
    fig.set_figheight(7)
    fig.set_figwidth(9)
    plt.xlabel("SHAP value (impact on model output)", fontsize =12, fontweight = 'heavy', labelpad = 8)
    plt.tick_params(axis="y",direction="out", labelsize = 12)
    plt.tick_params(axis="x",direction="out", labelsize = 12)
    return fig

def y5_interpret_lgb(*args):
    df5 = pd.DataFrame([args], columns=x5.columns)
    df5 = df5.astype({col: "category" for col in categorical_columns5})
    shap_values5 = y5_explainer_lgb.shap_values(df5)
    shap_values5 = np.abs(shap_values5)
    shap.bar_plot(shap_values5[0][0], max_display = 10, show = False, feature_names = f5_names)
    fig = plt.gcf()
    ax = plt.gca()
    wrap_labels(ax, 20)
    ax.figure
    plt.tight_layout()
    fig.set_figheight(7)
    fig.set_figwidth(9)
    plt.xlabel("SHAP value (impact on model output)", fontsize =12, fontweight = 'heavy', labelpad = 8)
    plt.tick_params(axis="y",direction="out", labelsize = 12)
    plt.tick_params(axis="x",direction="out", labelsize = 12)
    return fig

def y5_interpret_cb(*args):
    df5 = pd.DataFrame([args], columns=x5.columns)
    df5 = df5.astype({col: "category" for col in categorical_columns5})
    shap_values5 = y5_explainer_cb.shap_values(Pool(df5, cat_features = categorical_columns5))
    shap_values5 = np.abs(shap_values5)
    shap.bar_plot(shap_values5[0], max_display = 10, show = False, feature_names = f5_names)
    fig = plt.gcf()
    ax = plt.gca()
    wrap_labels(ax, 20)
    ax.figure
    plt.tight_layout()
    fig.set_figheight(7)
    fig.set_figwidth(9)
    plt.xlabel("SHAP value (impact on model output)", fontsize =12, fontweight = 'heavy', labelpad = 8)
    plt.tick_params(axis="y",direction="out", labelsize = 12)
    plt.tick_params(axis="x",direction="out", labelsize = 12)
    return fig

def y5_interpret_rf(*args):
    df5 = pd.DataFrame([args], columns=x5.columns)
    df5 = df5.astype({col: "category" for col in categorical_columns5})
    shap_values = y5_explainer_rf.shap_values(df5)
    shap_values1 = np.abs(shap_values5)
    shap.bar_plot(shap_values5[0][0], max_display = 10, show = False, feature_names = f5_names)
    fig = plt.gcf()
    ax = plt.gca()
    wrap_labels(ax, 20)
    ax.figure
    plt.tight_layout()
    fig.set_figheight(7)
    fig.set_figwidth(9)
    plt.xlabel("SHAP value (impact on model output)", fontsize =12, fontweight = 'heavy', labelpad = 8)
    plt.tick_params(axis="y",direction="out", labelsize = 12)
    plt.tick_params(axis="x",direction="out", labelsize = 12)
    return fig

with gr.Blocks(title = "TQP-atEDH") as demo:
        
    gr.Markdown(
        """
    <br/>
    <center><h2>NOT FOR CLINICAL USE</h2><center>    
    <br/>    
    <center><h1>Acute Traumatic Epidural Hematoma Outcomes</h1></center>
    <center><h2>Prediction Tool</h2></center>
    <br/>
    <center><h3>This web application should not be used to guide any clinical decisions.</h3><center>
    <br/>
    <center><i>The publication describing the details of this prediction tool can be reached from https://doi.org/10.1089/neu.2023.0122.</i><center>
        """
    )

    gr.Markdown(
        """
        <center><h3>Model Performances</h3></center>
          <div style="text-align:center;">
          <table>
          <tr>
            <th>Outcome</th>
            <th>Algorithm</th>
            <th>Weighted Precision</th>
            <th>Weighted Recall</th>
            <th>Weighted AUPRC</th>
            <th>Balanced Accuracy</th>
            <th>AUROC</th>
            <th>Brier Score</th>
          </tr>
          <tr>
            <td>Mortality</td>
            <td>LightGBM</td>
            <td>0.982 (0.973 - 0.991)</td>
            <td>0.974 (0.963 - 0.985)</td>
            <td>0.394 (0.361 - 0.427)</td>
            <td>0.760 (0.731 - 0.789)</td>
            <td>0.924 (0.888 - 0.987)</td>
            <td>0.014 (0.006 - 0.022)</td>             
          </tr>
          <tr>
            <td>Non-home Discharges</td>
            <td>Random Forest</td>
            <td>0.758 (0.728 - 0.788)</td>
            <td>0.764 (0.734 - 0.794)</td>
            <td>0.510 (0.475 - 0.545)</td>
            <td>0.673 (0.640 - 0.706)</td>
            <td>0.798 (0.749 - 0.818)</td>
            <td>0.159 (0.133 - 0.185)</td>             
          </tr>
          <tr>
            <td>Prolonged LOS</td>
            <td>CatBoost</td>
            <td>0.811 (0.785 - 0.837)</td>
            <td>0.827 (0.802 - 0.852)</td>
            <td>0.308 (0.277 - 0.339)</td>
            <td>0.653 (0.621 - 0.685)</td>
            <td>0.751 (0.711 - 0.798)</td>
            <td>0.124 (0.102 - 0.146)</td>             
          </tr>
          <tr>
            <td>Prolonged ICU-LOS</td>
            <td>LightGBM</td>
            <td>0.82 (0.789 - 0.851)</td>
            <td>0.818 (0.787 - 0.849)</td>
            <td>0.303 (0.266 - 0.34)</td>
            <td>0.629 (0.590 - 0.668)</td>
            <td>0.774 (0.689 - 0.801)</td>
            <td>0.111 (0.086 - 0.136)</td>             
          </tr>
          <tr>
            <td>Major Complications</td>
            <td>LightGBM</td>
            <td>0.946 (0.930 - 0.962)</td>
            <td>0.821 (0.795 - 0.847)</td>
            <td>0.075 (0.057 - 0.093)</td>
            <td>0.578 (0.544 - 0.612)</td>
            <td>0.733 (0.610 - 0.801)</td>
            <td>0.030 (0.018 - 0.042)</td>             
          </tr>          
        </table>
        </div>
        """
    )    

    with gr.Row():

        with gr.Column():

            Age = gr.Slider(label="Age", minimum = 18, maximum = 99, step = 1, value = 37)

            Sex = gr.Radio(label = "Sex", choices = unique_SEX, type = 'index', value = 'Male')

            Race = gr.Radio(label = "Race", choices = unique_RACE, type = 'index', value = 'White')
            
            Ethnicity = gr.Radio(label = "Ethnicity", choices = unique_ETHNICITY, type = 'index',value = 'Not Hispanic or Latino')

            Weight = gr.Slider(label = "Weight (in kilograms)", minimum = 20, maximum = 200, step = 1, value = 75)
            
            Height = gr.Slider(label = "Height (in centimeters)", minimum = 100, maximum = 250, step = 1, value = 175)
            
            Systolic_Blood_Pressure = gr.Slider(label = "Systolic Blood Pressure", minimum = 50, maximum = 250, step = 1, value = 135)

            Pulse_Rate = gr.Slider(label = "Pulse Rate", minimum=20, maximum=250, step=1, value = 75)

            Supplemental_Oxygen = gr.Radio(label = "Supplemental Oxygen", choices = unique_SUPPLEMENTALOXYGEN, type = 'index', value = 'No supplemental oxygen')
            
            Pulse_Oximetry = gr.Slider(label = "Pulse Oximetry", minimum = 50, maximum = 100, step = 1, value = 99)

            Respiratory_Assistance = gr.Radio(label = "Respiratory Assistance", choices = unique_RESPIRATORYASSISTANCE, type = 'index', value = 'Unassisted respiratory rate')

            Respiratory_Rate = gr.Slider(label = "Respiratory Rate", minimum = 1, maximum = 99, step = 1, value = 18)

            Temperature = gr.Slider(label = "Temperature", minimum = 30, maximum = 50, step = 0.1, value = 36.5)
            
            GCS__Eye = gr.Slider(label = "GCS - Eye", minimum = 1, maximum = 4, step = 1, value = 4)

            GCS__Verbal = gr.Slider(label = "GCS - Verbal", minimum = 1, maximum = 5, step = 1, value = 5)

            GCS__Motor = gr.Slider(label = "GCS - Motor", minimum = 1, maximum = 6, step = 1, value = 6)

            Total_GCS = gr.Slider(label = "GCS - Total", minimum = 1, maximum = 15, step = 1, value = 15)
            
            Pupillary_Response = gr.Radio(label = "Pupillary Response", choices = unique_TBIPUPILLARYRESPONSE, type = 'index', value = 'Both reactive')
            
            Midline_Shift = gr.Radio(label = "Midline Shift", choices = unique_TBIMIDLINESHIFT, type = 'index', value = 'No')
            
            Bleeding_Localization = gr.Radio(label = "Bleeding Localization", choices = unique_LOCALIZATION, type = 'index', value = 'Supratentorial')   
            
            Bleeding_Size = gr.Radio(label = "Bleeding Size", choices = unique_SIZE, type = 'index', value = 'Tiny (less than 0.6cm thick)')
            
            Current_Smoker = gr.Radio(label = "Current Smoker", choices = unique_CC_SMOKING, type = 'index', value = 'No')

            Comorbid_Condition__Alcohol_Use_Disorder = gr.Radio(label = "Comorbid Condition - Alcohol Use Disorder", choices = unique_CC_ALCOHOLISM, type = 'index', value = 'No')

            Comorbid_Condition__Substance_Abuse_Disorder = gr.Radio(label = "Comorbid Condition - Substance Abuse Disorder", choices = unique_CC_SUBSTANCEABUSE, type = 'index', value = 'No')

            Comorbid_Condition__Diabetes_Mellitus = gr.Radio(label = "Comorbid Condition - Diabetes Mellitus", choices = unique_CC_DIABETES, type = 'index', value = 'No')

            Comorbid_Condition__Hypertension = gr.Radio(label = "Comorbid Condition - Hypertension", choices = unique_CC_HYPERTENSION, type = 'index', value = 'No')

            Comorbid_Condition__Congestive_Heart_Failure = gr.Radio(label = "Comorbid Condition - Congestive Heart Failure", choices = unique_CC_CHF, type = 'index', value = 'No')
            
            History_of_Myocardial_Infarction = gr.Radio(label = "History of Myocardial Infarction", choices = unique_CC_MI, type = 'index', value = 'No')

            Comorbid_Condition__Angina_Pectoris = gr.Radio(label = "Comorbid Condition - Angina Pectoris", choices = unique_CC_ANGINAPECTORIS, type = 'index', value = 'No')
            
            History_of_Cerebrovascular_Accident = gr.Radio(label = "History of Cerebrovascular Accident", choices = unique_CC_CVA, type = 'index', value = 'No')

            Comorbid_Condition__Peripheral_Arterial_Disease = gr.Radio(label = "Comorbid Condition - Peripheral Arterial Disease", choices = unique_CC_PAD, type = 'index', value = 'No')

            Comorbid_Condition__Chronic_Obstructive_Pulmonary_Disease = gr.Radio(label = "Comorbid Condition - Chronic Obstructive Pulmonary Disease", choices = unique_CC_COPD, type = 'index', value = 'No')

            Comorbid_Condition__Chronic_Renal_Failure = gr.Radio(label = "Comorbid Condition - Chronic Renal Failure", choices = unique_CC_RENAL, type = 'index', value = 'No')

            Comorbid_Condition__Cirrhosis = gr.Radio(label = "Comorbid Condition - Cirrhosis", choices = unique_CC_CIRRHOSIS, type = 'index', value = 'No')

            Comorbid_Condition__Bleeding_Disorder = gr.Radio(label = "Comorbid Condition - Bleeding Disorder", choices = unique_CC_BLEEDING, type = 'index', value = 'No')
            
            Comorbid_Condition__Disseminated_Cancer = gr.Radio(label = "Comorbid Condition - Disseminated Cancer", choices = unique_CC_DISCANCER, type = 'index', value = 'No')

            Comorbid_Condition__Currently_Receiving_Chemotherapy_for_Cancer = gr.Radio(label = "Comorbid Condition - Currently Receiving Chemotherapy for Cancer", choices = unique_CC_CHEMO, type = 'index', value = 'No')

            Comorbid_Condition__Dementia = gr.Radio(label = "Comorbid Condition - Dementia", choices = unique_CC_DEMENTIA, type = 'index', value = 'No')
            Comorbid_Condition__Attention_Deficit_Disorder_or_Attention_Deficit_Hyperactivity_Disorder = gr.Radio(label = "Comorbid Condition - Attention Deficit Disorder or Attention Deficit Hyperactivity Disorder", choices = unique_CC_ADHD, type = 'index', value = 'No')

            Comorbid_Condition__Mental_or_Personality_Disorder = gr.Radio(label = "Comorbid Condition - Mental or Personality Disorder", choices = unique_CC_MENTALPERSONALITY, type = 'index', value = 'No')

            Ability_to_Complete_AgeAppropriate_ADL = gr.Radio(label = "Ability to Complete Age-Appropriate ADL", choices = unique_CC_FUNCTIONAL, type = 'index', value = 'Yes')

            Pregnancy = gr.Radio(label = "Pregnancy", choices = unique_CC_PREGNANCY, type = 'index', value = 'Not applicable (male patient)')

            Anticoagulant_Therapy = gr.Radio(label = "Anticoagulant Therapy", choices = unique_CC_ANTICOAGULANT, type = 'index', value = 'No')

            Steroid_Use = gr.Radio(label = "Steroid Use", choices = unique_CC_STEROID, type = 'index', value = 'No')
            
            Days_from_Incident_to_ED_or_Hospital_Arrival = gr.Slider(label = "Days from Incident to ED or Hospital Arrival", minimum = 1, maximum = 31, step = 1, value = 1)

            Transport_Mode = gr.Radio(label = "Transport Mode", choices = unique_TRANSPORTMODE, type = 'index', value = 'Ground ambulance')

            InterFacility_Transfer = gr.Radio(label = "Inter-Facility Transfer", choices = unique_INTERFACILITYTRANSFER, type = 'index', value = 'No')

            Trauma_Type = gr.Radio(label = "Trauma Type", choices = unique_TRAUMATYPE, type = 'index', value = 'Blunt')

            Injury_Intent = gr.Radio(label = "Injury Intent", choices = unique_INTENT, type = 'index', value = 'Unintentional')

            Mechanism_of_Injury = gr.Dropdown(label = "Mechanism of Injury", choices = unique_MECHANISM, type = 'index', value = 'Fall')
         
            Protective_Device = gr.Dropdown(label = "Protective Device", choices = unique_PROTDEV, type = 'index', value = 'None')

            WorkRelated = gr.Dropdown(label = "Work-Related", choices = unique_WORKRELATED, type = 'index', value = 'No')
            
            Blood_Transfusion = gr.Slider(label="Blood Transfusion (mL)", minimum = 0, maximum = 5000, step = 50, value = 0)
                        
            Neurosurgical_Intervention = gr.Radio(label = "Neurosurgical Intervention", choices = unique_INTERVENTION, type = 'index', value = 'No')

            Cerebral_Monitoring = gr.Dropdown(label = "Cerebral Monitoring", choices = unique_ICP, type = 'index', value = 'None')

            Alcohol_Screen = gr.Radio(label = "Alcohol Screen", choices = unique_ALCOHOLSCREEN, type = 'index', value = 'Yes')

            Alcohol_Screen_Result = gr.Slider(label="Alcohol Screen Result", minimum = 0, maximum = 1, step = 0.1, value = 0)
            
            Drug_Screen__Amphetamine = gr.Radio(label = "Drug Screen - Amphetamine", choices = unique_DRGSCR_AMPHETAMINE, type = 'index', value = 'No')
            
            Drug_Screen__Barbiturate = gr.Radio(label = "Drug Screen - Barbiturate", choices = unique_DRGSCR_BARBITURATE, type = 'index', value = 'No')
            
            Drug_Screen__Benzodiazepines = gr.Radio(label = "Drug Screen - Benzodiazepines", choices = unique_DRGSCR_BENZODIAZEPINES, type = 'index', value = 'No')
            
            Drug_Screen__Cannabinoid = gr.Radio(label = "Drug Screen - Cannabinoid", choices = unique_DRGSCR_CANNABINOID, type = 'index', value = 'No')
            
            Drug_Screen__Cocaine = gr.Radio(label = "Drug Screen - Cocaine", choices = unique_DRGSCR_COCAINE, type = 'index', value = 'No')
            
            Drug_Screen__MDMA_or_Ecstasy = gr.Radio(label = "Drug Screen - MDMA or Ecstasy", choices = unique_DRGSCR_ECSTASY, type = 'index', value = 'No')
            
            Drug_Screen__Methadone = gr.Radio(label = "Drug Screen - Methadone", choices = unique_DRGSCR_METHADONE, type = 'index', value = 'No')
            
            Drug_Screen__Methamphetamine = gr.Radio(label = "Drug Screen - Methamphetamine", choices = unique_DRGSCR_METHAMPHETAMINE, type = 'index', value = 'No')
            
            Drug_Screen__Opioid = gr.Radio(label = "Drug Screen - Opioid", choices = unique_DRGSCR_OPIOID, type = 'index', value = 'No')
            
            Drug_Screen__Oxycodone = gr.Radio(label = "Drug Screen - Oxycodone", choices = unique_DRGSCR_OXYCODONE, type = 'index', value = 'No')
            
            Drug_Screen__Phencyclidine = gr.Radio(label = "Drug Screen - Phencyclidine", choices = unique_DRGSCR_PHENCYCLIDINE, type = 'index', value = 'No')
            
            Drug_Screen__Tricyclic_Antidepressant = gr.Radio(label = "Drug Screen - Tricyclic Antidepressant", choices = unique_DRGSCR_TRICYCLICDEPRESS, type = 'index', value = 'No')
            
            ACS_Verification_Level = gr.Radio(label = "ACS Verification Level", choices = unique_VERIFICATIONLEVEL, type = 'index', value = 'Level I Trauma Center')
            
            Hospital_Type = gr.Radio(label = "Hospital Type", choices = unique_HOSPITALTYPE, type = 'index', value = 'Non-profit')
            
            Facility_Bed_Size = gr.Radio(label = "Facility Bed Size", choices = unique_BEDSIZE, type = 'index', value = 'More than 600')
            
            Primary_Method_of_Payment = gr.Dropdown(label = "Primary Method of Payment", choices = unique_PRIMARYMETHODPAYMENT, type = 'index', value = 'Private/commercial insurance')
            
        with gr.Column():
            
            with gr.Box():
                
                gr.Markdown(
                    """
                    <center> <h2>Mortality</h2> </center>
                    <center> This model uses the LightGBM algorithm. </center>
                    <br/>
                    """
                    )
                
                with gr.Row():
                    y1_predict_btn_lgb = gr.Button(value="Predict")
                
                gr.Markdown(
                    """
                    <br/>
                    """
                    )
                
                label1 = gr.Label()
                
                gr.Markdown(
                    """
                    <br/>
                    """
                    )
                
                with gr.Row():
                    y1_interpret_btn_lgb = gr.Button(value="Explain")
                
                gr.Markdown(
                    """
                    <br/>
                    """
                    )
                
                plot1 = gr.Plot()
                
                gr.Markdown(
                    """
                    <br/>
                    """
                    )
                
            with gr.Box():
                gr.Markdown(
                    """
                    <center> <h2>Discharge Disposition</h2> </center>
                    <center> This model uses the Random Forest algorithm. </center>
                    <br/>
                    """
                    )
                
                with gr.Row():
                    y2_predict_btn_rf = gr.Button(value="Predict")
                
                gr.Markdown(
                    """
                    <br/>
                    """
                    )
                
                label2 = gr.Label()
                
                gr.Markdown(
                    """
                    <br/>
                    """
                    )
                
                with gr.Row():
                    y2_interpret_btn_rf = gr.Button(value="Explain")
                
                gr.Markdown(
                    """
                    <br/>
                    """
                    )
                
                plot2 = gr.Plot()
                
                gr.Markdown(
                    """
                    <br/>
                    """
                    )
                
            with gr.Box():
                
                gr.Markdown(
                    """
                    <center> <h2>Prolonged Length of Stay</h2> </center>
                    <center> This model uses the CatBoost algorithm. </center>
                    <br/>
                    """
                    )
                
                with gr.Row():
                    y3_predict_btn_cb = gr.Button(value="Predict")
                
                gr.Markdown(
                    """
                    <br/>
                    """
                    )
                
                label3 = gr.Label()
                
                gr.Markdown(
                    """
                    <br/>
                    """
                    )
                
                with gr.Row():
                    y3_interpret_btn_cb = gr.Button(value="Explain")
                
                gr.Markdown(
                    """
                    <br/>
                    """
                    )
                
                plot3 = gr.Plot()
                
                gr.Markdown(
                    """
                    <br/>
                    """
                    )  

            with gr.Box():
                gr.Markdown(
                    """
                    <center> <h2>Prolonged Length of ICU Stay</h2> </center>
                    <center> This model uses the LightGBM algorithm. </center>
                    <br/>
                    """
                )
                with gr.Row():
                    y4_predict_btn_lgb = gr.Button(value="Predict")
                
                gr.Markdown(
                    """
                    <br/>
                    """
                    )
                
                label4 = gr.Label()
                
                gr.Markdown(
                    """
                    <br/>
                    """
                    )
                
                with gr.Row():
                    y4_interpret_btn_lgb = gr.Button(value="Explain")
                
                gr.Markdown(
                    """
                    <br/>
                    """
                    )
                
                plot4 = gr.Plot()
                
                gr.Markdown(
                    """
                    <br/>
                    """
                    )
                
            with gr.Box():
                gr.Markdown(
                    """
                    <center> <h2>Major Complications</h2> </center>
                    <center> This model uses the LightGBM algorithm. </center>
                    <br/>
                    """
                    )
                
                with gr.Row():
                    y5_predict_btn_lgb = gr.Button(value="Predict")
                
                gr.Markdown(
                    """
                    <br/>
                    """
                    )
                
                label5 = gr.Label()
                
                gr.Markdown(
                    """
                    <br/>
                    """
                    )
                
                with gr.Row():
                    y5_interpret_btn_lgb = gr.Button(value="Explain")
                
                gr.Markdown(
                    """
                    <br/>
                    """
                    )
                
                plot5 = gr.Plot()
                
                gr.Markdown(
                    """
                    <br/>
                    """
                    )                
                                
                y1_predict_btn_lgb.click(
                    y1_predict_lgb,
                    inputs = [Age, Sex, Ethnicity, Weight, Height, Systolic_Blood_Pressure, Pulse_Rate, Supplemental_Oxygen, Pulse_Oximetry, Respiratory_Assistance, Respiratory_Rate, Temperature, GCS__Eye, GCS__Verbal, GCS__Motor, Total_GCS, Pupillary_Response, Midline_Shift, Bleeding_Localization, Bleeding_Size, Current_Smoker, Comorbid_Condition__Alcohol_Use_Disorder, Comorbid_Condition__Substance_Abuse_Disorder, Comorbid_Condition__Diabetes_Mellitus, Comorbid_Condition__Hypertension, Comorbid_Condition__Congestive_Heart_Failure, History_of_Myocardial_Infarction, Comorbid_Condition__Angina_Pectoris, History_of_Cerebrovascular_Accident, Comorbid_Condition__Peripheral_Arterial_Disease, Comorbid_Condition__Chronic_Obstructive_Pulmonary_Disease, Comorbid_Condition__Chronic_Renal_Failure, Comorbid_Condition__Cirrhosis, Comorbid_Condition__Bleeding_Disorder, Comorbid_Condition__Disseminated_Cancer, Comorbid_Condition__Currently_Receiving_Chemotherapy_for_Cancer, Comorbid_Condition__Dementia, Comorbid_Condition__Attention_Deficit_Disorder_or_Attention_Deficit_Hyperactivity_Disorder, Comorbid_Condition__Mental_or_Personality_Disorder, Ability_to_Complete_AgeAppropriate_ADL, Pregnancy, Anticoagulant_Therapy, Steroid_Use, Days_from_Incident_to_ED_or_Hospital_Arrival, Transport_Mode, InterFacility_Transfer, Trauma_Type, Injury_Intent, Mechanism_of_Injury, WorkRelated, Blood_Transfusion, Neurosurgical_Intervention, Alcohol_Screen, Alcohol_Screen_Result, Drug_Screen__Amphetamine, Drug_Screen__Barbiturate, Drug_Screen__Benzodiazepines, Drug_Screen__Cannabinoid, Drug_Screen__Cocaine, Drug_Screen__MDMA_or_Ecstasy, Drug_Screen__Methadone, Drug_Screen__Methamphetamine, Drug_Screen__Opioid, Drug_Screen__Oxycodone, Drug_Screen__Phencyclidine, Drug_Screen__Tricyclic_Antidepressant, ACS_Verification_Level, Hospital_Type, Facility_Bed_Size, Primary_Method_of_Payment, Race, Cerebral_Monitoring, Protective_Device,],
                    outputs = [label1]
                )

                y2_predict_btn_rf.click(
                    y2_predict_rf,
                    inputs = [Age, Sex, Ethnicity, Weight, Height, Systolic_Blood_Pressure, Pulse_Rate, Supplemental_Oxygen, Pulse_Oximetry, Respiratory_Assistance, Respiratory_Rate, Temperature, GCS__Eye, GCS__Verbal, GCS__Motor, Total_GCS, Pupillary_Response, Midline_Shift, Bleeding_Localization, Bleeding_Size, Current_Smoker, Comorbid_Condition__Alcohol_Use_Disorder, Comorbid_Condition__Substance_Abuse_Disorder, Comorbid_Condition__Diabetes_Mellitus, Comorbid_Condition__Hypertension, Comorbid_Condition__Congestive_Heart_Failure, History_of_Myocardial_Infarction, Comorbid_Condition__Angina_Pectoris, History_of_Cerebrovascular_Accident, Comorbid_Condition__Peripheral_Arterial_Disease, Comorbid_Condition__Chronic_Obstructive_Pulmonary_Disease, Comorbid_Condition__Chronic_Renal_Failure, Comorbid_Condition__Cirrhosis, Comorbid_Condition__Bleeding_Disorder, Comorbid_Condition__Disseminated_Cancer, Comorbid_Condition__Currently_Receiving_Chemotherapy_for_Cancer, Comorbid_Condition__Dementia, Comorbid_Condition__Attention_Deficit_Disorder_or_Attention_Deficit_Hyperactivity_Disorder, Comorbid_Condition__Mental_or_Personality_Disorder, Ability_to_Complete_AgeAppropriate_ADL, Pregnancy, Anticoagulant_Therapy, Steroid_Use, Days_from_Incident_to_ED_or_Hospital_Arrival, Transport_Mode, InterFacility_Transfer, Trauma_Type, Injury_Intent, Mechanism_of_Injury, WorkRelated, Blood_Transfusion, Neurosurgical_Intervention, Alcohol_Screen, Alcohol_Screen_Result, Drug_Screen__Amphetamine, Drug_Screen__Barbiturate, Drug_Screen__Benzodiazepines, Drug_Screen__Cannabinoid, Drug_Screen__Cocaine, Drug_Screen__MDMA_or_Ecstasy, Drug_Screen__Methadone, Drug_Screen__Methamphetamine, Drug_Screen__Opioid, Drug_Screen__Oxycodone, Drug_Screen__Phencyclidine, Drug_Screen__Tricyclic_Antidepressant, ACS_Verification_Level, Hospital_Type, Facility_Bed_Size, Primary_Method_of_Payment, Race, Cerebral_Monitoring, Protective_Device,],
                    outputs = [label2]
                )
                
                y3_predict_btn_cb.click(
                    y3_predict_cb,
                    inputs = [Age, Sex, Ethnicity, Weight, Height, Systolic_Blood_Pressure, Pulse_Rate, Supplemental_Oxygen, Pulse_Oximetry, Respiratory_Assistance, Respiratory_Rate, Temperature, GCS__Eye, GCS__Verbal, GCS__Motor, Total_GCS, Pupillary_Response, Midline_Shift, Bleeding_Localization, Bleeding_Size, Current_Smoker, Comorbid_Condition__Alcohol_Use_Disorder, Comorbid_Condition__Substance_Abuse_Disorder, Comorbid_Condition__Diabetes_Mellitus, Comorbid_Condition__Hypertension, Comorbid_Condition__Congestive_Heart_Failure, History_of_Myocardial_Infarction, Comorbid_Condition__Angina_Pectoris, History_of_Cerebrovascular_Accident, Comorbid_Condition__Peripheral_Arterial_Disease, Comorbid_Condition__Chronic_Obstructive_Pulmonary_Disease, Comorbid_Condition__Chronic_Renal_Failure, Comorbid_Condition__Cirrhosis, Comorbid_Condition__Bleeding_Disorder, Comorbid_Condition__Disseminated_Cancer, Comorbid_Condition__Currently_Receiving_Chemotherapy_for_Cancer, Comorbid_Condition__Dementia, Comorbid_Condition__Attention_Deficit_Disorder_or_Attention_Deficit_Hyperactivity_Disorder, Comorbid_Condition__Mental_or_Personality_Disorder, Ability_to_Complete_AgeAppropriate_ADL, Pregnancy, Anticoagulant_Therapy, Steroid_Use, Days_from_Incident_to_ED_or_Hospital_Arrival, Transport_Mode, InterFacility_Transfer, Trauma_Type, Injury_Intent, Mechanism_of_Injury, WorkRelated, Blood_Transfusion, Neurosurgical_Intervention, Alcohol_Screen, Alcohol_Screen_Result, Drug_Screen__Amphetamine, Drug_Screen__Barbiturate, Drug_Screen__Benzodiazepines, Drug_Screen__Cannabinoid, Drug_Screen__Cocaine, Drug_Screen__MDMA_or_Ecstasy, Drug_Screen__Methadone, Drug_Screen__Methamphetamine, Drug_Screen__Opioid, Drug_Screen__Oxycodone, Drug_Screen__Phencyclidine, Drug_Screen__Tricyclic_Antidepressant, ACS_Verification_Level, Hospital_Type, Facility_Bed_Size, Primary_Method_of_Payment, Race, Cerebral_Monitoring, Protective_Device,],
                    outputs = [label3]
                )

                y4_predict_btn_lgb.click(
                    y4_predict_lgb,
                    inputs = [Age, Sex, Ethnicity, Weight, Height, Systolic_Blood_Pressure, Pulse_Rate, Supplemental_Oxygen, Pulse_Oximetry, Respiratory_Assistance, Respiratory_Rate, Temperature, GCS__Eye, GCS__Verbal, GCS__Motor, Total_GCS, Pupillary_Response, Midline_Shift, Bleeding_Localization, Bleeding_Size, Current_Smoker, Comorbid_Condition__Alcohol_Use_Disorder, Comorbid_Condition__Substance_Abuse_Disorder, Comorbid_Condition__Diabetes_Mellitus, Comorbid_Condition__Hypertension, Comorbid_Condition__Congestive_Heart_Failure, History_of_Myocardial_Infarction, Comorbid_Condition__Angina_Pectoris, History_of_Cerebrovascular_Accident, Comorbid_Condition__Peripheral_Arterial_Disease, Comorbid_Condition__Chronic_Obstructive_Pulmonary_Disease, Comorbid_Condition__Chronic_Renal_Failure, Comorbid_Condition__Cirrhosis, Comorbid_Condition__Bleeding_Disorder, Comorbid_Condition__Disseminated_Cancer, Comorbid_Condition__Currently_Receiving_Chemotherapy_for_Cancer, Comorbid_Condition__Dementia, Comorbid_Condition__Attention_Deficit_Disorder_or_Attention_Deficit_Hyperactivity_Disorder, Comorbid_Condition__Mental_or_Personality_Disorder, Ability_to_Complete_AgeAppropriate_ADL, Pregnancy, Anticoagulant_Therapy, Steroid_Use, Days_from_Incident_to_ED_or_Hospital_Arrival, Transport_Mode, InterFacility_Transfer, Trauma_Type, Injury_Intent, Mechanism_of_Injury, WorkRelated, Blood_Transfusion, Neurosurgical_Intervention, Alcohol_Screen, Alcohol_Screen_Result, Drug_Screen__Amphetamine, Drug_Screen__Barbiturate, Drug_Screen__Benzodiazepines, Drug_Screen__Cannabinoid, Drug_Screen__Cocaine, Drug_Screen__MDMA_or_Ecstasy, Drug_Screen__Methadone, Drug_Screen__Methamphetamine, Drug_Screen__Opioid, Drug_Screen__Oxycodone, Drug_Screen__Phencyclidine, Drug_Screen__Tricyclic_Antidepressant, ACS_Verification_Level, Hospital_Type, Facility_Bed_Size, Primary_Method_of_Payment, Race, Cerebral_Monitoring, Protective_Device,],
                    outputs = [label4]
                )
                
                y5_predict_btn_lgb.click(
                    y5_predict_lgb,
                    inputs = [Age, Sex, Ethnicity, Weight, Height, Systolic_Blood_Pressure, Pulse_Rate, Supplemental_Oxygen, Pulse_Oximetry, Respiratory_Assistance, Respiratory_Rate, Temperature, GCS__Eye, GCS__Verbal, GCS__Motor, Total_GCS, Pupillary_Response, Midline_Shift, Bleeding_Localization, Bleeding_Size, Current_Smoker, Comorbid_Condition__Alcohol_Use_Disorder, Comorbid_Condition__Substance_Abuse_Disorder, Comorbid_Condition__Diabetes_Mellitus, Comorbid_Condition__Hypertension, Comorbid_Condition__Congestive_Heart_Failure, History_of_Myocardial_Infarction, Comorbid_Condition__Angina_Pectoris, History_of_Cerebrovascular_Accident, Comorbid_Condition__Peripheral_Arterial_Disease, Comorbid_Condition__Chronic_Obstructive_Pulmonary_Disease, Comorbid_Condition__Chronic_Renal_Failure, Comorbid_Condition__Cirrhosis, Comorbid_Condition__Bleeding_Disorder, Comorbid_Condition__Disseminated_Cancer, Comorbid_Condition__Currently_Receiving_Chemotherapy_for_Cancer, Comorbid_Condition__Dementia, Comorbid_Condition__Attention_Deficit_Disorder_or_Attention_Deficit_Hyperactivity_Disorder, Comorbid_Condition__Mental_or_Personality_Disorder, Ability_to_Complete_AgeAppropriate_ADL, Pregnancy, Anticoagulant_Therapy, Steroid_Use, Days_from_Incident_to_ED_or_Hospital_Arrival, Transport_Mode, InterFacility_Transfer, Trauma_Type, Injury_Intent, Mechanism_of_Injury, WorkRelated, Blood_Transfusion, Neurosurgical_Intervention, Alcohol_Screen, Alcohol_Screen_Result, Drug_Screen__Amphetamine, Drug_Screen__Barbiturate, Drug_Screen__Benzodiazepines, Drug_Screen__Cannabinoid, Drug_Screen__Cocaine, Drug_Screen__MDMA_or_Ecstasy, Drug_Screen__Methadone, Drug_Screen__Methamphetamine, Drug_Screen__Opioid, Drug_Screen__Oxycodone, Drug_Screen__Phencyclidine, Drug_Screen__Tricyclic_Antidepressant, ACS_Verification_Level, Hospital_Type, Facility_Bed_Size, Primary_Method_of_Payment, Race, Cerebral_Monitoring, Protective_Device,],
                    outputs = [label5]
                )

                y1_interpret_btn_lgb.click(
                    y1_interpret_lgb,
                    inputs = [Age, Sex, Ethnicity, Weight, Height, Systolic_Blood_Pressure, Pulse_Rate, Supplemental_Oxygen, Pulse_Oximetry, Respiratory_Assistance, Respiratory_Rate, Temperature, GCS__Eye, GCS__Verbal, GCS__Motor, Total_GCS, Pupillary_Response, Midline_Shift, Bleeding_Localization, Bleeding_Size, Current_Smoker, Comorbid_Condition__Alcohol_Use_Disorder, Comorbid_Condition__Substance_Abuse_Disorder, Comorbid_Condition__Diabetes_Mellitus, Comorbid_Condition__Hypertension, Comorbid_Condition__Congestive_Heart_Failure, History_of_Myocardial_Infarction, Comorbid_Condition__Angina_Pectoris, History_of_Cerebrovascular_Accident, Comorbid_Condition__Peripheral_Arterial_Disease, Comorbid_Condition__Chronic_Obstructive_Pulmonary_Disease, Comorbid_Condition__Chronic_Renal_Failure, Comorbid_Condition__Cirrhosis, Comorbid_Condition__Bleeding_Disorder, Comorbid_Condition__Disseminated_Cancer, Comorbid_Condition__Currently_Receiving_Chemotherapy_for_Cancer, Comorbid_Condition__Dementia, Comorbid_Condition__Attention_Deficit_Disorder_or_Attention_Deficit_Hyperactivity_Disorder, Comorbid_Condition__Mental_or_Personality_Disorder, Ability_to_Complete_AgeAppropriate_ADL, Pregnancy, Anticoagulant_Therapy, Steroid_Use, Days_from_Incident_to_ED_or_Hospital_Arrival, Transport_Mode, InterFacility_Transfer, Trauma_Type, Injury_Intent, Mechanism_of_Injury, WorkRelated, Blood_Transfusion, Neurosurgical_Intervention, Alcohol_Screen, Alcohol_Screen_Result, Drug_Screen__Amphetamine, Drug_Screen__Barbiturate, Drug_Screen__Benzodiazepines, Drug_Screen__Cannabinoid, Drug_Screen__Cocaine, Drug_Screen__MDMA_or_Ecstasy, Drug_Screen__Methadone, Drug_Screen__Methamphetamine, Drug_Screen__Opioid, Drug_Screen__Oxycodone, Drug_Screen__Phencyclidine, Drug_Screen__Tricyclic_Antidepressant, ACS_Verification_Level, Hospital_Type, Facility_Bed_Size, Primary_Method_of_Payment, Race, Cerebral_Monitoring, Protective_Device,],
                    outputs = [plot1],
                )
                
                y2_interpret_btn_rf.click(
                    y2_interpret_rf,
                    inputs = [Age, Sex, Ethnicity, Weight, Height, Systolic_Blood_Pressure, Pulse_Rate, Supplemental_Oxygen, Pulse_Oximetry, Respiratory_Assistance, Respiratory_Rate, Temperature, GCS__Eye, GCS__Verbal, GCS__Motor, Total_GCS, Pupillary_Response, Midline_Shift, Bleeding_Localization, Bleeding_Size, Current_Smoker, Comorbid_Condition__Alcohol_Use_Disorder, Comorbid_Condition__Substance_Abuse_Disorder, Comorbid_Condition__Diabetes_Mellitus, Comorbid_Condition__Hypertension, Comorbid_Condition__Congestive_Heart_Failure, History_of_Myocardial_Infarction, Comorbid_Condition__Angina_Pectoris, History_of_Cerebrovascular_Accident, Comorbid_Condition__Peripheral_Arterial_Disease, Comorbid_Condition__Chronic_Obstructive_Pulmonary_Disease, Comorbid_Condition__Chronic_Renal_Failure, Comorbid_Condition__Cirrhosis, Comorbid_Condition__Bleeding_Disorder, Comorbid_Condition__Disseminated_Cancer, Comorbid_Condition__Currently_Receiving_Chemotherapy_for_Cancer, Comorbid_Condition__Dementia, Comorbid_Condition__Attention_Deficit_Disorder_or_Attention_Deficit_Hyperactivity_Disorder, Comorbid_Condition__Mental_or_Personality_Disorder, Ability_to_Complete_AgeAppropriate_ADL, Pregnancy, Anticoagulant_Therapy, Steroid_Use, Days_from_Incident_to_ED_or_Hospital_Arrival, Transport_Mode, InterFacility_Transfer, Trauma_Type, Injury_Intent, Mechanism_of_Injury, WorkRelated, Blood_Transfusion, Neurosurgical_Intervention, Alcohol_Screen, Alcohol_Screen_Result, Drug_Screen__Amphetamine, Drug_Screen__Barbiturate, Drug_Screen__Benzodiazepines, Drug_Screen__Cannabinoid, Drug_Screen__Cocaine, Drug_Screen__MDMA_or_Ecstasy, Drug_Screen__Methadone, Drug_Screen__Methamphetamine, Drug_Screen__Opioid, Drug_Screen__Oxycodone, Drug_Screen__Phencyclidine, Drug_Screen__Tricyclic_Antidepressant, ACS_Verification_Level, Hospital_Type, Facility_Bed_Size, Primary_Method_of_Payment, Race, Cerebral_Monitoring, Protective_Device,],
                    outputs = [plot2],
                )

                y3_interpret_btn_cb.click(
                    y3_interpret_cb,
                    inputs = [Age, Sex, Ethnicity, Weight, Height, Systolic_Blood_Pressure, Pulse_Rate, Supplemental_Oxygen, Pulse_Oximetry, Respiratory_Assistance, Respiratory_Rate, Temperature, GCS__Eye, GCS__Verbal, GCS__Motor, Total_GCS, Pupillary_Response, Midline_Shift, Bleeding_Localization, Bleeding_Size, Current_Smoker, Comorbid_Condition__Alcohol_Use_Disorder, Comorbid_Condition__Substance_Abuse_Disorder, Comorbid_Condition__Diabetes_Mellitus, Comorbid_Condition__Hypertension, Comorbid_Condition__Congestive_Heart_Failure, History_of_Myocardial_Infarction, Comorbid_Condition__Angina_Pectoris, History_of_Cerebrovascular_Accident, Comorbid_Condition__Peripheral_Arterial_Disease, Comorbid_Condition__Chronic_Obstructive_Pulmonary_Disease, Comorbid_Condition__Chronic_Renal_Failure, Comorbid_Condition__Cirrhosis, Comorbid_Condition__Bleeding_Disorder, Comorbid_Condition__Disseminated_Cancer, Comorbid_Condition__Currently_Receiving_Chemotherapy_for_Cancer, Comorbid_Condition__Dementia, Comorbid_Condition__Attention_Deficit_Disorder_or_Attention_Deficit_Hyperactivity_Disorder, Comorbid_Condition__Mental_or_Personality_Disorder, Ability_to_Complete_AgeAppropriate_ADL, Pregnancy, Anticoagulant_Therapy, Steroid_Use, Days_from_Incident_to_ED_or_Hospital_Arrival, Transport_Mode, InterFacility_Transfer, Trauma_Type, Injury_Intent, Mechanism_of_Injury, WorkRelated, Blood_Transfusion, Neurosurgical_Intervention, Alcohol_Screen, Alcohol_Screen_Result, Drug_Screen__Amphetamine, Drug_Screen__Barbiturate, Drug_Screen__Benzodiazepines, Drug_Screen__Cannabinoid, Drug_Screen__Cocaine, Drug_Screen__MDMA_or_Ecstasy, Drug_Screen__Methadone, Drug_Screen__Methamphetamine, Drug_Screen__Opioid, Drug_Screen__Oxycodone, Drug_Screen__Phencyclidine, Drug_Screen__Tricyclic_Antidepressant, ACS_Verification_Level, Hospital_Type, Facility_Bed_Size, Primary_Method_of_Payment, Race, Cerebral_Monitoring, Protective_Device,],
                    outputs = [plot3],
                )
                
                y4_interpret_btn_lgb.click(
                    y4_interpret_lgb,
                    inputs = [Age, Sex, Ethnicity, Weight, Height, Systolic_Blood_Pressure, Pulse_Rate, Supplemental_Oxygen, Pulse_Oximetry, Respiratory_Assistance, Respiratory_Rate, Temperature, GCS__Eye, GCS__Verbal, GCS__Motor, Total_GCS, Pupillary_Response, Midline_Shift, Bleeding_Localization, Bleeding_Size, Current_Smoker, Comorbid_Condition__Alcohol_Use_Disorder, Comorbid_Condition__Substance_Abuse_Disorder, Comorbid_Condition__Diabetes_Mellitus, Comorbid_Condition__Hypertension, Comorbid_Condition__Congestive_Heart_Failure, History_of_Myocardial_Infarction, Comorbid_Condition__Angina_Pectoris, History_of_Cerebrovascular_Accident, Comorbid_Condition__Peripheral_Arterial_Disease, Comorbid_Condition__Chronic_Obstructive_Pulmonary_Disease, Comorbid_Condition__Chronic_Renal_Failure, Comorbid_Condition__Cirrhosis, Comorbid_Condition__Bleeding_Disorder, Comorbid_Condition__Disseminated_Cancer, Comorbid_Condition__Currently_Receiving_Chemotherapy_for_Cancer, Comorbid_Condition__Dementia, Comorbid_Condition__Attention_Deficit_Disorder_or_Attention_Deficit_Hyperactivity_Disorder, Comorbid_Condition__Mental_or_Personality_Disorder, Ability_to_Complete_AgeAppropriate_ADL, Pregnancy, Anticoagulant_Therapy, Steroid_Use, Days_from_Incident_to_ED_or_Hospital_Arrival, Transport_Mode, InterFacility_Transfer, Trauma_Type, Injury_Intent, Mechanism_of_Injury, WorkRelated, Blood_Transfusion, Neurosurgical_Intervention, Alcohol_Screen, Alcohol_Screen_Result, Drug_Screen__Amphetamine, Drug_Screen__Barbiturate, Drug_Screen__Benzodiazepines, Drug_Screen__Cannabinoid, Drug_Screen__Cocaine, Drug_Screen__MDMA_or_Ecstasy, Drug_Screen__Methadone, Drug_Screen__Methamphetamine, Drug_Screen__Opioid, Drug_Screen__Oxycodone, Drug_Screen__Phencyclidine, Drug_Screen__Tricyclic_Antidepressant, ACS_Verification_Level, Hospital_Type, Facility_Bed_Size, Primary_Method_of_Payment, Race, Cerebral_Monitoring, Protective_Device,],
                    outputs = [plot4],
                )
                
                y5_interpret_btn_lgb.click(
                    y5_interpret_lgb,
                    inputs = [Age, Sex, Ethnicity, Weight, Height, Systolic_Blood_Pressure, Pulse_Rate, Supplemental_Oxygen, Pulse_Oximetry, Respiratory_Assistance, Respiratory_Rate, Temperature, GCS__Eye, GCS__Verbal, GCS__Motor, Total_GCS, Pupillary_Response, Midline_Shift, Bleeding_Localization, Bleeding_Size, Current_Smoker, Comorbid_Condition__Alcohol_Use_Disorder, Comorbid_Condition__Substance_Abuse_Disorder, Comorbid_Condition__Diabetes_Mellitus, Comorbid_Condition__Hypertension, Comorbid_Condition__Congestive_Heart_Failure, History_of_Myocardial_Infarction, Comorbid_Condition__Angina_Pectoris, History_of_Cerebrovascular_Accident, Comorbid_Condition__Peripheral_Arterial_Disease, Comorbid_Condition__Chronic_Obstructive_Pulmonary_Disease, Comorbid_Condition__Chronic_Renal_Failure, Comorbid_Condition__Cirrhosis, Comorbid_Condition__Bleeding_Disorder, Comorbid_Condition__Disseminated_Cancer, Comorbid_Condition__Currently_Receiving_Chemotherapy_for_Cancer, Comorbid_Condition__Dementia, Comorbid_Condition__Attention_Deficit_Disorder_or_Attention_Deficit_Hyperactivity_Disorder, Comorbid_Condition__Mental_or_Personality_Disorder, Ability_to_Complete_AgeAppropriate_ADL, Pregnancy, Anticoagulant_Therapy, Steroid_Use, Days_from_Incident_to_ED_or_Hospital_Arrival, Transport_Mode, InterFacility_Transfer, Trauma_Type, Injury_Intent, Mechanism_of_Injury, WorkRelated, Blood_Transfusion, Neurosurgical_Intervention, Alcohol_Screen, Alcohol_Screen_Result, Drug_Screen__Amphetamine, Drug_Screen__Barbiturate, Drug_Screen__Benzodiazepines, Drug_Screen__Cannabinoid, Drug_Screen__Cocaine, Drug_Screen__MDMA_or_Ecstasy, Drug_Screen__Methadone, Drug_Screen__Methamphetamine, Drug_Screen__Opioid, Drug_Screen__Oxycodone, Drug_Screen__Phencyclidine, Drug_Screen__Tricyclic_Antidepressant, ACS_Verification_Level, Hospital_Type, Facility_Bed_Size, Primary_Method_of_Payment, Race, Cerebral_Monitoring, Protective_Device,],
                    outputs = [plot5],
                )

    gr.Markdown(
                """    
                <center><h2>Disclaimer</h2>
                <center> 
                The American College of Surgeons National Trauma Data Bank (ACS-NTDB) and the hospitals participating in the ACS-NTDB are the source of the data used herein; they have not been verified and are not responsible for the statistical validity of the data analysis or the conclusions derived by the authors. The predictive tool located on this web page is for general health information only. This prediction tool should not be used in place of professional medical service for any disease or concern. Users of the prediction tool shouldn't base their decisions about their own health issues on the information presented here. You should ask any questions to your own doctor or another healthcare professional. The authors of the study mentioned above make no guarantees or representations, either express or implied, as to the completeness, timeliness, comparative or contentious nature, or utility of any information contained in or referred to in this prediction tool. The risk associated with using this prediction tool or the information in this predictive tool is not at all assumed by the authors. The information contained in the prediction tools may be outdated, not complete, or incorrect because health-related information is subject to frequent change and multiple confounders. No express or implied doctor-patient relationship is established by using the prediction tool. The prediction tools on this website are not validated by the authors. Users of the tool are not contacted by the authors, who also do not record any specific information about them. You are hereby advised to seek the advice of a doctor or other qualified healthcare provider before making any decisions, acting, or refraining from acting in response to any healthcare problem or issue you may be experiencing at any time, now or in the future. By using the prediction tool, you acknowledge and agree that neither the authors nor any other party are or will be liable or otherwise responsible for any decisions you make, actions you take, or actions you choose not to take as a result of using any information presented here.
                <br/>
                <h4>By using this tool, you accept all of the above terms.<h4/>
                </center>
                """
    )                
                
demo.launch()