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XGBoost_best_of_10.joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:86ec2890140b347fb2c78e4d47c1013897553efd52638abc0ff687ad9b3920c4
+size 126041

app.py

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+import numpy as np
+import joblib
+import gradio as gr
+import matplotlib.pyplot as plt
+
+# Load the pre-trained model
+model = joblib.load("XGBoost_best_of_10.joblib")  
+
+def predict_hc(mt_content, liquid_limit, plastic_limit, specific_gravity, 
+              initial_wc, temperature, dry_density_values):
+    warnings = []
+    predictions = []
+    single_pred = None
+    
+    # Process dry density values
+    try:
+        density_values = [float(x.strip()) for x in dry_density_values.split(',')]
+        if len(density_values) < 4 or len(density_values) > 8:
+            raise ValueError("Please enter 4 to 8 values separated by commas")
+    except ValueError as e:
+        return f"Error in dry density input: {str(e)}", None, None, None
+    
+    # Fixed features (same for all predictions)
+    fixed_features = np.array([
+        mt_content,
+        liquid_limit,
+        plastic_limit,
+        specific_gravity,
+        initial_wc,
+        temperature
+    ])
+    
+    # Define trained ranges for soil properties
+    trained_ranges = {
+        "Montmorillonite Content": (33.60, 92.00),
+        "Liquid Limit": (50.00, 500.00),
+        "Plastic Limit": (20.00, 200.00),
+        "Specific Gravity": (2.65, 2.82),
+        "Initial Water Content": (5.40, 21.10),
+        "Temperature": (20.00, 40.00),
+        "Dry Density": (1.30, 2.05),
+    }
+    
+    # Check input values against trained ranges
+    input_values = {
+        "Montmorillonite Content": mt_content,
+        "Liquid Limit": liquid_limit,
+        "Plastic Limit": plastic_limit,
+        "Specific Gravity": specific_gravity,
+        "Initial Water Content": initial_wc,
+        "Temperature": temperature,
+    }
+    
+    for feature, value in input_values.items():
+        min_val, max_val = trained_ranges[feature]
+        if value < min_val:
+            warnings.append(f"⚠️ {feature} is below the trained range ({min_val}-{max_val}).")
+        elif value > max_val:
+            warnings.append(f"⚠️ {feature} is above the trained range ({min_val}-{max_val}).")
+    
+    # Make predictions for all density values
+    for density in density_values:
+        # Check dry density range
+        min_density, max_density = trained_ranges["Dry Density"]
+        if density < min_density:
+            warnings.append(f"⚠️ Dry Density {density} is below the trained range ({min_density}-{max_density}).")
+        elif density > max_density:
+            warnings.append(f"⚠️ Dry Density {density} is above the trained range ({min_density}-{max_density}).")
+        
+        # Create input array
+        input_array = np.append(fixed_features, density).reshape(1, -1)
+        pred = model.predict(input_array)[0]
+        predictions.append(pred)
+    
+    # If only one density value provided, show single prediction
+    if len(density_values) == 1:
+        single_pred = predictions[0]
+    
+    # Create plot if multiple density values
+    plot_path_png = None
+    if len(density_values) > 1:
+        fig, ax = plt.subplots(figsize=(5, 4))
+        ax.plot(density_values, predictions, marker="o", linestyle="-", color="blue", markersize=8)
+        ax.set_xlabel("Dry Density [g/cm³]")
+        ax.set_ylabel("Saturated Hydraulic Conductivity (HC) [-]")
+        ax.grid(True, linestyle="--", alpha=0.5)
+        
+        plot_path_png = "HC_plot.png"
+        fig.savefig(plot_path_png, dpi=600, bbox_inches="tight")
+        plt.close(fig)
+    
+    # Format output
+    pred_text = ""
+    if single_pred is not None:
+        pred_text += f"Predicted Saturated HC: {single_pred:.4f} [-]\n\n"
+    else:
+        pred_text += "Predicted Saturated HC values:\n"
+        for d, p in zip(density_values, predictions):
+            pred_text += f"• At ρ = {d:.2f} g/cm³: HC = {p:.4f} [-]\n"
+        pred_text += "\n"
+    
+    if warnings:
+        pred_text += "\n".join(warnings)
+    
+    # Team text
+    marketing_text = (
+        "**Developed by:**  \nMuntasir Shehab*, Reza Taherdangkoo and Christoph Butscher  \n\n"
+        "**Institution:**  \nTU Bergakademie Freiberg, Institute of Geotechnics  \n"
+        "Gustav-Zeuner-Str. 1, Freiberg, 09599, Germany"
+    )
+    
+    return pred_text, plot_path_png, marketing_text
+
+# Input components
+inputs = [
+    gr.Number(label="Montmorillonite Content [%]", value=60, minimum=5.60, maximum=96.00, step=0.1),
+    gr.Number(label="Liquid Limit [%]", value=150, minimum=50.00, maximum=500.00, step=1),
+    gr.Number(label="Plastic Limit [%]", value=50, minimum=20.00, maximum=200.00, step=1),
+    gr.Number(label="Specific Gravity", value=2.7, minimum=2.15, maximum=2.92, step=0.01),
+    gr.Number(label="Initial Water Content [%]", value=10, minimum=5.40, maximum=21.10, step=0.1),
+    gr.Number(label="Temperature [°C]", value=25, minimum=20.00, maximum=40.00, step=0.1),
+    gr.Textbox(label="Dry Density values [g/cm³] (4-8 values, comma separated)", 
+              value="1.3, 1.5, 1.7, 1.9", 
+              placeholder="Enter 4-8 values separated by commas (e.g., 1.3, 1.5, 1.7, 1.9)"),
+]
+
+# Output components
+outputs = [
+    gr.Textbox(label="Prediction Results & Warnings"),
+    gr.Image(label="HC vs Dry Density Plot", type="filepath"),
+    gr.Markdown(label="About the Team")
+]
+
+# Launch interface
+gr.Interface(
+    fn=predict_hc,
+    inputs=inputs,
+    outputs=outputs,
+    title="Saturated Hydraulic Conductivity Prediction Model",
+    description="Predict saturated hydraulic conductivity (HC) based on soil properties. Enter 4-8 dry density values to see the trend."
+).launch(share=True)

requirements.txt

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+gradio
+joblib
+numpy
+xgboost