app.py

import gradio as gr
import joblib

# Load the saved model
model = joblib.load('rdf_model.pkl')

# Define the function for prediction
def predict(Age, hPostMen, Parity, hEndoMet, hTubeLig, hOCP, Urea, TG, FBS, AoMen):
    # Prepare the input data as a single sample
    input_data = [[Age, hPostMen, Parity, hEndoMet, hTubeLig, hOCP, Urea, TG, FBS, AoMen]]
    
    # Get the prediction
    prediction = model.predict(input_data)[0]
    
    # Return the result
    return f"Predicted probability of detected mass is {'Malignant' if prediction == 1 else 'Benign'}"

# Define the Gradio interface
iface = gr.Interface(
    predict,
    [
        gr.inputs.Number(label="Age (Age in years)"),
        gr.inputs.Radio([0, 1], label="Menopause Status: 0 - No, 1 - Yes"),
        gr.inputs.Radio([1, 2, 3], label="Parity: 1 - Nulliparous, 2 - Uniparous, 3 - Multiparous"),
        gr.inputs.Radio([0, 1], label="History of Endometriosis: 0 - No, 1 - Yes"),
        gr.inputs.Radio([0, 1], label="History of Tubal Ligation: 0 - No, 1 - Yes"),
        gr.inputs.Radio([0, 1], label="History of OCP usage: 0 - No, 1 - Yes"),
        gr.inputs.Number(label="Serum Urea"),
        gr.inputs.Number(label="Serum Triglyceride"),
        gr.inputs.Number(label="Fasting Blood Sugar"),
        gr.inputs.Radio([1, 2, 3], label="Age of Menarche: 1 - <10, 2: 11-13, 3: >13"),
    ],
    gr.outputs.Textbox(label="Prediction"),
)

# Launch the interface
iface.launch()