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app.py

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+import numpy as np
+import pandas as pd
+from keras.models import load_model
+from sklearn.preprocessing import StandardScaler
+import matplotlib.pyplot as plt
+import pickle
+import gradio as gr
+import io
+import requests
+
+# Load the model and scaler
+transformer = load_model('transformer_pile.h5')
+sc_f = pickle.load(open('scaler_f.pkl', 'rb'))
+
+# URL to the default Excel file (replace with your actual URL)
+DEFAULT_EXCEL_URL = "data_pile.xlsx"
+
+def download_default_excel():
+    response = requests.get(DEFAULT_EXCEL_URL)
+    return io.BytesIO(response.content)
+
+def process_excel(file):
+    if isinstance(file, str) and file == "default":
+        file = download_default_excel()
+    
+    df = pd.read_excel(file, sheet_name='soil')
+    df_y = pd.read_excel(file, sheet_name='pile')
+    df_p = pd.read_excel(file, sheet_name='pile_length')
+
+    data = np.array(df)
+    data_y = np.array(df_y)
+    data_pile = np.array(df_p)[:, 1:61]
+    x_feature = data_y[:, 0:4]
+    bh = data[:, 1:61]
+    bh2 = data[:, 61:122] / 2
+
+    x_train = bh / 50
+    x_feature = sc_f.transform(x_feature)
+    soil_data = np.stack([x_train, bh2, data_pile], axis=2)
+    return soil_data
+
+def predict_pile(file_choice, uploaded_file, pile_length, section_width, section_length, pile_type):
+    if file_choice == "default":
+        file = "default"
+    else:
+        if uploaded_file is None:
+            return "Please upload an Excel file or choose the default option."
+        file = uploaded_file
+    
+    X = process_excel(file)
+    
+    # Convert pile type to numerical value
+    pile_type_num = 1 if pile_type == "Circular" else 2
+    
+    # Create feature array
+    feature = np.array([pile_length, section_width, section_length, pile_type_num])
+    feature = np.reshape(feature, (1, -1))
+    fd = sc_f.transform(feature)
+    x_feature = fd
+
+    # Use the first sample for demonstration
+    Xd = X[0:1]
+
+    X_train_CNN = np.zeros((Xd.shape[0], Xd.shape[1], x_feature.shape[1] + 3))
+    X_train_CNN[:, :, 0:3] = Xd
+    for i in range(Xd.shape[0]):
+        X_train_CNN[i, :, 3] = x_feature[i, 1]
+        X_train_CNN[i, :, 4] = x_feature[i, 2]
+        X_train_CNN[i, :, 5] = x_feature[i, 3]
+        X_train_CNN[i, :, 6] = x_feature[i, 3]
+    
+    XT = X_train_CNN
+    print(XT.shape)
+    y_ini = np.zeros((1, 40))
+    y_ini[0, 0] = 0
+
+    for step in range(39):
+        y = transformer.predict([XT, y_ini, fd])
+        y_ini[0, step+1] = y
+
+    y_pred = y_ini * 40000
+
+    plt.figure(figsize=(10, 6))
+    ydist = range(1, 41)
+    plt.plot(ydist, y_pred[0], color='blue', label='predict')
+    plt.legend()
+    plt.xlabel("Deformation")
+    plt.ylabel("Load")
+    plt.title(f"Pile Prediction (Length: {pile_length}m, {pile_type})")
+    
+    return plt
+
+iface = gr.Interface(
+    fn=predict_pile,
+    inputs=[
+        gr.Radio(["default", "upload"], label="Choose Excel File Source", value="default"),
+        gr.File(label="Upload Excel File", type="binary", visible=False),
+        gr.Number(label="Pile Length (m)", value=30),
+        gr.Number(label="Section Width (m)", value=1),
+        gr.Number(label="Section Length (m)", value=1),
+        gr.Radio(["Circular", "Barrette"], label="Pile Type", value="Circular")
+    ],
+    outputs="plot",
+    title="Pile Prediction Model",
+    description="Choose the default Excel file or upload your own, then enter pile characteristics to predict pile behavior.",
+    live=False
+)
+
+def update_file_input(choice):
+    return gr.update(visible=choice == "upload")
+
+iface.inputs[0].change(update_file_input, inputs=[iface.inputs[0]], outputs=[iface.inputs[1]])
+
+iface.launch()

requirements.txt

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+numpy
+pandas
+keras
+scikit-learn
+matplotlib
+gradio
+openpyxl
+requests
+tensorflow
+keras

scaler_f.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:4816bd84cf7af78609fbe1b16c487d39abec8b02670dccc2de5b7db5d748f82a
+size 640

transformer_pile.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:9aae751caaa685d61c5fe04379987b38f8b212eed51cee6a3d6d56b4313ce1cf
+size 7422544