Blame for app.py

import gradio as gr
import pandas as pd
import pickle

# Load the saved model and feature columns
with open('logistic_regression_model.pkl', 'rb') as model_file:
    model = pickle.load(model_file)

with open('feature_columns.pkl', 'rb') as col_file:
    feature_columns = pickle.load(col_file)

def predict_status(air_temp, proc_temp, speed, torque, tool_wear):
    # Prepare the input data
    sample_input = {
        "Air temperature [K]": air_temp,
        "Process temperature [K]": proc_temp,
        "Rotational speed [rpm]": speed,
        "Torque [Nm]": torque,
        "Tool wear [min]": tool_wear
    }
    
    # Convert the sample input to a DataFrame
    sample_df = pd.DataFrame([sample_input])
    
    # Ensure all columns from training data are present in the sample DataFrame
    for col in feature_columns:
        if col not in sample_df.columns:
            sample_df[col] = 0  # Add missing columns and fill with 0
    
    # Reorder columns to match the order in the training data
    sample_df = sample_df[feature_columns]

    # Make a prediction
    prediction = model.predict(sample_df)[0]

    # Initialize the reason list
    reasons = []

    # Define acceptable ranges for each feature with user-friendly feedback
    acceptable_ranges = {
        "Air temperature [K]": (150, 500),
        "Process temperature [K]": (150, 500),
        "Rotational speed [rpm]": (500, 1500),
        "Torque [Nm]": (100, 800),
        "Tool wear [min]": (50, 300)
    }

    # User-friendly reasons for each feature
    if air_temp < 150:
        reasons.append("• The air temperature is too low, which might be causing the machine to underperform.")
    elif air_temp > 500:
        reasons.append("• The air temperature is too high, which could be causing the machine to overheat.")

    if proc_temp < 150:
        reasons.append("• The process temperature is below the normal range, possibly affecting machine efficiency.")
    elif proc_temp > 500:
        reasons.append("• The process temperature is too high, which might lead to overheating.")

    if speed < 500:
        reasons.append("• The rotational speed is too low, causing the machine to operate inefficiently.")
    elif speed > 1500:
        reasons.append("• The rotational speed is too high, which might be causing excessive strain on the machine.")

    if torque < 100:
        reasons.append("• The torque is too low, which may indicate that the machine is not operating at its full capacity.")
    elif torque > 800:
        reasons.append("• The torque is too high, possibly putting excessive stress on the machine's components.")

    if tool_wear < 50:
        reasons.append("• The tool wear is minimal, but routine checks are still recommended to prevent future issues.")
    elif tool_wear > 300:
        reasons.append("• The tool wear is high, and the tool may need to be replaced soon to maintain optimal performance.")

    # Generate the final result message
    if prediction == 0:
        result_message = "🔴 Machine is NOT working.\n"  # First line for machine status
        if reasons:
            result_message += "\n".join(reasons)  # Second row onward for reasons with bullet points
        else:
            result_message += "No specific reason identified."  # In case no issues found
    else:
        result_message = "🟢 Machine is working fine."

    return result_message

# Define custom CSS for professional UI/UX
custom_css = """
    body {
        background-color: #f0f2f5;
        font-family: Arial, sans-serif;
    }
    .gradio-container {
        max-width: 800px;
        margin: auto;
        padding: 40px;
        border-radius: 12px;
        box-shadow: 0 4px 12px rgba(0, 0, 0, 0.1);
        background-color: #fff;
        border: 1px solid #ddd;
    }
    .gradio-title {
        font-size: 28px;
        font-weight: bold;
        color: #333;
        text-align: center;
        margin-bottom: 20px;
    }
    .gradio-description {
        font-size: 16px;
        color: #555;
        text-align: center;
        margin-bottom: 40px;
    }
    .gradio-footer {
        font-size: 14px;
        color: #777;
        text-align: center;
        margin-top: 20px;
    }
    .gradio-input label {
        font-weight: bold;
        color: #444;
    }
    footer {
        font-size: 12px;
        color: #555;
        text-align: center;
        padding: 10px;
        border-top: 1px solid #e0e0e0;
        margin-top: 40px;
    }
    footer a {
        color: #555;
        text-decoration: none;
    }
    footer a:hover {
        text-decoration: underline;
    }
"""

# Define the Gradio interface
iface = gr.Interface(
    fn=predict_status,
    inputs=[
        gr.Slider(label="Air Temperature (K)", minimum=0, maximum=1000, step=0.01, value=300),
        gr.Slider(label="Process Temperature (K)", minimum=0, maximum=1000, step=0.01, value=300),
        gr.Slider(label="Rotational Speed (rpm)", minimum=0, maximum=2000, step=1, value=1000),
        gr.Slider(label="Torque (Nm)", minimum=0, maximum=1000, step=0.1, value=500),
        gr.Slider(label="Tool Wear (min)", minimum=0, maximum=1000, step=1, value=100),
    ],
    outputs="text",
    title="Smart Machine Health Predictor",
    description="Provide machine parameters to predict whether the machine is working or not.",
    css=custom_css,  # Apply custom CSS
)

# Launch the Gradio app
if __name__ == "__main__":
    iface.launch()