app.py

import gradio as gr
import numpy as np
import joblib

# Load the model and scaler
model = joblib.load('logistic_regression_model.pkl')
scaler = joblib.load('scaler.pkl')

def initial_risk_check(features):
    # Scale and predict risk based on the features
    features_scaled = scaler.transform([features])
    prediction = model.predict_proba(features_scaled)[:, 1]
    
    # Define risk thresholds
    high_risk_threshold = 0.75
    moderate_risk_threshold = 0.5
    
    # Determine risk level and recommendation
    if prediction >= high_risk_threshold:
        return "High risk. Please consult a doctor immediately.", prediction
    elif prediction >= moderate_risk_threshold:
        return "Moderate risk. Consider lifestyle changes and monitor your health closely.", prediction
    else:
        return "Low risk. Maintain a healthy lifestyle and schedule routine check-ups.", prediction

def launch_interface():
    with gr.Blocks(css="""
        .gradio-container {
            font-family: Arial, sans-serif;
            background-color: #f9f9f9;
            color: #333;
        }
        .gr-button {
            background-color: #007bff;
            color: white;
        }
        .gr-button:hover {
            background-color: #0056b3;
        }
        .gr-textbox {
            margin-bottom: 1em;
        }
    """) as app:
        
        gr.Markdown("""
            # Gestational Diabetes Risk Calculator
            This calculator helps you assess your risk for gestational diabetes. Enter your details below and get an initial risk assessment. 
            If you are above medium risk, please provide more detailed information for a more accurate assessment.
        """)
        
        with gr.Row():
            pregnancies = gr.Number(label="Number of Pregnancies")
            age = gr.Number(label="Age")
            bmi = gr.Number(label="BMI")
            detailed = gr.Checkbox(label="Click if above medium risk for more detailed input", value=False)
        
        with gr.Row():
            ogtt = gr.Number(label="OGTT", visible=False)
            sys_bp = gr.Number(label="Systolic Blood Pressure", visible=False)
            hdl = gr.Number(label="HDL", visible=False)  # Assuming HDL is used for DPF

        submit_button = gr.Button("Submit")
        output = gr.Textbox(label="Result")

        def update_output(pregnancies, age, bmi, detailed, ogtt, sys_bp, hdl):
            if detailed:
                features = [pregnancies, ogtt, sys_bp, bmi, hdl, age]
            else:
                features = [pregnancies, 120, 120, bmi, 0.5, age]  # Default values for detailed inputs
            result, prediction = initial_risk_check(features)
            return result

        def toggle_visibility(detailed):
            return {
                ogtt: gr.update(visible=detailed),
                sys_bp: gr.update(visible=detailed),
                hdl: gr.update(visible=detailed)
            }

        detailed.change(
            toggle_visibility, 
            inputs=[detailed],
            outputs=[ogtt, sys_bp, hdl]
        )

        submit_button.click(
            update_output, 
            inputs=[pregnancies, age, bmi, detailed, ogtt, sys_bp, hdl],
            outputs=output
        )

    app.launch()

if __name__ == "__main__":
    launch_interface()