app.py

import gradio as gr
import numpy as np
import pandas as pd
from gradio.outputs import Label



def multi_inputs(input_2, input_3, input_1, input_5, input_4, input_6, input_7, input_8, input_9, input_10, input_11, input_12, input_13):

    output=gr.outputs.Textbox(label = "Therapeutic Dose of Warfarin")

    intputdata = ['Gender','Race (Reported)','Age','Height (cm)','Weight (kg)','Diabetes','Simvastatin (Zocor)','Amiodarone (Cordarone)','Target INR','INR on Reported Therapeutic Dose of Warfarin','Cyp2C9 genotypes','VKORC1 genotype: -1639 G>A (3673); chr16:31015190; rs9923231; C/T']
    
    
    inputs = [input_2, input_3, input_1, input_5, input_4, input_6, input_7, input_8, input_9, input_10, input_11, input_12]

    df1 = pd.DataFrame(data= [inputs],columns= [intputdata])
    input_13 = 'Logistic Regression'
    
    if input_13 == ' Logistic Regression ':
        output = logistic_model.predict(df1)
        
    if output == 0:
        output = "Low dose required"
    else: 
        output = "High Dose"
    return output

    input_13 = 'Decision Tree'
    
    if input_13 == ' Decision Tree ':
        output = logistic_model.predict(df1)
        
    if output == 0:
        output = "Low dose required"
    else: 
        output = "High Dose"
    return output

    input_13 = 'Random Forest'
    
    if input_13 == ' Random Forest ':
        output = logistic_model.predict(df1)
        
    if output == 0:
        output = "Low dose required"
    else: 
        output = "High Dose"
    return output
    
input_1 = gr.inputs.Number(label = "Age")
input_2 = gr.inputs.Dropdown(choices=["0", "1"], label = "Gender")
input_3 = gr.inputs.Dropdown(choices=["0", "1", "2", "3", "4", "5"], label = "Race")
input_4 = gr.inputs.Number(label = "Weight")
input_5 = gr.inputs.Number(label = "Height")
input_6 = gr.inputs.Dropdown(choices=["1", "0"], label = "Diabetes")
input_7 = gr.inputs.Dropdown(choices=["1", "0"], label = "Simvastatin (Zocor)")
input_8 = gr.inputs.Dropdown(choices=["1", "0"], label = "Amiodarone (Cordarone)")
input_9 = gr.inputs.Number(label = "Target INR")
input_10 = gr.inputs.Number(label = "INR on Reported Therapeutic Dose of Warfarin")
input_11 = gr.inputs.Dropdown(choices=["1", "2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "12", "13"], label = "Cyp2C9 genotypes")
input_12 = gr.inputs.Dropdown(choices=["0", "2", "1"], label = "VKORC1 genotype: -1639 G>A (3673); chr16:31015190; rs9923231; C/T")
input_13 = gr.inputs.Dropdown(choices=["Decision Tree", "Logistic Regression", "Random Forest"], label = "Model")
output=gr.outputs.Textbox(label = "Therapeutic Dose of Warfarin")


gr.Interface(fn= multi_inputs, inputs=[input_1, input_2, input_3, input_4, input_5, input_6, input_7, input_8, input_9, input_10, input_11, input_12, input_13], 
             outputs=[output]
            ).launch()