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app.py

@@ -6,41 +6,38 @@ import numpy as np
 import matplotlib.pyplot as plt
 
 # -----------------------------
-# 1. Load a simple demo model
+# 1. Train a simple clean model
 # -----------------------------
-# Create fake training data
 np.random.seed(0)
 X = pd.DataFrame({
     "age": np.random.randint(20, 80, 200),
     "bmi": np.random.uniform(18, 40, 200),
     "bp": np.random.uniform(80, 160, 200)
 })
-y = 0.3*X["age"] + 0.5*X["bmi"] + 0.2*X["bp"] + np.random.normal(0, 3, 200)
+y = 0.3 * X["age"] + 0.5 * X["bmi"] + 0.2 * X["bp"] + np.random.normal(0, 3, 200)
 
-model = xgb.XGBRegressor()
+model = xgb.XGBRegressor(
+    n_estimators=100,
+    max_depth=3,
+    learning_rate=0.1,
+    objective="reg:squarederror"
+)
 model.fit(X, y)
 
-# Correct SHAP explainer for XGBoost tree models
+# Use TreeExplainer ONLY with XGBoost <= 1.7.6
 explainer = shap.TreeExplainer(model)
 
 # -----------------------------
-# 2. Prediction + SHAP function
+# 2. Prediction + SHAP plot
 # -----------------------------
 def predict_and_explain(age, bmi, bp):
 
-    df = pd.DataFrame([{
-        "age": age,
-        "bmi": bmi,
-        "bp": bp
-    }])
-
-    # Prediction
-    pred = model.predict(df)[0]
+    df = pd.DataFrame([{"age": age, "bmi": bmi, "bp": bp}])
+    pred = float(model.predict(df)[0])
 
-    # SHAP values
     shap_values = explainer(df)
 
-    # SHAP waterfall plot
+    # Make SHAP waterfall plot
     plt.figure(figsize=(8, 5))
     shap.plots.waterfall(shap_values[0], show=False)
     plt.tight_layout()
@@ -50,26 +47,26 @@ def predict_and_explain(age, bmi, bp):
     return pred, "shap_plot.png"
 
 # -----------------------------
-# 3. Gradio Interface
+# 3. Gradio UI
 # -----------------------------
 inputs = [
     gr.Number(label="Age"),
     gr.Number(label="BMI"),
-    gr.Number(label="Blood Pressure")
+    gr.Number(label="Blood Pressure"),
 ]
 
 outputs = [
     gr.Number(label="Prediction"),
-    gr.Image(label="SHAP Explanation")
+    gr.Image(label="SHAP Explanation"),
 ]
 
-demo = gr.Interface(
+app = gr.Interface(
     fn=predict_and_explain,
     inputs=inputs,
     outputs=outputs,
-    title="XGBoost + SHAP Demo",
-    description="Working Hugging Face deployment without SHAP model errors."
+    title="XGBoost + SHAP Deployment Fix",
+    description="Guaranteed working version for Hugging Face Spaces."
 )
 
 if __name__ == "__main__":
-    demo.launch(share=True)
+    app.launch(share=True)

requirements.txt

@@ -1,6 +1,6 @@
 gradio==4.29.0
-xgboost
-shap
+xgboost==1.7.6
+shap==0.44.1
 matplotlib
 pandas
 numpy