from dash.dependencies import Input, Output, State
from dash import html

def register_nlp_callbacks(app):

    @app.callback(
        Output("nlp-training-status", "children"),
        Output("nlp-training-status", "is_open"),
        Input("start-training-nlp", "n_clicks"),
        State("start-training-nlp", "disabled"),
    )
    def start_nlp_training(n_clicks, disabled):
        if n_clicks:
            try:
                # Add your NLP training logic here
                return "NLP training started!", True
            except Exception as e:
                return f"Error during NLP training: {e}", True
        return "", False

    @app.callback(
        Output("nlp-data-upload-status", "children"),
        Input("upload-nlp-data", "contents"),
        State("upload-nlp-data", "filename"),
        State("upload-nlp-data", "last_modified"),
    )
    def upload_nlp_data(contents, filename, last_modified):
        if contents:
            try:
                # Add your logic to handle the uploaded data
                return f"Uploaded {filename} successfully!", True
            except Exception as e:
                return f"Error during upload: {e}", True
        return "", False

    @app.callback(
        Output("nlp-data-display", "children"),
        Input("refresh-nlp-data", "n_clicks"),
    )
    def display_nlp_data(n_clicks):
        if n_clicks:
            try:
                # Add your logic to fetch and display the NLP data
                nlp_data = "Example NLP Data"  # Replace with actual data fetching logic
                return html.Div([
                    html.H5("NLP Data:"),
                    html.Pre(nlp_data)
                ])
            except Exception as e:
                return f"Error during data display: {e}"
        return html.Div("No NLP data to display")

    @app.callback(
        Output("nlp-model-status", "children"),
        Input("refresh-nlp-model-status", "n_clicks"),
    )
    def display_nlp_model_status(n_clicks):
        if n_clicks:
            try:
                # Add your logic to fetch and display the NLP model status
                model_status = "NLP Model is up-to-date"  # Replace with actual status checking logic
                return html.Div([
                    html.H5("NLP Model Status:"),
                    html.P(model_status)
                ])
            except Exception as e:
                return f"Error during model status display: {e}"
        return html.Div("No NLP model status to display")