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.gradio/certificate.pem

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README.md

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 ---
-title: SHAP Explain Feature
-emoji: 📚
-colorFrom: purple
-colorTo: red
-sdk: gradio
-sdk_version: 6.0.1
+title: SHAP_explain_feature
 app_file: app.py
-pinned: false
+sdk: gradio
+sdk_version: 5.47.2
 ---
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+import gradio as gr
+import shap
+import xgboost as xgb
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+
+# ---------------------------
+# TRAIN A TINY MODEL
+# ---------------------------
+np.random.seed(42)
+df = pd.DataFrame({
+    "age": np.random.randint(20, 80, 200),
+    "bmi": np.random.uniform(18, 35, 200),
+    "steps": np.random.randint(2000, 14000, 200),
+})
+df["cost"] = df["age"]*20 + df["bmi"]*40 - df["steps"]*0.4 + np.random.normal(0,200,200)
+
+X = df[["age","bmi","steps"]]
+y = df["cost"]
+
+model = xgb.XGBRegressor(n_estimators=40, max_depth=3)
+model.fit(X, y)
+
+explainer = shap.Explainer(model, X)
+
+# ---------------------------
+# GRADIO INFERENCE FUNCTION
+# ---------------------------
+def explain_cost(age, bmi, steps):
+    input_data = pd.DataFrame([{
+        "age": age,
+        "bmi": bmi,
+        "steps": steps
+    }])
+
+    shap_values = explainer(input_data)
+
+    # Create SHAP waterfall plot
+    plt.figure(figsize=(8,6))
+    shap.waterfall_plot(shap_values[0], show=False)
+    plt.tight_layout()
+
+    # Save plot to temporary file
+    plot_path = "shap_plot.png"
+    plt.savefig(plot_path)
+    plt.close()
+
+    pred = model.predict(input_data)[0]
+    return pred, plot_path
+
+
+# ---------------------------
+# GRADIO UI
+# ---------------------------
+with gr.Blocks() as demo:
+    gr.Markdown("# SHAP Explainability Demo")
+
+    age = gr.Slider(20, 80, value=40, label="Age")
+    bmi = gr.Slider(18, 35, value=25, label="BMI")
+    steps = gr.Slider(2000, 15000, value=8000, label="Daily Steps")
+
+    btn = gr.Button("Explain Prediction")
+
+    pred_output = gr.Number(label="Predicted Cost ($)")
+    shap_output = gr.Image(label="SHAP Waterfall")
+
+    btn.click(explain_cost, [age, bmi, steps], [pred_output, shap_output])
+
+demo.launch(share=True)

requirements.txt

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+gradio==4.29.0
+xgboost
+shap
+matplotlib
+pandas
+numpy