Create app.py

app.py

@@ -0,0 +1,70 @@
+import shap
+import gradio as gr
+import numpy as np
+import shap
+import joblib
+import pandas as pd
+import matplotlib.pyplot as plt
+
+model = joblib.load('gdsc_xgboost_model.pkl')
+
+def predict_ic50(AUC, Z_SCORE, DRUG_ID, TARGET, TARGET_PATHWAY, Growth_Properties_Suspension):
+    # Create DataFrame for input
+    input_data = pd.DataFrame([{
+        'AUC': AUC,
+        'Z_SCORE': Z_SCORE,
+        'DRUG_ID': DRUG_ID,
+        'TARGET': TARGET,
+        'TARGET_PATHWAY': TARGET_PATHWAY,
+        'Growth Properties_Suspension': Growth_Properties_Suspension
+    }])
+
+    # One-hot encode categorical features if necessary
+    input_data = pd.get_dummies(input_data)
+
+    # Align input with model features
+    model_features = model.get_booster().feature_names
+    for feature in model_features:
+        if feature not in input_data.columns:
+            input_data[feature] = 0  # Add missing features with 0
+
+    input_data = input_data[model_features]
+
+    # Predict IC50
+    ic50_pred = model.predict(input_data)[0]
+
+    # SHAP Explanation
+    explainer = shap.Explainer(model)
+    shap_values = explainer(input_data)
+
+    # Plot SHAP explanation
+    plt.figure(figsize=(10, 6))
+    shap.plots.waterfall(shap_values[0], max_display=10)
+    plt.title("SHAP Explanation for Prediction")
+    plt.savefig("shap_plot.png")
+    plt.close()
+
+    return f"Predicted LN_IC50: {ic50_pred:.3f}", "shap_plot.png"
+
+inputs = [
+    gr.Number(label="AUC (0.5 - 1.5)", value=0.85, info="Area Under Curve - Typically 0.5 to 1.5"),
+    gr.Number(label="Z_SCORE (-2 to 2)", value=0.45, info="Z-Score for dose-response curve"),
+    gr.Number(label="DRUG_ID (Numeric Code)", value=1003, info="Unique identifier for the drug"),
+    gr.Textbox(label="TARGET", value="MTORC1", placeholder="e.g., MTORC1", info="Gene or protein targeted by the drug"),
+    gr.Textbox(label="TARGET_PATHWAY", value="PI3K/MTOR signaling", placeholder="e.g., PI3K/MTOR signaling", info="Biological pathway affected"),
+    gr.Checkbox(label="Growth Properties - Suspension", value=False, info="Check if cells grow in suspension")
+]
+
+outputs = [
+    gr.Textbox(label="Predicted LN_IC50"),
+    gr.Image(label="SHAP Explanation")
+]
+
+gr.Interface(
+    fn=predict_ic50,
+    inputs=inputs,
+    outputs=outputs,
+    title="GDSC Drug Sensitivity Predictor",
+    description="Predict LN_IC50 for cancer drug response and visualize feature impact using SHAP. Please follow the input guidelines for accurate predictions.",
+    theme="default"
+).launch()