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app.py

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+
+import gradio as gr
+import joblib
+import shap
+import numpy as np
+import pandas as pd
+import json
+
+model = joblib.load("model_v6.pkl")
+
+with open("feature_order.json") as f:
+    features = json.load(f)
+
+explainer = shap.TreeExplainer(model)
+
+def predict(log_AF, hydro_diff, protein_pos, charge_change, grantham):
+
+    X = pd.DataFrame([{
+        "log_AF": log_AF,
+        "Hydro_diff": hydro_diff,
+        "Protein_pos_norm": protein_pos,
+        "Charge_change": charge_change,
+        "Grantham": grantham
+    }])
+
+    prob = model.predict_proba(X)[0][1]
+    shap_vals = explainer.shap_values(X)[0]
+
+    explanation = ""
+    for i, feat in enumerate(features):
+        explanation += f"{feat}: value={X.iloc[0][feat]:.4f}, SHAP={shap_vals[i]:+.4f}\n"
+
+    return f"Pathogenic Probability: {prob:.4f}", explanation
+
+demo = gr.Interface(
+    fn=predict,
+    inputs=[
+        gr.Number(label="log_AF"),
+        gr.Number(label="Hydrophobicity Difference"),
+        gr.Number(label="Protein Position Normalized (0–1)"),
+        gr.Number(label="Charge Change (0 or 1)"),
+        gr.Number(label="Grantham Score")
+    ],
+    outputs=[
+        gr.Textbox(label="Prediction"),
+        gr.Textbox(label="Feature Contributions")
+    ],
+    title="Explainable Mutation Pathogenicity Model v6",
+    description="XGBoost + SHAP Explainable Variant Predictor"
+)
+
+demo.launch()