app.py

# app.py

import os
import joblib
import gradio as gr
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import shap
from sklearn.pipeline import Pipeline # Keep for type hinting/structure

# =====================================================================================
# PART 1: MODEL LOADING
# This section now directly loads the model file you have uploaded to the repository.
# =====================================================================================

# <<< CHANGE 1: Update the filename to match your uploaded model.
MODEL_FILE = "machine_failure_prediction_model.joblib"

# <<< CHANGE 2: Remove the model creation logic. We will now directly load your file.
# The app assumes this file exists in your Hugging Face repository.
try:
    loaded_model = joblib.load(MODEL_FILE)
    print(f"Successfully loaded model from {MODEL_FILE}")
except FileNotFoundError:
    print(f"Error: Model file not found at {MODEL_FILE}. Make sure the file is uploaded to the repository.")
    # You might want to display an error in the Gradio app itself if the model fails to load.
    loaded_model = None # Set to None to handle the error case


# =====================================================================================
# PART 2: BACKEND LOGIC (Prediction and SHAP Calculation)
# =====================================================================================

def predict_failure(Type, air_temperature, process_temperature, rotational_speed, torque, tool_wear):
    """Predicts machine failure and calculates SHAP values using the loaded model."""
    if loaded_model is None:
        return "Error: Model not loaded.", None

    input_data = pd.DataFrame({
        'Type': [Type], 'Air temperature [K]': [air_temperature],
        'Process temperature [K]': [process_temperature], 'Rotational speed [rpm]': [rotational_speed],
        'Torque [Nm]': [torque], 'Tool wear [min]': [tool_wear]
    })

    preprocessor = loaded_model.named_steps['preprocessor']
    classifier = loaded_model.named_steps['classifier']
    input_processed = preprocessor.transform(input_data)
    probability = classifier.predict_proba(input_processed)[:, 1]

    explainer = shap.TreeExplainer(classifier)
    shap_values = explainer.shap_values(input_processed)
    feature_names = preprocessor.get_feature_names_out()

    # SHAP values for the "Failure" class (index 1)
    shap_val_failure = shap_values[1][0]
    base_val_failure = explainer.expected_value[1]

    return probability[0], shap_val_failure, feature_names, base_val_failure


# =====================================================================================
# PART 3: FRONTEND LOGIC (Plotting and Gradio Interface)
# =====================================================================================

def generate_shap_plot(shap_values, feature_names, base_value):
    """Generates a SHAP waterfall plot for the Gradio interface."""
    plt.close('all')
    explanation = shap.Explanation(
        values=shap_values, base_values=base_value, feature_names=feature_names
    )
    fig, _ = plt.subplots()
    shap.waterfall_plot(explanation, max_display=10, show=False)
    plt.tight_layout()
    return fig

def predict_and_generate_plot(Type, air_temperature, process_temperature, rotational_speed, torque, tool_wear):
    """Wrapper function that connects the backend prediction to the frontend plot."""
    result = predict_failure(
        Type, air_temperature, process_temperature, rotational_speed, torque, tool_wear
    )

    if isinstance(result, tuple):
        probability, shap_values, feature_names, base_value = result
        shap_plot = generate_shap_plot(shap_values, feature_names, base_value)
        return f"{probability:.2%}", shap_plot
    else:
        # Handle the case where the model failed to load
        error_message = result
        empty_plot = plt.figure()
        plt.text(0.5, 0.5, 'Error: Model could not be loaded.', horizontalalignment='center', verticalalignment='center')
        return error_message, empty_plot


# Define the Gradio interface layout and components
with gr.Blocks(theme=gr.themes.Soft()) as iface_with_shap:
    gr.Markdown("# Machine Failure Prediction with Live SHAP Analysis")
    gr.Markdown("Adjust the sliders to see the real-time probability of machine failure and how each feature's value contributes to the prediction.")
    with gr.Row():
        with gr.Column(scale=1):
            gr.Markdown("### Input Features")
            type_input = gr.Dropdown(label="Type", choices=['L', 'M', 'H'], value='L')
            air_temp_input = gr.Slider(minimum=295, maximum=305, value=300, label="Air temperature [K]")
            proc_temp_input = gr.Slider(minimum=305, maximum=315, value=310, label="Process temperature [K]")
            rpm_input = gr.Slider(minimum=1000, maximum=3000, value=1500, label="Rotational speed [rpm]")
            torque_input = gr.Slider(minimum=5, maximum=80, value=40, label="Torque [Nm]")
            wear_input = gr.Slider(minimum=0, maximum=250, value=100, label="Tool wear [min]")
            inputs = [type_input, air_temp_input, proc_temp_input, rpm_input, torque_input, wear_input]

        with gr.Column(scale=2):
            gr.Markdown("### Prediction Outputs")
            probability_output = gr.Textbox(label="Probability of Machine Failure")
            plot_output = gr.Plot(label="Feature Contribution to Failure (SHAP Waterfall Plot)")

    # This makes the app load the first prediction on startup
    iface_with_shap.load(
        fn=predict_and_generate_plot,
        inputs=inputs,
        outputs=[probability_output, plot_output]
    )
    
    # This connects the UI changes to the prediction function
    for input_comp in inputs:
        input_comp.change(
            fn=predict_and_generate_plot,
            inputs=inputs,
            outputs=[probability_output, plot_output]
        )

# Launch the application
if __name__ == "__main__":
    iface_with_shap.launch()