import gradio as gr
import numpy as np
import joblib
from datetime import datetime, timedelta

# Load model and label encoders
model = joblib.load("best_model.pkl")
le_operation_type = joblib.load("le_operation_type.pkl")
le_operator_id = joblib.load("le_operator_id.pkl")

def predict_end_date(start_date_str, quantity, operation_type, operator_id, day_of_week):
    try:
        start_date = datetime.strptime(start_date_str, "%d-%m-%Y")
    except ValueError:
        return "Error: Start Date must be in DD-MM-YYYY format"

    # Encode categorical inputs
    try:
        operation_code = le_operation_type.transform([operation_type])[0]
    except ValueError:
        return f"Unknown Operation Type: {operation_type}"

    try:
        operator_code = le_operator_id.transform([operator_id])[0]
    except ValueError:
        return f"Unknown Operator ID: {operator_id}"

    # Feature engineering
    quantity_log = np.log1p(quantity)
    quantity_log_squared = quantity_log ** 2
    quantity_operation_interaction = quantity_log * operation_code
    quantity_operator_interaction = quantity_log * operator_code

    # Create feature vector for prediction
    X = np.array([[
        quantity_log,
        quantity_log_squared,
        quantity_operation_interaction,
        quantity_operator_interaction,
        operation_code,
        operator_code,
        day_of_week
    ]])

    pred_days = model.predict(X)[0]

    whole_days = int(pred_days)
    fractional_day = pred_days - whole_days

    total_seconds = fractional_day * 24 * 3600
    hours = int(total_seconds // 3600)
    minutes = int((total_seconds % 3600) // 60)

    est_end_datetime = start_date + timedelta(days=whole_days, hours=hours, minutes=minutes)
    est_end_date_str = est_end_datetime.strftime("%d-%m-%Y %H:%M")
    duration_str = f"{whole_days} days, {hours} hours, {minutes} minutes"

    return f"Estimated End Date & Time: {est_end_date_str}\nEstimated Duration: {duration_str}"

# Build Gradio interface
def gradio_interface(start_date, quantity, operation_type, operator_id, day_of_week):
    return predict_end_date(start_date, quantity, operation_type, operator_id, day_of_week)

operation_options = list(le_operation_type.classes_)
operator_options = list(le_operator_id.classes_)

iface = gr.Interface(
    fn=gradio_interface,
    inputs=[
        gr.Textbox(label="Start Date (DD-MM-YYYY)", value="21-07-2025"),
        gr.Number(label="Quantity", value=6000),
        gr.Dropdown(choices=operation_options, label="Operation Type", value=operation_options[0]),
        gr.Dropdown(choices=operator_options, label="Operator ID", value=operator_options[0]),
        gr.Slider(minimum=0, maximum=6, step=1, label="Day of Week (0=Monday, 6=Sunday)", value=1)
    ],
    outputs="text",
    title="Manufacturing Scheduler Prediction",
    description="Predict the estimated end date and duration of a manufacturing job based on input parameters."
)

if __name__ == "__main__":
    iface.launch()