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64d8f32 daf5a5f 64d8f32 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 | import streamlit as st
import joblib
import json
import numpy as np
import pickle
model = joblib.load('mmrech/model.joblib')
st.title("MACE Prediction After LT")
numeric_cols = [
'Weight', 'Height', 'BodyMassIndex', 'Hematocrit', 'Leukocytes', 'Platelets',
'TotalBilirubin', 'DirectBilirubin', 'Creatinine', 'Urea', 'ProthrombinTimeActivity',
'InternationalNormalizedRatio', 'Sodium', 'Potassium', 'Albumin', 'AST', 'ALT', 'GGT',
'AlkalinePhosphatase', 'LeftAtriumSize', 'DistalVolumeOfLeftVentricle',
'SystolicVolumeOfLeftVentricle'
]
categorical_cols = [
("Race", ["White", "Black", "Mixed/Other"]),
("Sex", ["Female", "Male"]),
("PreviousVaricealBandLigation", ["No", "Yes"]),
("PortalHypertensiveGastropathy", ["Mild", "Severe", "None"]),
("Ascites", ["No", "Yes"]),
("SpontaneousBacterialPeritonitis", ["No", "Yes"]),
("HepatopulmonarySyndrome", ["No", "Yes"]),
("BetaBlockerUse", ["No", "Yes"]),
("PortalVeinThrombosis", ["No", "Yes"]),
("HepaticEncephalopathy", ["No", "Yes"]),
("HepatorenalSyndrome", ["No", "Yes"]),
("AntibioticTherapyFor24h", ["No", "Yes"]),
("HospitalizedFor48h", ["No", "Yes"]),
("PreTransplantHemodialysis", ["No", "Yes"]),
("HepatocellularCarcinoma", ["No", "Yes"]),
("MitralInsufficiency", ["Mild", "Moderate", "Severe", "Absent"]),
("TricuspidInsufficiency", ["Mild", "Moderate", "Severe", "Absent"]),
("BloodGroup", ["A", "B", "AB", "O"]),
("CongestiveHeartFailure", ["No", "Yes"]),
("Angioplasty", ["No", "Yes"]),
("Dyslipidemia", ["No", "Yes"]),
("Hypertension", ["No", "Yes"]),
("AcuteMyocardialInfarction", ["No", "Yes"]),
("Stroke", ["Ischemic", "Hemorrhagic", "No"]),
("DiabetesMellitus", ["No", "Yes"]),
("DynamicAlteration", ["No", "Yes"]),
("NonInvasiveMethod", ["No", "Yes"]),
("ValveReplacement", ["No", "Yes"])
]
# Create input fields for numeric variables
numeric_values = {}
for numeric_var in numeric_cols:
numeric_values[numeric_var] = st.sidebar.number_input(numeric_var, value=0.0)
# Create select boxes for categorical variables with numeric values
categorical_values = {}
for categorical_var, classes in categorical_cols:
options = {cls: i for i, cls in enumerate(classes)}
selected_option = st.sidebar.selectbox(categorical_var, list(options.keys()))
categorical_values[categorical_var] = options[selected_option]
# Combine numeric and categorical values into a single dictionary
features = {**numeric_values, **categorical_values}
# Convert features dictionary to a 2D array for model prediction
features_array = np.array(list(features.values())).reshape(1, -1)
# Define the predict function
def predict(features_array):
# Make predictions using the model
prediction = model.predict(features_array)
return prediction
if st.button('Predict'):
# Get the prediction
prediction = predict(features_array)
st.write(f"Prediction: {prediction[0]}") |