Create app.py

app.py

@@ -0,0 +1,106 @@
+from sklearn.preprocessing import *
+from sklearn.model_selection import *
+import numpy as np
+from sklearn.linear_model import LogisticRegression
+from sklearn.preprocessing import LabelEncoder
+from keras.models import Sequential
+from keras.layers import Dense
+from sklearn.metrics import *
+from sklearn.svm import SVC
+import pandas as pd
+
+import gradio as gr
+from joblib import dump, load
+
+#loading models
+log = load('logistic_model.joblib')
+knn = load('knn_model.joblib')
+decision = load('decision_tree_model.joblib')
+deep = load('deep_model.joblib')
+
+#input/output modules
+input_module1 = gr.Dropdown(choices=["Logistic Regression", "KNN", "Decision Tree","Neural Network"], label = "method")
+input_module2 = gr.Dropdown(choices=["male", 'female'], label = "gender")
+input_module3 = gr.Dropdown(choices=['african-american','asian','black','black african','black or african american',\
+                                     'caucasian', 'han chinese', 'hispanic', 'intermediate', 'japanese', 'korean', 'other',\
+                                     'Other (Black British)','Other Mixed Race','White'], label = "race")
+input_module4 = gr.Number(label='age')
+input_module5 = gr.Number(label='height (cm)')
+input_module6 = gr.Number(label='weight (kg)')
+input_module7 = gr.Checkbox(label='Diabetes')
+input_module8 = gr.Checkbox(label='Simvastatin (Zocor)')
+input_module9 = gr.Checkbox(label='Amiodarone (Cordarone)')
+input_module10 = gr.Number(label='target INR')
+input_module11 = gr.Number(label='INR on Reported Therapeutic Dose of Warfarin')
+input_module12 = gr.Number(label='Cyp2C9 genotypes (1-13)')
+input_module13 = gr.Dropdown(choices=['A/A','A/G','G/G'], label = "VKORC1 genotype")
+
+output_module = gr.Number(label='Therapeutic Dose of Warfarin (>=30 mg/wk) (1=true, 0=false)')
+
+race_options = ['african-american','asian','black','black african','black or african american',\
+                                     'caucasian', 'han chinese', 'hispanic', 'intermediate', 'japanese', 'korean', 'other',\
+                                     'Other (Black British)','Other Mixed Race','White']
+
+#gradio function
+def predict(method, gender, race, age, height, weight, diabetes, simv, amio, targetINR, INR, cyp2c9, vkorc1):
+
+  #converting inputs into numeric data
+  if gender == 'male':
+    gender = 0
+  else:
+    gender = 1
+
+  for i in range(len(race_options)):
+    if race_options[i] == race:
+      race_options[i] = 1
+    else:
+      race_options[i] = 0
+  
+  if diabetes == True:
+    diabetes = 1
+  else:
+    diabetes = 0
+  
+  if simv == True:
+    simv = 1
+  else:
+    simv = 0
+  
+  if amio == True:
+    amio = 1
+  else:
+    amio = 0
+
+  if vkorc1 == 'A/A':
+    vkorc1 = 0
+  elif vkorc1 == 'A/G':
+    vkorc1 = 1
+  else:
+    vkorc1 = 2
+
+  #compiling data
+  data = [gender, age, height, weight, diabetes, simv, amio, targetINR, INR, cyp2c9, vkorc1]
+  data.extend(race_options)
+  data.extend([0]) #accounting for extra unused race category (there are 2 'other' options for race)
+
+  #predicting using given method
+  if method == "Logistic Regression":
+    value = log.predict([data])[0]
+  elif method == "KNN":
+    value = knn.predict([data])[0]
+  elif method == "Decision Tree":
+    value = decision.predict([data])[0]
+  else:
+    value = deep.predict([data])[0]
+    if value >= 0.5:
+      value = 1
+    else:
+      value = 0
+    
+
+  return value
+
+
+
+gr.Interface(fn=predict, inputs=[input_module1,input_module2,input_module3,input_module4,input_module5,input_module6,input_module7,\
+                                 input_module8,input_module9,input_module10,input_module11,input_module12,input_module13], outputs=output_module).launch(debug=True)