import gradio as gr
import joblib
import numpy as np
import os

# --- 1. Load the Pre-Trained Model ---
# This script assumes 'model.joblib' exists because you've run train.py
MODEL_PATH = "model.joblib"
model = None

try:
    model = joblib.load(MODEL_PATH)
    print("Model 'model.joblib' loaded successfully.")
except FileNotFoundError:
    print(f"ERROR: Model file not found at '{MODEL_PATH}'.")
    print("Please run 'python train.py' in your terminal first to create the model file.")


# --- 2. Define Feature Names ---
# This list must be in the exact same order as the data your model was trained on.
feature_names = [
    'time_in_cycles', 'setting_1', 'setting_2', 's_1', 's_2', 's_3', 's_4', 's_5', 
    's_6', 's_7', 's_8', 's_9', 's_10', 's_11', 's_12', 's_13', 's_14', 's_15', 
    's_16', 's_17', 's_18', 's_19', 's_20', 's_21'
]

# --- 3. Create the Prediction Function ---
def predict_rul(*args):
    """
    Takes all 24 slider/number inputs as arguments, arranges them into the
    correct format, and returns the model's RUL prediction.
    """
    if model is None:
        return "Model not loaded. Please run 'python train.py' and restart the app."

    input_data = np.array(args).reshape(1, -1)
    prediction = model.predict(input_data)
    final_prediction = prediction[0]
    
    return f"{round(final_prediction, 2)} cycles remaining"

# --- 4. Build the Gradio Interface ---
with gr.Blocks(theme=gr.themes.Soft()) as demo:
    gr.Markdown("# North Star 1.0")
    gr.Markdown(
        """
        Predictive maintenance machine learning system, forecasting faults and remaining useful life (RUL).

        Demo parameters trained on aircraft turbo fan engine dataset (NASA FD001 file). 

        Model automatically trains on dataset upon launch.

        """
    )
    # This component will display your four images in a neat grid.
    

    gr.Video(
        value="intro.mp4", #  
        label="Customizable for multiple machines",
        autoplay=True, # Optional: makes the video play automatically
        interactive=False # Optional: prevents users from uploading their own video
        )




    gr.Markdown("### Engine Parameters & Sensor Readings")
    
    # Create a list to hold all our input components in a single column
    inputs = []
    # Arrange the number inputs into a 3-column grid for a more compact layout
    num_columns = 3
    for i in range(0, len(feature_names), num_columns):
        with gr.Row():
            for j in range(num_columns):
                if i + j < len(feature_names):
                    name = feature_names[i + j]
                    # Create the component and add it to our list of inputs
                    component = gr.Number(label=name, value=0.0)
                    inputs.append(component)

    # Place the prediction button below the inputs
    predict_btn = gr.Button("Predict RUL", variant="primary")

    gr.Markdown("### Prediction Result")
    # Create the output textbox below the button
    outputs = gr.Textbox(label="Predicted Remaining Useful Life (RUL)")
    
    # Connect the button's "click" event to our prediction function
    predict_btn.click(
        fn=predict_rul,
        inputs=inputs,
        outputs=outputs
    )

# --- 5. Launch the App ---
if __name__ == "__main__":
    demo.launch()