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import gradio as gr
import joblib
# Define a function for the Gradio interface
def predict_risk(Age, hOCP, hMisCrg, hHRT, CA125, HE4, FBS, USG):
# Load the trained model
model = joblib.load('RDF_OvCa_Final.joblib')
# Create a DataFrame with the input data
input_data = pd.DataFrame({
'Age': [Age],
'hOCP': [hOCP],
'hMisCrg': [hMisCrg],
'hHRT': [hHRT],
'CA125': [CA125],
'HE4': [HE4],
'FBS': [FBS],
'USG': [USG]
})
# Predict the probability of malignancy (class 1)
probability = model.predict_proba(input_data)[:, 1][0]
# Scale the risk score to -1 to +1
risk_score = 2 * probability - 1
# Determine the predicted status
status = "malignant" if probability >= cutoff else "benign"
result = f"The OvaCa Risk Score is {risk_score:.2f}. Based on it, the probability is more of {status} ({1 if status == 'malignant' else 0})."
return result
# Define the Gradio interface
iface = gr.Interface(
fn=predict_risk,
inputs=[
gr.inputs.Number(label="Age of patient in years"),
gr.inputs.Radio([0, 1], label="History of OCP intake (0: No 1: Yes)"),
gr.inputs.Radio([0, 1], label="History of Miscarriage (0: No 1: Yes)"),
gr.inputs.Radio([0, 1], label="History of HRT (0: No 1: Yes)"),
gr.inputs.Number(label="Serum CA125 level"),
gr.inputs.Number(label="Serum HE4 level"),
gr.inputs.Number(label="Serum Fasting Blood Sugar Level"),
gr.inputs.Radio([0, 1], label="USG Finding (0: Absent or Single Finding 1: More Than 1 Findings)")
],
outputs=gr.outputs.Textbox(label="Result (Risk Score on a Scale of -1 to +1, where >0 ~ Malignant)")
)
# Launch the Gradio interface
iface.launch()
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