Create main.py

main.py

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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()