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import gradio as gr
from joblib import load
import numpy as np
import pandas as pd
# Load your saved models
rf = load('best_random_forest_model.joblib')
dt = load('best_decision_tree_model.joblib')
mlp = load('best_MLP_classifier_model.joblib')
knn = load('best_knn_model.joblib')
# Class names
class_names = ["High Therapeutic Dose of Warfarin Required","Low Therapeutic Dose of Warfarin Required"]
# Load training data for expected feature names
training_data = pd.read_csv('dataset_train.csv')
# Drop the 'Unnamed: 0' column if it exists
if 'Unnamed: 0' in training_data.columns:
training_data = training_data.drop(columns=['Unnamed: 0'])
expected_feature_names = training_data.columns.tolist()
# Define the prediction function
def predict_warfarin_dose(gender, race, age, height, weight, diabetes, simvastatin, amiodarone, genotype, inr, algorithm):
# Decode the encoded values
gender = "Male" if gender == 1 else "Female"
race = race_dict_inverse[race]
age = age_dict_inverse[age]
genotype = genotype_dict_inverse[genotype]
# Convert input data to DataFrame for one-hot encoding
input_data = pd.DataFrame([[gender, race, age, height, weight, diabetes, simvastatin, amiodarone, genotype, inr]],
columns=['gender', 'race', 'age', 'height', 'weight', 'diabetes', 'simvastatin',
'amiodarone', 'genotype', 'inr'])
# One-hot encode categorical features
input_data_encoded = pd.get_dummies(input_data, columns=['gender', 'race', 'diabetes', 'simvastatin', 'amiodarone', 'genotype'])
# Reindex the DataFrame to match expected feature names
input_data_encoded = input_data_encoded.reindex(columns=expected_feature_names, fill_value=0)
# Predict using the selected algorithm
if algorithm == 'Random Forest':
model = rf
elif algorithm == 'Decision Tree':
model = dt
elif algorithm == 'MLP':
model = mlp
elif algorithm == 'KNN':
model = knn
else:
raise ValueError("Invalid algorithm selected.")
y_prob = model.predict_proba(input_data_encoded)
class_idx = np.argmax(y_prob)
preds_dict = {class_names[i]: float(y_prob[0, i]) for i in range(len(class_names))}
name = class_names[class_idx]
return name, preds_dict
race_dict = {
"African-American":0,
"Asian":1,
"Black":2,
"Black African":3,"Black Caribbean":4,"Black or African American":5,"Black other":6 ,
"Caucasian":7,"Chinese":8,"Han Chinese":9,"Hispanic":10,"Indian":11,"Intermediate":12,
"Japanese":13,"Korean":14, "Malay":15, "Other":16, "Other (Black British)":17, "Other (Hungarian)":18, "Other Mixed Race":19, "White":20}
age_dict = {
"10-19":0,
"20-29":1,
"30-39":2,
"40-49":3,"50-59":4,"60-69":5,"70-79":6,
"80-89":7,"90+":8}
genotype_dict = {"A/A":0, "A/G":1, "G/G":2}
# Invert dictionaries for decoding
genotype_dict_inverse = {v: k for k, v in genotype_dict.items()}
race_dict_inverse = {v: k for k, v in race_dict.items()}
age_dict_inverse = {v: k for k, v in age_dict.items()}
# Create Gradio interface
gender_choices = [("Male", 1), ("Female", 0)]
gender_module = gr.Dropdown(choices=gender_choices, label="Gender")
# Assuming race_choices, age_choices, genotype_choices are already defined
race_module = gr.Dropdown(choices=list(race_dict.items()), label="Race")
age_module = gr.Dropdown(choices=list(age_dict.items()), label="Age Group")
genotype_module = gr.Dropdown(choices=list(genotype_dict.items()), label="Genotype")
height_module = gr.Number(label="Height")
weight_module = gr.Number(label="Weight")
diabetes_module = gr.Number(label="Diabetes")
simvastatin_module = gr.Radio(choices=[0, 1], label="Simvastatin")
amiodarone_module = gr.Radio(choices=[0, 1], label="Amiodarone")
inr_module = gr.Number(label="INR Reported")
algorithm_module = gr.Dropdown(choices=["Random Forest", "Decision Tree", "MLP", "KNN"], label="Algorithm")
output_module1 = gr.Textbox(label="Predicted Class")
output_module2 = gr.Label(label="Predicted Probability")
iface = gr.Interface(fn=predict_warfarin_dose,
inputs=[gender_module, race_module, age_module, height_module, weight_module, diabetes_module,
simvastatin_module, amiodarone_module, genotype_module, inr_module, algorithm_module],
outputs=[output_module1, output_module2])
iface.launch(debug=True,share=True) |