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# -*- coding: utf-8 -*-
"""app.ipynb
Automatically generated by Colab.
Original file is located at
https://colab.research.google.com/drive/1oJP09Coya1D16dQ_7fpVGlbuLnCWxHTN
"""
import gradio as gr
import joblib
import numpy as np
from tensorflow.keras.models import load_model
# Load models
svr_model = joblib.load('SVR_model.joblib')
rf_model = joblib.load('RandomForestRegressor_model.joblib')
dt_model = joblib.load('best_DecisionTreeRegressor_model.joblib')
dl_model = load_model('best_DeepLearning_model.h5')
# Load preprocessing objects
label_encoder = joblib.load('label_encoder.joblib')
column_transformer = joblib.load('column_transformer.joblib')
def predict_warfarin_dose(gender, race, age, height, weight, diabetes,
simvastatin, amiodarone, inr_reported,
cyp2c9, vkorc1, model_choice):
try:
# Encode Age
age_encoded = label_encoder.transform([age])
# Create input list
inputs = [
str(gender),
str(race),
str(age),
float(height) if height is not None else 0.0,
float(weight) if weight is not None else 0.0,
float(diabetes),
float(simvastatin),
float(amiodarone),
float(inr_reported) if inr_reported is not None else 0.0,
str(cyp2c9),
str(vkorc1)
]
# Transform inputs
inputs_transformed = column_transformer.transform([inputs])
inputs_transformed[0][-7] = age_encoded[0]
input_data = np.array(inputs_transformed, dtype=np.float32)
# Make prediction based on model choice
if model_choice == 'Deep Learning':
prediction = dl_model.predict(input_data)[0][0]
elif model_choice == 'Support Vector Regression':
prediction = svr_model.predict(input_data)[0]
elif model_choice == 'Random Forest':
prediction = rf_model.predict(input_data)[0]
else:
prediction = dt_model.predict(input_data)[0]
return f"Predicted Warfarin Dose: {prediction:.2f} mg/week"
except Exception as e:
return f"Error in prediction: {str(e)}"
# Create the Gradio interface
iface = gr.Interface(
fn=predict_warfarin_dose,
inputs=[
gr.Radio(["male", "female"], label="Gender"),
gr.Dropdown(["Asian", "Black", "White", "Unknown", "Mixed or Missing"], label="Race"),
gr.Dropdown(["0-9", "10-19", "20-29", "30-39", "40-49", "50-59", "60-69",
"70-79", "80-89", "90+"], label="Age"),
gr.Number(label="Height (cm)"),
gr.Number(label="Weight (kg)"),
gr.Radio([0.0, 1.0], label="Diabetes"),
gr.Radio([0.0, 1.0], label="Simvastatin (Zocor)"),
gr.Radio([0.0, 1.0], label="Amiodarone (Cordarone)"),
gr.Number(label="INR on Reported Therapeutic Dose of Warfarin"),
gr.Dropdown(["*1/*1", "*1/*2", "*1/*3", "*2/*2", "*2/*3", "*3/*3"],
label="Cyp2C9 genotypes"),
gr.Radio(["A/A", "A/G", "G/G"], label="VKORC1 genotypes"),
gr.Dropdown(['Decision Tree', 'Support Vector Regression',
'Random Forest', 'Deep Learning'], label="Model Selection")
],
outputs=gr.Textbox(label="Prediction Result"),
title="Warfarin Dosage Prediction System",
description="""This system predicts the optimal warfarin dosage based on patient characteristics.
Enter the required information below and select a model for prediction.""",
examples=[
["male", "Asian", "50-59", 170, 70, 0.0, 0.0, 0.0, 2.5, "*1/*1", "A/G", "Random Forest"],
["female", "White", "60-69", 165, 65, 1.0, 1.0, 0.0, 2.8, "*1/*2", "G/G", "Deep Learning"]
],
theme="default"
)
# Launch the interface
if __name__ == "__main__":
iface.launch() |