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app2.py

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+import streamlit as st
+import torch
+import joblib
+import numpy as np
+from PIL import Image
+import shap
+import pandas as pd
+import matplotlib.pyplot as plt
+
+# Load the model and scalers
+@st.cache_resource
+def load_model_and_scalers():
+    loaded_model = torch.jit.load('model.pt')
+    loaded_scaler1 = joblib.load('scaler1.pkl')
+    loaded_scaler2 = joblib.load('scaler2.pkl')
+    return loaded_model, loaded_scaler1, loaded_scaler2
+
+loaded_model, loaded_scaler1, loaded_scaler2 = load_model_and_scalers()
+
+# Add this new function to load the training data
+@st.cache_data
+def load_training_data():
+    data = pd.read_excel("peak5.xlsx")
+    X = data.iloc[:, 0:7].to_numpy()
+    return X, data.columns[:7].tolist()
+
+X_train, feature_names = load_training_data()
+
+# Create a wrapper function for SHAP
+def f(X):
+    with torch.no_grad():
+        X_tensor = torch.tensor(X, dtype=torch.float32)
+        output = loaded_model(X_tensor).numpy()
+    return loaded_scaler2.inverse_transform(output)
+
+def add_logo(logo_path, size=(200, 150)):
+    logo = Image.open('logoAI.png')
+    logo = logo.resize(size)
+    st.image(logo, use_column_width=False)
+
+st.title('Explainable AI (XAI) for Predicting Peak Particle Velocity in Pile Driving on Bangkok Subsoil')
+add_logo("logoAI.png")
+
+# Create input fields
+st.header('Enter Input Values:')
+pile_width = st.number_input('Pile width (mm)', value=300.0,min_value=260.0,max_value=800.0)
+pile_length = st.number_input('Pile length (m)', value=18.0,min_value=15.0,max_value=20.0)
+weight = st.number_input('Weight (ton)', value=4.2,min_value=3.0,max_value=6.0)
+drop_height = st.number_input('Drop height (m)', value=0.5)
+distance = st.number_input('Distance (m)', min_value=3.0,value=9.0)
+location = st.selectbox('Location', ['On ground', 'On foundation', 'On building'], index=0)
+trigger = st.selectbox('Trigger', ['Longitudinal', 'Transverse', 'Vertical'], index=0)
+
+# Convert location and trigger to numerical values
+location_value = ['On ground', 'On foundation', 'On building'].index(location) + 1
+trigger_value = ['Longitudinal', 'Transverse', 'Vertical'].index(trigger) + 1
+
+# Button to make prediction
+if st.button('Make Prediction'):
+    # Prepare input data
+    input = np.array([pile_width, pile_length, weight, drop_height, distance, location_value, trigger_value])
+    inputx = np.reshape(input, (1, 7))
+    
+    # Transform input data
+    X_test1 = loaded_scaler1.transform(inputx).astype(np.float32)
+    X_test1 = torch.from_numpy(X_test1)
+    
+    # Make prediction
+    with torch.no_grad():
+        test_outputs = loaded_model(X_test1)
+        test_outputs2 = loaded_scaler2.inverse_transform(test_outputs.cpu())
+    
+    # Display results
+    st.subheader('Prediction Results:')
+    st.write(f"Peak Particle Velocity: {test_outputs2[0][0]:.2f} mm/s")
+    
+    # Add SHAP explanation
+    st.subheader('Explanation of Prediction:')
+    
+    # Create SHAP explainer
+    explainer = shap.KernelExplainer(f, shap.sample(loaded_scaler1.transform(X_train), 100))
+    shap_values = explainer.shap_values(X_test1.numpy())
+    
+    # Create SHAP waterfall plot
+    shap_values_single = shap_values[0].flatten()
+    expected_value = explainer.expected_value[0]
+    
+    # Convert feature values to strings
+    feature_values = [f"{x:.1f}" for x in inputx[0]]
+    
+    explanation = shap.Explanation(
+        values=shap_values_single,
+        base_values=expected_value,
+        data=feature_values,
+        feature_names=feature_names
+    )
+    
+    fig, ax = plt.subplots()
+    shap.plots.waterfall(explanation, show=False)
+    st.pyplot(fig)
+    
+ 
+
+st.sidebar.header('About')
+st.sidebar.info('This app uses a pre-trained PyTorch model to predict peak particle velocity based on user input. It is specifically designed for Bangkok sub-soil conditions.\n paper:https://arxiv.org/abs/2409.05918')

model.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:10fd93e7e130f38f36325646c41d7f6f72e9907041264de10a531084762c4e42
+size 117093

requirements.txt

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+torch
+joblib 
+gradio
+scikit-learn
+pillow

scaler1.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:2d7d2394b06f07aced32be1a4c7bb7839693bbe915ff9fb044c39ea453a8512e
+size 767

scaler2.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:d487a9f6ac4e940f5e747c3adae72a467c2e95049293456b7de6f0f21a684028
+size 623