Update app.py

app.py

@@ -1,4 +1,3 @@
-#app.py
 import pandas as pd
 import shap
 import matplotlib.pyplot as plt
@@ -12,31 +11,12 @@ try:
     print("Model loaded successfully for Gradio.")
 except FileNotFoundError:
     print("Error: 'machine_failure_prediction_model.joblib' not found. Ensure the model file is in the correct directory.")
-    loaded_model = None # Set to None if loading fails
+    loaded_model = None
 
 def predict_failure_with_shap(Type, air_temp, process_temp, rotational_speed, torque, tool_wear):
-    """
-    Predicts machine failure, generates SHAP analysis, and returns prediction,
-    probabilities, and SHAP waterfall plot for the given input.
-
-    Args:
-        Type (str): Machine type (L, M, or H).
-        air_temp (float): Air temperature in Kelvin.
-        process_temp (float): Process temperature in Kelvin.
-        rotational_speed (int): Rotational speed in rpm.
-        torque (float): Torque in Nm.
-        tool_wear (int): Tool wear in minutes.
-
-    Returns:
-        tuple: A tuple containing:
-            - str: Formatted prediction string.
-            - str: Formatted probabilities string.
-            - matplotlib.figure.Figure: SHAP waterfall plot figure object.
-    """
     if loaded_model is None:
-        return "Error: Model not loaded.", "", None # Return error if model loading failed
+        return "Error: Model not loaded.", "", None
 
-    # Create a DataFrame from the input
     input_data = pd.DataFrame({
         'Type': [Type],
         'Air temperature [K]': [air_temp],
@@ -46,43 +26,27 @@ def predict_failure_with_shap(Type, air_temp, process_temp, rotational_speed, to
         'Tool wear [min]': [tool_wear]
     })
 
-    # Make prediction and get probabilities using the loaded pipeline
     predicted_failure = loaded_model.predict(input_data)[0]
     predicted_proba = loaded_model.predict_proba(input_data)[0]
 
-    # Format the prediction and probabilities for Gradio output
     prediction_label = f"Predicted Failure: {'Failure' if predicted_failure == 1 else 'No Failure'}"
     probabilities_label = f"Probabilities (No Failure, Failure): {predicted_proba[0]:.4f}, {predicted_proba[1]:.4f}"
 
-
-    # Get preprocessor and classifier from the pipeline
     preprocessor = loaded_model.named_steps['preprocessor']
     classifier = loaded_model.named_steps['classifier']
-
-    # Transform the input data
     X_transformed = preprocessor.transform(input_data)
 
-    # Initialize SHAP explainer and calculate SHAP values
     explainer = shap.TreeExplainer(classifier)
-    # Ensure SHAP values are calculated for the transformed input data
     shap_values = explainer.shap_values(X_transformed)
-
-    # Get feature names after preprocessing
     feature_names = preprocessor.get_feature_names_out()
 
-    # Handle multi-output SHAP values (for binary classification, usually list of arrays)
     if isinstance(shap_values, list):
-        # For binary classification, shap_values[0] is for class 0, shap_values[1] for class 1
-        shap_val = shap_values[1][0] # Get values for the positive class (Failure)
-        base_val = explainer.expected_value[1] # Get expected value for the positive class
+        shap_val = shap_values[1][0]
+        base_val = explainer.expected_value[1]
     else:
-         # For single-output models, or if shap_values is a single array
-         # Assuming the positive class is at index 1 for probability output
-         shap_val = shap_values[0, :] if shap_values.ndim == 2 else shap_values[0, :, 1] # Get values for the positive class
-         base_val = explainer.expected_value if not isinstance(explainer.expected_value, (list, tuple, np.ndarray)) else explainer.expected_value[1] # Get expected value for the positive class
+        shap_val = shap_values[0, :] if shap_values.ndim == 2 else shap_values[0, :, 1]
+        base_val = explainer.expected_value if not isinstance(explainer.expected_value, (list, tuple, np.ndarray)) else explainer.expected_value[1]
 
-    # Generate SHAP waterfall plot
-    # Use a different figure explicitly to avoid interference with other plots
     fig = plt.figure()
     shap.waterfall_plot(
         shap.Explanation(
@@ -93,37 +57,31 @@ def predict_failure_with_shap(Type, air_temp, process_temp, rotational_speed, to
         ), show=False
     )
     plt.title("SHAP Waterfall Plot for Failure Prediction")
-    plt.tight_layout() # Adjust layout to prevent labels overlapping
+    plt.tight_layout()
+
+    return prediction_label, probabilities_label, fig
 
-    # Define the Gradio input components
-    inputs = [
+
+# Define Gradio interface OUTSIDE the function
+iface = gr.Interface(
+    fn=predict_failure_with_shap,
+    inputs=[
         gr.Dropdown(choices=['L', 'M', 'H'], label='Machine Type'),
         gr.Number(label='Air temperature [K]', step=0.1),
         gr.Number(label='Process temperature [K]', step=0.1),
         gr.Number(label='Rotational speed [rpm]', step=1),
         gr.Number(label='Torque [Nm]', step=0.1),
         gr.Number(label='Tool wear [min]', step=1)
-    ]
-
-    # Define the Gradio output components
-    outputs = [
+    ],
+    outputs=[
         gr.Label(label='Predicted Failure (0=No Failure, 1=Failure)'),
         gr.Label(label='Prediction Probabilities'),
         gr.Plot(label='SHAP Waterfall Plot')
-    ]
-
-    # Create the Gradio interface using the corrected parameter
-    iface = gr.Interface(
-        fn=predict_failure_with_shap,
-        inputs=inputs,
-        outputs=outputs,
-        title="Machine Failure Prediction with SHAP Analysis",
-        description="Enter the machine parameters to predict failure and see the SHAP analysis.",
-        flagging_mode='never' # Using the recommended parameter
-    )
-
-    # Return the outputs for Gradio
-    return prediction_label, probabilities_label, fig
+    ],
+    title="Machine Failure Prediction with SHAP Analysis",
+    description="Enter the machine parameters to predict failure and see the SHAP analysis.",
+    flagging_mode='never'
+)
 
 if __name__ == "__main__":
     iface.launch(server_name="0.0.0.0", server_port=7860)