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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() |