Create app.py

Version 1.0 of the app

app.py

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+import pandas as pd
+import jinja2
+
+from pycaret.classification import *
+import imblearn as im
+import sklearn
+
+import gradio as gr
+import numpy as np
+
+def predict(age, female, race, elective, aweekend, zipinc_qrtl, hosp_region, hosp_division, hosp_locteach,
+            hosp_bedsize, h_contrl, pay, anemia, atrial_fibrillation, 
+            cancer, cardiac_arrhythmias, carotid_artery_disease, 
+            chronic_kidney_disease, chronic_pulmonary_disease, coagulopathy,
+            depression, diabetes_mellitus, drug_abuse, dyslipidemia, endocarditis,
+            family_history, fluid_and_electrolyte_disorder, heart_failure,
+            hypertension, known_cad, liver_disease, obesity, peripheral_vascular_disease,
+            prior_cabg, prior_icd, prior_mi, prior_pci, prior_ppm, prior_tia_stroke,
+            pulmonary_circulation_disorder, smoker, valvular_disease, weight_loss,
+            endovascular_tavr, transapical_tavr):
+  
+  model = load_model('final_model')
+
+  df = pd.DataFrame.from_dict({
+      'age': [age], 'female': [female], 'race': [race], 'elective': elective,
+       'aweekend': [aweekend], 'zipinc_qrtl': [zipinc_qrtl], 
+       'hosp_region': [hosp_region], 'hosp_division': [hosp_division],
+       'hosp_locteach': [hosp_locteach], 'hosp_bedsize': [hosp_bedsize],
+       'h_contrl': [h_contrl], 'pay': [pay], 'anemia': [anemia], 
+       'atrial_fibrillation': [atrial_fibrillation], 'cancer': [cancer],
+       'cardiac_arrhythmias': [cardiac_arrhythmias], 
+       'carotid_artery_disease': [carotid_artery_disease], 
+       'chronic_kidney_disease': [chronic_kidney_disease], 
+       'chronic_pulmonary_disease': [chronic_pulmonary_disease], 
+       'coagulopathy': [coagulopathy], 'depression': [depression],
+       'diabetes_mellitus': [diabetes_mellitus], 'drug_abuse': [drug_abuse], 
+       'dyslipidemia': [dyslipidemia], 'endocarditis': [endocarditis],
+       'family_history': [family_history], 'fluid_and_electrolyte_disorder': [fluid_and_electrolyte_disorder],
+       'heart_failure': [heart_failure], 'hypertension': [hypertension],
+       'known_cad': [known_cad], 'liver_disease': [liver_disease],
+       'obesity': [obesity], 'peripheral_vascular_disease': [peripheral_vascular_disease],
+       'prior_cabg': [prior_cabg], 'prior_icd': [prior_icd], 'prior_mi': [prior_mi],
+       'prior_pci': [prior_pci], 'prior_ppm': [prior_ppm], 'prior_tia_stroke': [prior_tia_stroke],
+       'pulmonary_circulation_disorder': [pulmonary_circulation_disorder], 
+       'smoker': [smoker], 'valvular_disease': [valvular_disease],
+       'weight_loss': [weight_loss], 'endovascular_tavr': [endovascular_tavr],
+       'transapical_tavr': [transapical_tavr]
+  })
+  
+  df.loc[:, df.dtypes == 'object'] =\
+    df.select_dtypes(['object'])\
+    .apply(lambda x: x.astype('category'))
+
+  # converting ordinal column to ordinal
+  df.zipinc_qrtl = df.zipinc_qrtl.astype(ordinal_cat)
+
+  pred = predict_model(model, df, raw_score=True)
+
+  return {'Death %': round(100*pred['Score_Yes'][0], 2),
+       'Survival %': round(100*pred['Score_No'][0], 2),
+       'Predicting Death Outcome:': pred['Label'][0]}
+
+# Defining the containers for each input
+age = gr.inputs.Slider(minimum=0, maximum=100, default=60, label="Age")
+female = gr.inputs.Dropdown(choices=["Female", "Male"],label = 'Sex')
+race = gr.inputs.Dropdown(choices=['Asian or Pacific Islander', 'Black', 'Hispanic', 'Native American', 'White',  'Other'], label = 'Race')
+elective = gr.inputs.Radio(choices=['Elective', 'NonElective'], label = 'Elective')
+aweekend = gr.inputs.Radio(choices=["No", "Yes"], label = 'Weekend')
+zipinc_qrtl = gr.inputs.Radio(choices=['FirstQ', 'SecondQ', 'ThirdQ', 'FourthQ'], label = 'Zip Income Quartile')
+hosp_region = gr.inputs.Radio(choices=['Midwest', 'Northeast', 'South', 'West'], label = 'Hospital Region')
+hosp_division = gr.inputs.Radio(choices=['New England', 'Middle Atlantic', 'East North Central', 'West North Central', 'South Atlantic', 'East South Central', 'West South Central', 'Mountain', 'Pacific'], label = 'Hospital Division')
+hosp_locteach = gr.inputs.Radio(choices=['Urban teaching', 'Urban nonteaching', 'Rural'], label= 'Hospital Location/Teaching')
+hosp_bedsize = gr.inputs.Radio(choices=['Small', 'Medium', 'Large'], label= 'Hospital Bedsize')
+h_contrl = gr.inputs.Radio(choices= ['Government_nonfederal', 'Private_invest_own', 'Private_not_profit'], label = 'Hospital Control')
+pay = gr.inputs.Dropdown(choices= ['Private insurance', 'Medicare', 'Medicaid',  'Self-pay', 'No charge', 'Other'], label = 'Payee')
+anemia = gr.inputs.Radio(choices=["No", "Yes"], label = 'Anemia')
+atrial_fibrillation = gr.inputs.Radio(choices=["No", "Yes"], label = 'Atrial Fibrillation')
+cancer = gr.inputs.Radio(choices=["No", "Yes"], label = 'Cancer')
+cardiac_arrhythmias = gr.inputs.Radio(choices=["No", "Yes"], label = 'Cardiac Arrhythmias')
+carotid_artery_disease = gr.inputs.Radio(choices=["No", "Yes"], label = 'Carotid Artery Disease') 
+chronic_kidney_disease = gr.inputs.Radio(choices=["No", "Yes"], label = 'Chronic Kidney Disease')
+chronic_pulmonary_disease = gr.inputs.Radio(choices=["No", "Yes"], label = 'Chronic Pulmonary Disease') 
+coagulopathy =  gr.inputs.Radio(choices=["No", "Yes"], label = 'Coagulopathy')
+depression = gr.inputs.Radio(choices=["No", "Yes"], label = 'Depression')
+diabetes_mellitus = gr.inputs.Radio(choices=["No", "Yes"], label = 'Diabetes Mellitus')
+drug_abuse = gr.inputs.Radio(choices=["No", "Yes"], label = 'Drug Abuse')
+dyslipidemia = gr.inputs.Radio(choices=["No", "Yes"], label = 'Dyslipidemia')
+endocarditis = gr.inputs.Radio(choices=["No", "Yes"], label = 'Endocarditis')
+family_history = gr.inputs.Radio(choices=["No", "Yes"], label = 'Family History')
+fluid_and_electrolyte_disorder = gr.inputs.Radio(choices=["No", "Yes"], label = 'Fluid and Electrolyte Disorder')
+heart_failure = gr.inputs.Radio(choices=["No", "Yes"], label = 'Heart Failure')
+hypertension = gr.inputs.Radio(choices=["No", "Yes"], label = 'Hypertension')
+known_cad = gr.inputs.Radio(choices=["No", "Yes"], label = 'Known CAD')
+liver_disease = gr.inputs.Radio(choices=["No", "Yes"], label = 'Liver Disease')
+obesity = gr.inputs.Radio(choices=["No", "Yes"], label = 'Obesity')
+peripheral_vascular_disease = gr.inputs.Radio(choices=["No", "Yes"], label = 'Peripheral Vascular Disease')
+prior_cabg = gr.inputs.Radio(choices=["No", "Yes"], label = 'Prior CABG')
+prior_icd = gr.inputs.Radio(choices=["No", "Yes"], label = 'Prior ICD')
+prior_mi = gr.inputs.Radio(choices=["No", "Yes"], label = 'Prior MI')
+prior_pci = gr.inputs.Radio(choices=["No", "Yes"], label = 'Prior PCI') 
+prior_ppm = gr.inputs.Radio(choices=["No", "Yes"], label = 'Prior PPM')
+prior_tia_stroke = gr.inputs.Radio(choices=["No", "Yes"], label = 'Prior TIA Stroke')
+pulmonary_circulation_disorder = gr.inputs.Radio(choices=["No", "Yes"], label = 'Pulmonary Circulation Disorder') 
+smoker = gr.inputs.Radio(choices=["No", "Yes"], label = 'Smoker')
+valvular_disease = gr.inputs.Radio(choices=["No", "Yes"], label = 'Valvular Disease') 
+weight_loss = gr.inputs.Radio(choices=["No", "Yes"], label = 'Weight Loss')
+endovascular_tavr = gr.inputs.Radio(choices=["No", "Yes"], label = 'Endovascular TAVR')
+transapical_tavr = gr.inputs.Radio(choices=["No", "Yes"], label = 'Transapical TAVR', default= 'Yes')
+
+
+# Defining and launching the interface
+gr.Interface(predict, [age, female, race, elective, aweekend, zipinc_qrtl, hosp_region, hosp_division, hosp_locteach,
+            hosp_bedsize, h_contrl, pay, anemia, atrial_fibrillation, 
+            cancer, cardiac_arrhythmias, carotid_artery_disease, 
+            chronic_kidney_disease, chronic_pulmonary_disease, coagulopathy,
+            depression, diabetes_mellitus, drug_abuse, dyslipidemia, endocarditis,
+            family_history, fluid_and_electrolyte_disorder, heart_failure,
+            hypertension, known_cad, liver_disease, obesity, peripheral_vascular_disease,
+            prior_cabg, prior_icd, prior_mi, prior_pci, prior_ppm, prior_tia_stroke,
+            pulmonary_circulation_disorder, smoker, valvular_disease, weight_loss,
+            endovascular_tavr, transapical_tavr], 
+            outputs = gr.Textbox(label="Predicted Outcomes for this Patient", lines=4),
+            live=True,
+            title = "Predicting In-Hospital Mortality After TAVR Using Preoperative Variables and Penalized Logistic Regression",
+            description = "The app below utilizes the finalized logistic regression model with an l2 penalty based on the manuscript by Alhwiti et al. The manuscript will be submitted to JACC: Cardiovascular Interventions. The data used for model building is all TAVR procedures between 2012 and 2019 as reported in the HCUP NIS database. <br><br> The purpose of the app is to provide evidence-based clinical support for interventional cardiology. <br> <br> For instruction on how to use the app and the encoding required for the variables,  please see <b>XYZ: insert website link here</b>.",
+            examples = ex_data, 
+            css = 'https://bootswatch.com/5/journal/bootstrap.css').launch(debug = False);