Create app.py

app.py

@@ -0,0 +1,104 @@
+import pandas as pd
+import numpy as np
+import gradio as gr
+import joblib  # For loading the model
+import matplotlib.pyplot as plt
+import io
+from PIL import Image
+
+# Load the scaler and model
+scaler_path = "scaler.pkl"
+model_path = "ebm_model.pkl"
+
+# Load the pre-trained scaler and model
+scaler = joblib.load(scaler_path)
+ebm = joblib.load(model_path)
+print("Loaded saved model and scaler.")
+
+# Global variable to store the most recent input data for explanation
+last_input_data_scaled = None
+
+# Define prediction function
+def predict_strength(cement, blast_furnace_slag, fly_ash, water, superplasticizer,
+                     coarse_aggregate, fine_aggregate, age):
+    global last_input_data_scaled
+    input_data = pd.DataFrame({
+        'cement': [cement], 'blast_furnace_slag': [blast_furnace_slag],
+        'fly_ash': [fly_ash], 'water': [water],
+        'superplasticizer': [superplasticizer],
+        'coarse_aggregate': [coarse_aggregate],
+        'fine_aggregate': [fine_aggregate], 'age': [age]
+    })
+    last_input_data_scaled = scaler.transform(input_data)
+    prediction = ebm.predict(last_input_data_scaled)
+    return prediction[0]
+
+# Explanation function for enhanced visual
+def show_local_explanation():
+    if last_input_data_scaled is not None:
+        local_exp = ebm.explain_local(last_input_data_scaled)
+        contributions = local_exp.data(0)['scores']
+        names = local_exp.data(0)['names']
+
+        # Enhanced Plotting
+        fig, ax = plt.subplots(figsize=(10, 6))
+        colors = ['red' if x < 0 else 'green' for x in contributions]
+        ax.barh(names, contributions, color=colors)
+        ax.set_xlabel('Contribution to Prediction')
+        ax.set_title('Local Explanation for the Most Recent Prediction')
+
+        # Save plot to a buffer
+        buf = io.BytesIO()
+        plt.savefig(buf, format='png', bbox_inches='tight')
+        buf.seek(0)
+        plt.close()
+
+        # Load image for display
+        img = Image.open(buf)
+        img_array = np.array(img)
+        return img_array
+    else:
+        return "No prediction has been made yet."
+
+# Gradio interface setup with introduction and instructions
+with gr.Blocks() as app:
+    gr.Markdown("## Concrete Strength Prediction App")
+    gr.Markdown("""
+    This app predicts the compressive strength of concrete based on its composition using the Explainable Boosting Machine (EBM).
+    EBM is a transparent, interpretable machine learning model that combines the power of boosting techniques with interpretable models,
+    making it easier to explain prediction outcomes.
+    """)
+    gr.Markdown("### Instructions")
+    gr.Markdown("""
+    - Enter the composition of the concrete in the input fields.
+    - Click 'Predict Concrete Strength' to see the predicted strength.
+    - Click 'Show Local Explanation' to see the contribution of each feature to the prediction.
+    """)
+    
+    with gr.Row():
+        cement = gr.Number(label="Cement")
+        slag = gr.Number(label="Blast Furnace Slag")
+        fly_ash = gr.Number(label="Fly Ash")
+        water = gr.Number(label="Water")
+        superplasticizer = gr.Number(label="Superplasticizer")
+        coarse_agg = gr.Number(label="Coarse Aggregate")
+        fine_agg = gr.Number(label="Fine Aggregate")
+        age = gr.Number(label="Age")
+
+    predict_btn = gr.Button("Predict Concrete Strength")
+    explanation_btn = gr.Button("Show Local Explanation")
+    result = gr.Textbox(label="Predicted Concrete Strength")
+    local_image = gr.Image(label="Local Explanation", type="numpy")
+
+    predict_btn.click(
+        fn=predict_strength,
+        inputs=[cement, slag, fly_ash, water, superplasticizer, coarse_agg, fine_agg, age],
+        outputs=result
+    )
+    explanation_btn.click(
+        fn=show_local_explanation,
+        inputs=[],
+        outputs=local_image
+    )
+
+app.launch()