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import gradio as gr
import pandas as pd
import numpy as np
import joblib
# Load trained components
model = joblib.load("voting_model_multiclass.pkl")
scaler = joblib.load("scaler.pkl")
label_encoders = joblib.load("feature_label_encoders.pkl")
target_encoder = joblib.load("target_label_encoder.pkl")
# Feature order used during training
feature_order = [
'Age', 'Sex', 'Socioeconomic_Status', 'Vitamin_D_Level_ng/ml',
'Vitamin_D_Status', 'Vitamin_D_Supplemented', 'Bacterial_Infection',
'Viral_Infection', 'Co_Infection', 'IL6_pg/ml', 'IL8_pg/ml'
]
def predict_arti_severity(
age, sex, ses, vit_d_level, vit_d_status, vit_d_supp,
bacterial, viral, co_infect, il6, il8
):
# Create a single row DataFrame
input_data = pd.DataFrame([[
age, sex, ses, vit_d_level, vit_d_status, vit_d_supp,
bacterial, viral, co_infect, il6, il8
]], columns=feature_order)
# Encode categorical columns
for col in input_data.select_dtypes(include='object').columns:
le = label_encoders[col]
input_data[col] = le.transform(input_data[col])
# Scale the input
input_scaled = scaler.transform(input_data)
# Predict
pred = model.predict(input_scaled)[0]
pred_label = target_encoder.inverse_transform([pred])[0]
return f"Predicted ARTI Severity: {pred_label}"
# Define Gradio interface
interface = gr.Interface(
fn=predict_arti_severity,
inputs=[
gr.Number(label="Age (years)", value=2),
gr.Radio(choices=['Male', 'Female'], label="Sex"),
gr.Radio(choices=['Low', 'Middle', 'High'], label="Socioeconomic Status"),
gr.Number(label="Vitamin D Level (ng/ml)", value=20),
gr.Radio(choices=['Deficient', 'Insufficient', 'Sufficient'], label="Vitamin D Status"),
gr.Radio(choices=['Yes', 'No'], label="Vitamin D Supplemented"),
gr.Dropdown(choices=['Streptococcus pneumoniae', 'Klebsiella pneumoniae', 'Staphylococcus aureus', 'None'], label="Bacterial Infection"),
gr.Dropdown(choices=['RSV', 'Influenza A', 'Influenza B', 'Adenovirus', 'Rhinovirus', 'Metapneumovirus', 'Parainfluenza', 'None'], label="Viral Infection"),
gr.Radio(choices=['Yes', 'No'], label="Co-Infection"),
gr.Number(label="IL-6 (pg/ml)", value=25),
gr.Number(label="IL-8 (pg/ml)", value=40)
],
outputs=gr.Textbox(label="Prediction Result"),
title="ARTI Severity Predictor",
description="Predict ARTI severity in children based on vitamin D, infection data, and inflammation markers."
)
# Launch app
interface.launch()
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