Upload app.py

app.py

@@ -4,7 +4,7 @@ import torch
 import shap
 import matplotlib.pyplot as plt
 import joblib
-
+import pandas as pd
 # Load scalers and model
 @st.cache_resource
 def load_resources():
@@ -54,7 +54,7 @@ with torch.no_grad():
     prediction = model(torch.tensor(input_scaled, dtype=torch.float32)).numpy()
     prediction_unscaled = scaler_y.inverse_transform(prediction)
 
-st.write(f"Predicted Resilience Modulus: {prediction_unscaled[0][0]:.2f}")
+st.write(f"Predicted Resilience Modulus: {prediction_unscaled[0][0]:.2f} MPa")
 
 # SHAP explanation
 if st.button("Explain Prediction"):
@@ -81,9 +81,12 @@ if st.button("Explain Prediction"):
     shap.plots.waterfall(explanation, show=False)
     st.pyplot(fig)
     
-    st.write(f"Base value (unscaled): {([[expected_value]])[0][0]:.4f}")
-    st.write(f"Output value (unscaled): {prediction_unscaled[0][0]:.4f}")
+    st.write(f"Base value (unscaled): {([[expected_value]])[0][0]:.2f} MPa")
+    st.write(f"Output value (unscaled): {prediction_unscaled[0][0]:.2f} MPa")
     
     st.write("\nFeature contributions (unscaled):")
-    for name, value in zip(feature_names, shap_values_single):
-        st.write(f"{name}: {value:.4f}")
+    feature_contributions = pd.DataFrame({
+        'Contribution': shap_values_single
+    }, index=feature_names)
+    feature_contributions['Contribution'] = feature_contributions['Contribution'].round(4)
+    st.table(feature_contributions)