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import gradio as gr
import pandas as pd
import numpy as np
import joblib
# Load the model and scaler
model = joblib.load('catboost_model.pkl')
scaler = joblib.load('scaler.pkl')
# Define feature lists (make sure the order is consistent with training)
cat_features = ['Gender', 'Race', 'Education', 'Smoke', 'Alcohol']
num_features = ['Age', 'BMI', 'PIR', 'MPAH', 'PFDE', 'PFHxS', 'PFNA', 'PFOA', 'PFOS', 'PFUA']
# Define the prediction function
def predict_ra(gender, race, education, smoke, alcohol, age, bmi, pir, mpah, pfde, pfhxs, pfna, pfoa, pfos, pfua):
"""
Predicts Rheumatoid arthritis risk based on input features.
"""
# 1. Create a DataFrame
input_data = pd.DataFrame({
'Gender': [gender],
'Race': [race],
'Education': [education],
'Smoke': [smoke],
'Alcohol': [alcohol],
'Age': [age],
'BMI': [bmi],
'PIR': [pir],
'MPAH': [mpah],
'PFDE': [pfde],
'PFHxS': [pfhxs],
'PFNA': [pfna],
'PFOA': [pfoa],
'PFOS': [pfos],
'PFUA': [pfua]
})
# 2. Standardize numerical features
input_data[num_features] = scaler.transform(input_data[num_features])
# 3. Make a prediction
prediction = model.predict(input_data)[0]
probability = model.predict_proba(input_data)[0][1]
# 4. Format the output
if prediction == 1:
result_text = "Prediction: Rheumatoid arthritis"
else:
result_text = "Prediction: Healthy"
return f"{result_text}\nRisk Probability: {probability:.2%}"
# Define the Gradio interface using gr.Blocks() for layout flexibility
with gr.Blocks() as iface:
gr.Markdown("# Rheumatoid arthritis Risk Prediction Calculator") # Title
gr.Markdown("Enter patient information to predict Rheumatoid arthritis risk.") # Description
with gr.Row(): # Layout: Use Rows and Columns for organization
with gr.Column():
gr.Markdown("## Basic Information")
gender = gr.Radio([1, 2], label="Gender", info="1: Male, 2: Female", value=1)
race = gr.Dropdown([1, 2, 3, 4, 5], label="Race", info="1: Mexican American, 2: Other Hispanic, 3: Non-Hispanic White, 4: Non-Hispanic Black, 5: Other Race", value=1)
education = gr.Radio([0, 1], label="Education Level", info="0: High school or below, 1: Above high school", value=0)
smoke = gr.Checkbox(label="Smoking", info="Does the patient smoke?")
alcohol = gr.Checkbox(label="Alcohol Consumption", info="Does the patient consume alcohol?")
with gr.Column():
gr.Markdown("## Physical Indicators")
age = gr.Number(label="Age", value=30)
bmi = gr.Number(label="BMI", value=22.0)
pir = gr.Number(label="PIR", value=2.0)
mpah = gr.Number(label="MPAH (ng/mL)", value=0.5)
pfde = gr.Number(label="PFDE (ng/mL)", value=0.5)
pfhxs = gr.Number(label="PFHxS (ng/mL)", value=1.0)
pfna = gr.Number(label="PFNA (ng/mL)", value=0.5)
pfoa = gr.Number(label="PFOA (ng/mL)", value=2.0)
pfos = gr.Number(label="PFOS (ng/mL)", value=5.0)
pfua = gr.Number(label="PFUA (ng/mL)", value=0.5)
predict_button = gr.Button("Predict") # Prediction button
output_textbox = gr.Textbox(label="Prediction Result") # Output text box
predict_button.click(predict_ra,
inputs=[gender, race, education, smoke, alcohol, age, bmi, pir, mpah, pfde, pfhxs, pfna, pfoa, pfos, pfua],
outputs=output_textbox)
# Launch the Gradio app
iface.launch() |