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app2.py

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+import streamlit as st
+import numpy as np
+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():
+    scaler_X = joblib.load("scaler_X_DS.joblib")
+    scaler_y = joblib.load("scaler_y_DS.joblib")
+    
+    model = torch.jit.load("scripted_model_DS.pt")
+    model.eval()
+    
+    return scaler_X, scaler_y, model
+
+# Create a wrapper function for SHAP
+def model_wrapper(X):
+    with torch.no_grad():
+        X_tensor = torch.tensor(X, dtype=torch.float32)
+        output = model(X_tensor).numpy()
+    return scaler_y.inverse_transform(output)
+
+# Streamlit app
+st.title("Dynamic Stability Predictor")
+
+# Load resources
+scaler_X, scaler_y, model = load_resources()
+
+# Define feature names and default values
+feature_names = [
+    "25", "19", "12.5", "9.5", "4.75", "2.36", "1.18", "0.6", "0.3", "0.15", "0.075", "CA", "FA", "type"
+]
+default_values = [100, 100, 81.593, 68.395, 49.318, 29.283, 17.261, 14.257, 6.041, 3.000, 2.115, 0.600, 0.350, 1.0]
+
+# Input features
+st.sidebar.header("Input Features")
+input_features = {}
+for feature, default_value in zip(feature_names, default_values):
+    if feature == "type":
+        type_option = st.sidebar.selectbox(f"Enter {feature}", options=["1 - Limestone", "2 - Basalt"], index=0)
+        input_features[feature] = 1.0 if type_option == "1 - Limestone" else 2.0
+    else:
+        input_features[feature] = st.sidebar.number_input(f"Enter {feature}", value=default_value)
+
+# Create input array
+input_array = np.array([input_features[feature] for feature in feature_names]).reshape(1, -1)
+input_scaled = scaler_X.transform(input_array)
+
+# Make prediction
+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 Dynamic Stability: {prediction_unscaled[0][0]:.2f} pass/mm")
+
+# SHAP explanation
+if st.button("Explain Prediction"):
+    # Generate some random background data for SHAP
+    background_data = np.random.randn(100, 14)  # 100 samples, 14 features
+    background_data_scaled = scaler_X.transform(background_data)
+    
+    explainer = shap.KernelExplainer(model_wrapper, background_data_scaled)
+    shap_values = explainer.shap_values(input_scaled)
+    
+    shap_values_single = shap_values[0].flatten()
+    expected_value = explainer.expected_value[0]
+    
+    feature_values = [f"{x:.1f}" for x in input_array[0]]
+    
+    explanation = shap.Explanation(
+        values=shap_values_single,
+        base_values=expected_value,
+        data=feature_values,
+        feature_names=feature_names
+    )
+    
+    fig, ax = plt.subplots(figsize=(10, 6))
+    shap.plots.waterfall(explanation, show=False)
+    st.pyplot(fig)
+    
+    st.write(f"Base value (unscaled): {([[expected_value]])[0][0]:.2f} pass/mm")
+    st.write(f"Output value (unscaled): {prediction_unscaled[0][0]:.2f} pass/mm")
+    
+    st.write("\nFeature contributions (unscaled):")
+    feature_contributions = pd.DataFrame({
+        'Contribution': shap_values_single
+    }, index=feature_names)
+    feature_contributions['Contribution'] = feature_contributions['Contribution'].round(4)
+    st.table(feature_contributions)

requirements.txt

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+streamlit
+numpy
+torch
+shap
+matplotlib
+joblib

scaler_X_DS.joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:6835ccf71cbd062cc20230e43eb0166f6a8a7bd2f7cd65fc37e657873e778edb
+size 951

scaler_y_DS.joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:1dd90832e71904d2eab0440e1f0ff044fbf6329b098215b024febf47c7596e92
+size 623

scripted_model_DS.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:fa630b0d18893474bfadbe48873d929fbc464aaa7be2b34d1254e266196ea526
+size 1651715