"""
Main Dash application for Chronos 2 Time Series Forecasting
"""

import base64
import io
import logging
from pathlib import Path
from dash import Dash, html, dcc, Input, Output, State, callback_context
import dash_bootstrap_components as dbc
import pandas as pd

# Import components
from components.upload import (
    create_upload_component,
    create_column_selector,
    create_sample_data_loader,
    format_upload_status,
    create_data_preview_table,
    create_quality_report
)
from components.chart import (
    create_forecast_chart,
    create_empty_chart,
    create_metrics_display,
    create_backtest_metrics_display,
    decimate_data
)
from components.controls import (
    create_forecast_controls,
    create_model_status_bar,
    create_results_section,
    create_app_header,
    create_footer
)

# Import services
from services.model_service import model_service
from services.data_processor import data_processor
from services.cache_manager import cache_manager

# Import utilities
from utils.validators import (
    validate_file_upload,
    validate_column_selection,
    validate_forecast_parameters
)
from utils.metrics import calculate_metrics

# Import configuration
from config.settings import CONFIG, APP_METADATA, LOG_LEVEL, LOG_FORMAT, LOG_FILE, setup_directories
from config.constants import MAX_CHART_POINTS

# Setup logging with both file and console handlers
def setup_logging():
    """Configure logging to write to both file and console"""
    # Create logs directory first
    Path(LOG_FILE).parent.mkdir(parents=True, exist_ok=True)

    # Get root logger
    root_logger = logging.getLogger()
    root_logger.setLevel(LOG_LEVEL)

    # Remove any existing handlers
    root_logger.handlers = []

    # Create formatters
    formatter = logging.Formatter(LOG_FORMAT)

    # File handler - writes all logs to file
    file_handler = logging.FileHandler(LOG_FILE, mode='a', encoding='utf-8')
    file_handler.setLevel(LOG_LEVEL)
    file_handler.setFormatter(formatter)
    root_logger.addHandler(file_handler)

    # Console handler - writes to stderr
    console_handler = logging.StreamHandler()
    console_handler.setLevel(LOG_LEVEL)
    console_handler.setFormatter(formatter)
    root_logger.addHandler(console_handler)

    logger = logging.getLogger(__name__)
    logger.info(f"Logging configured - writing to {LOG_FILE}")
    return logger

logger = setup_logging()

# Initialize Dash app
app = Dash(
    __name__,
    external_stylesheets=[
        dbc.themes.BOOTSTRAP,
        'https://use.fontawesome.com/releases/v5.15.4/css/all.css'
    ],
    suppress_callback_exceptions=True,
    title=APP_METADATA['title']
)

# App layout
app.layout = dbc.Container([
    # Header
    create_app_header(),

    # Model status
    html.Div(id='model-status-bar'),

    # Stores for data
    dcc.Store(id='uploaded-data-store'),
    dcc.Store(id='processed-data-store'),
    dcc.Store(id='forecast-results-store'),

    # Sample data loader
    create_sample_data_loader(),

    # Upload section
    create_upload_component(),

    # Column selector (hidden initially)
    create_column_selector(),

    # Forecast controls
    create_forecast_controls(),

    # Results section (hidden initially)
    create_results_section(),

    # Footer
    create_footer()

], fluid=True, className="py-4")


# Callback: Load model on startup
@app.callback(
    Output('model-status-bar', 'children'),
    Input('model-status-bar', 'id')
)
def load_model_on_startup(_):
    """Load the model when the app starts"""
    logger.info("=" * 80)
    logger.info("CALLBACK: load_model_on_startup - ENTRY")
    logger.info("=" * 80)

    try:
        logger.info("Attempting to load Chronos-2 model...")
        result = model_service.load_model()

        logger.info(f"Model loading result: {result}")

        if result['status'] == 'success':
            logger.info("✓ Model loaded successfully - returning 'ready' status bar")
            status_bar = create_model_status_bar('ready')
            logger.info(f"Status bar created: {type(status_bar)}")
            return status_bar
        else:
            logger.error(f"✗ Model loading failed: {result.get('error')}")
            return create_model_status_bar('error')
    except Exception as e:
        logger.error(f"✗ EXCEPTION in load_model_on_startup: {str(e)}", exc_info=True)
        return create_model_status_bar('error')
    finally:
        logger.info("CALLBACK: load_model_on_startup - EXIT")
        logger.info("=" * 80)


# Callback: Handle file upload
@app.callback(
    [Output('uploaded-data-store', 'data'),
     Output('upload-status', 'children'),
     Output('column-selector-card', 'style'),
     Output('date-column-dropdown', 'options'),
     Output('target-column-dropdown', 'options'),
     Output('id-column-dropdown', 'options'),
     Output('covariate-columns-dropdown', 'options')],
    Input('upload-data', 'contents'),
    State('upload-data', 'filename')
)
def handle_file_upload(contents, filename):
    """Handle file upload and extract column information"""
    logger.info("=" * 80)
    logger.info("CALLBACK: handle_file_upload - ENTRY")
    logger.info(f"Filename: {filename}")
    logger.info(f"Contents received: {contents is not None}")
    logger.info("=" * 80)

    if contents is None:
        logger.warning("No contents provided - returning empty response")
        return None, '', {'display': 'none'}, [], [], [], []

    try:
        # Parse uploaded file
        content_type, content_string = contents.split(',')
        decoded = base64.b64decode(content_string)

        # Server-side validation
        validation = validate_file_upload(filename, len(decoded))
        if not validation['valid']:
            error_msg = ' '.join(validation['issues'])
            logger.warning(f"File upload validation failed: {error_msg}")
            return None, format_upload_status('error', error_msg, True), {'display': 'none'}, [], [], [], []

        # Additional security: Sanitize filename
        import re
        safe_filename = re.sub(r'[^\w\-\.]', '_', filename)
        if safe_filename != filename:
            logger.info(f"Sanitized filename from '{filename}' to '{safe_filename}'")

        # Load file
        logger.info(f"Loading file with data_processor: {len(decoded)} bytes")
        result = data_processor.load_file(decoded, filename)
        logger.info(f"Load result status: {result['status']}")

        if result['status'] == 'error':
            logger.error(f"✗ File loading error: {result['error']}")
            return None, format_upload_status('error', result['error'], True), {'display': 'none'}, [], [], [], []

        # Get column information
        logger.info("Getting column information from data_processor")
        col_info = data_processor.get_column_info()
        logger.info(f"Column info: date_cols={col_info['date_columns']}, numeric_cols={col_info['numeric_columns'][:5]}...")

        # Create dropdown options
        date_options = [{'label': col, 'value': col} for col in col_info['date_columns']]
        target_options = [{'label': col, 'value': col} for col in col_info['numeric_columns']]
        id_options = [{'label': col, 'value': col} for col in col_info['all_columns']]
        # Covariates can be any numeric column
        covariate_options = [{'label': col, 'value': col} for col in col_info['numeric_columns']]

        logger.info(f"Created dropdown options: {len(date_options)} date, {len(target_options)} target, {len(id_options)} id, {len(covariate_options)} covariate")

        success_msg = f"Successfully loaded {filename} ({len(result['data'])} rows, {len(result['data'].columns)} columns)"
        logger.info(f"✓ {success_msg}")

        logger.info("CALLBACK: handle_file_upload - EXIT (success)")
        logger.info("=" * 80)

        return (
            result['metadata'],
            format_upload_status('success', success_msg),
            {'display': 'block'},
            date_options,
            target_options,
            id_options,
            covariate_options
        )

    except Exception as e:
        logger.error(f"✗ EXCEPTION in handle_file_upload: {str(e)}", exc_info=True)
        logger.info("CALLBACK: handle_file_upload - EXIT (exception)")
        logger.info("=" * 80)
        return None, format_upload_status('error', f"Error: {str(e)}", True), {'display': 'none'}, [], [], [], []


# Callback: Load sample data
@app.callback(
    [Output('uploaded-data-store', 'data', allow_duplicate=True),
     Output('upload-status', 'children', allow_duplicate=True),
     Output('column-selector-card', 'style', allow_duplicate=True),
     Output('date-column-dropdown', 'options', allow_duplicate=True),
     Output('target-column-dropdown', 'options', allow_duplicate=True),
     Output('id-column-dropdown', 'options', allow_duplicate=True),
     Output('covariate-columns-dropdown', 'options', allow_duplicate=True)],
    [Input('load-weather', 'n_clicks'),
     Input('load-airquality', 'n_clicks'),
     Input('load-bitcoin', 'n_clicks'),
     Input('load-stock', 'n_clicks'),
     Input('load-traffic', 'n_clicks'),
     Input('load-electricity', 'n_clicks')],
    prevent_initial_call=True
)
def load_sample_data(weather_clicks, airquality_clicks, bitcoin_clicks, stock_clicks, traffic_clicks, electricity_clicks):
    """Load sample datasets"""
    ctx = callback_context
    if not ctx.triggered:
        return None, '', {'display': 'none'}, [], [], [], []

    button_id = ctx.triggered[0]['prop_id'].split('.')[0]

    # Map button to filename
    sample_files = {
        'load-weather': 'weather_stations.csv',
        'load-airquality': 'air_quality_uci.csv',
        'load-bitcoin': 'bitcoin_price.csv',
        'load-stock': 'stock_sp500.csv',
        'load-traffic': 'traffic_speeds.csv',
        'load-electricity': 'electricity_consumption.csv'
    }

    filename = sample_files.get(button_id)
    if not filename:
        return None, '', {'display': 'none'}, [], [], [], []

    try:
        # Load sample file
        filepath = f"{CONFIG['datasets_folder']}/{filename}"
        with open(filepath, 'rb') as f:
            contents = f.read()

        result = data_processor.load_file(contents, filename)

        if result['status'] == 'error':
            return None, format_upload_status('error', result['error'], True), {'display': 'none'}, [], [], [], []

        # Get column information
        col_info = data_processor.get_column_info()

        date_options = [{'label': col, 'value': col} for col in col_info['date_columns']]
        target_options = [{'label': col, 'value': col} for col in col_info['numeric_columns']]
        id_options = [{'label': col, 'value': col} for col in col_info['all_columns']]
        covariate_options = [{'label': col, 'value': col} for col in col_info['numeric_columns']]

        success_msg = f"Loaded sample dataset: {filename}"

        return (
            result['metadata'],
            format_upload_status('success', success_msg),
            {'display': 'block'},
            date_options,
            target_options,
            id_options,
            covariate_options
        )

    except Exception as e:
        logger.error(f"Error loading sample data: {str(e)}", exc_info=True)
        error_msg = f"Sample data not found. Please ensure datasets folder exists: {CONFIG['datasets_folder']}"
        return None, format_upload_status('warning', error_msg), {'display': 'none'}, [], [], [], []


# Callback: Handle forecasting mode changes
@app.callback(
    [Output('covariate-section', 'style'),
     Output('target-help-text', 'children')],
    Input('forecasting-mode', 'value')
)
def update_forecasting_mode(mode):
    """Update UI based on selected forecasting mode"""
    if mode == 'univariate':
        return (
            {'display': 'none'},
            'Select ONE target variable (multi-select available, but use only one for univariate)'
        )
    elif mode == 'multivariate':
        return (
            {'display': 'none'},
            'Select MULTIPLE target variables to forecast together'
        )
    else:  # covariate-informed
        return (
            {'display': 'block'},
            'Select target variable(s) to forecast (can select multiple)'
        )


# Callback: Handle backtest enable/disable
@app.callback(
    Output('backtest-controls', 'style'),
    Input('backtest-enable', 'value')
)
def toggle_backtest_controls(backtest_enabled):
    """Show/hide backtest controls based on checkbox"""
    if 'enabled' in backtest_enabled:
        return {'display': 'block'}
    return {'display': 'none'}


# Callback: Update data preview and quality report
@app.callback(
    [Output('data-preview-container', 'children'),
     Output('data-quality-report', 'children'),
     Output('processed-data-store', 'data'),
     Output('generate-forecast-btn', 'disabled')],
    [Input('date-column-dropdown', 'value'),
     Input('target-column-dropdown', 'value'),
     Input('forecasting-mode', 'value'),
     Input('covariate-columns-dropdown', 'value')],
    State('id-column-dropdown', 'value')
)
def update_preview_and_process(date_col, target_col, mode, covariate_cols, id_col):
    """Update data preview and process data when columns are selected"""
    logger.info("=" * 80)
    logger.info("CALLBACK: update_preview_and_process - ENTRY")
    logger.info(f"date_col: {date_col}")
    logger.info(f"target_col: {target_col}")
    logger.info(f"mode: {mode}")
    logger.info(f"covariate_cols: {covariate_cols}")
    logger.info(f"id_col: {id_col}")
    logger.info("=" * 80)

    if not date_col or not target_col:
        logger.warning(f"Missing required columns - date_col: {date_col}, target_col: {target_col}")
        return '', '', None, True

    try:
        # Ensure target_col is a list for consistency
        if not isinstance(target_col, list):
            target_col = [target_col] if target_col else []

        # Ensure covariate_cols is a list
        if covariate_cols and not isinstance(covariate_cols, list):
            covariate_cols = [covariate_cols]

        # Validate column selection
        # For multivariate, validate each target column
        for t_col in target_col:
            validation = validate_column_selection(data_processor.data, date_col, t_col)
            if not validation['valid']:
                error_msg = ' '.join(validation['issues'])
                return format_upload_status('error', error_msg, True), '', None, True

        # Show preview
        preview = create_data_preview_table(data_processor.data)

        # Process data - pass target columns based on mode
        # For univariate: single target, for multivariate: list of targets
        if mode == 'univariate':
            target_to_process = target_col[0]  # Single target string
        else:
            target_to_process = target_col  # List of targets for multivariate

        result = data_processor.preprocess(
            date_column=date_col,
            target_column=target_to_process,
            id_column=id_col,
            forecast_horizon=30
        )

        if result['status'] == 'error':
            return preview, format_upload_status('error', result['error'], True), None, True

        # Show quality report
        quality_report = create_quality_report(result['quality_report'])

        # Store processed data with forecasting mode and columns
        processed_data = {
            'data': result['data'].to_json(date_format='iso'),
            'quality_report': result['quality_report'],
            'forecasting_mode': mode,
            'target_columns': target_col,
            'covariate_columns': covariate_cols if covariate_cols else [],
            'date_column': date_col,
            'id_column': id_col
        }

        return preview, quality_report, processed_data, False

    except Exception as e:
        logger.error(f"Error in preview/process: {str(e)}", exc_info=True)
        return '', format_upload_status('error', f"Error: {str(e)}", True), None, True


# Callback: Generate forecast
@app.callback(
    [Output('forecast-chart', 'figure'),
     Output('metrics-display', 'children'),
     Output('results-card', 'style'),
     Output('loading-output', 'children')],
    Input('generate-forecast-btn', 'n_clicks'),
    [State('processed-data-store', 'data'),
     State('horizon-slider', 'value'),
     State('confidence-checklist', 'value'),
     State('backtest-enable', 'value'),
     State('backtest-size-slider', 'value')],
    prevent_initial_call=True
)
def generate_forecast(n_clicks, processed_data, horizon, confidence_levels, backtest_enabled, backtest_size):
    """Generate forecast using the Chronos model, optionally with backtesting"""
    logger.info("=" * 80)
    logger.info("CALLBACK: generate_forecast - ENTRY")
    logger.info(f"n_clicks: {n_clicks}")
    logger.info(f"horizon: {horizon}")
    logger.info(f"confidence_levels: {confidence_levels}")
    logger.info(f"processed_data is None: {processed_data is None}")
    logger.info("=" * 80)

    if not processed_data or not n_clicks:
        logger.warning(f"Early return - processed_data exists: {processed_data is not None}, n_clicks: {n_clicks}")
        return create_empty_chart(), '', {'display': 'none'}, ''

    try:
        # Load processed data
        logger.info("Loading processed data from JSON...")
        df = pd.read_json(processed_data['data'])
        logger.info(f"Loaded DataFrame: shape={df.shape}, columns={df.columns.tolist()}")

        # Get forecasting mode and metadata
        mode = processed_data.get('forecasting_mode', 'univariate')
        target_columns = processed_data.get('target_columns', [])
        covariate_columns = processed_data.get('covariate_columns', [])

        logger.info(f"Forecasting mode: {mode}")
        logger.info(f"Target columns: {target_columns}")
        logger.info(f"Covariate columns: {covariate_columns}")

        # Validate parameters
        logger.info("Validating forecast parameters...")
        validation = validate_forecast_parameters(horizon, confidence_levels, len(df))
        logger.info(f"Validation result: {validation}")

        if not validation['valid']:
            error_msg = ' '.join(validation['issues'])
            logger.error(f"✗ Validation failed: {error_msg}")
            return create_empty_chart(error_msg), '', {'display': 'none'}, ''

        # Perform backtesting if enabled
        backtest_df = None
        backtest_metrics = None

        if backtest_enabled and 'enabled' in backtest_enabled:
            logger.info(f"Backtesting enabled with test_size={backtest_size}")

            backtest_result = model_service.backtest(
                data=df,
                test_size=min(backtest_size, len(df) // 3),  # Ensure we have enough training data
                forecast_horizon=horizon,
                confidence_levels=confidence_levels
            )

            if backtest_result['status'] == 'success':
                backtest_df = backtest_result['backtest_data']
                backtest_metrics = backtest_result['metrics']
                logger.info(f"✓ Backtest completed: {backtest_metrics}")
            else:
                logger.warning(f"Backtest failed: {backtest_result.get('error', 'Unknown error')}")

        # Generate forecast
        logger.info(f"Calling model_service.predict() - horizon={horizon}, confidence={confidence_levels}, mode={mode}")
        logger.info(f"Model service state: is_loaded={model_service.is_loaded}, variant={model_service.model_variant}")

        forecast_result = model_service.predict(
            data=df,
            horizon=horizon,
            confidence_levels=confidence_levels
        )

        logger.info(f"Forecast result status: {forecast_result['status']}")

        if forecast_result['status'] == 'error':
            logger.error(f"✗ Forecast generation failed: {forecast_result['error']}")
            return create_empty_chart(f"Forecast failed: {forecast_result['error']}"), '', {'display': 'none'}, ''

        # Get forecast data
        forecast_df = forecast_result['forecast']
        logger.info(f"Forecast DataFrame shape: {forecast_df.shape}, columns: {forecast_df.columns.tolist()}")

        # Decimate data if too large
        logger.info("Decimating data for chart...")
        historical_decimated = decimate_data(df, MAX_CHART_POINTS // 2)
        forecast_decimated = decimate_data(forecast_df, MAX_CHART_POINTS // 2)
        logger.info(f"Decimated - historical: {len(historical_decimated)}, forecast: {len(forecast_decimated)}")

        # Prepare data for chart (rename Chronos 2 columns to chart format)
        logger.info("Renaming columns for chart...")
        historical_for_chart = historical_decimated.rename(columns={
            'timestamp': 'ds',
            'target': 'y'
        })
        logger.info(f"Historical chart data columns: {historical_for_chart.columns.tolist()}")

        # Create chart title and labels based on target columns
        logger.info("Creating forecast chart...")
        primary_target = target_columns[0] if target_columns else 'Target'

        if mode == 'multivariate' and len(target_columns) > 1:
            chart_title = f"Forecast: {primary_target} (with {', '.join(target_columns[1:])} as covariates)"
            y_label = primary_target
        elif covariate_columns:
            chart_title = f"Forecast: {primary_target} (with covariates)"
            y_label = primary_target
        else:
            chart_title = f"Forecast: {primary_target}"
            y_label = primary_target

        fig = create_forecast_chart(
            historical_data=historical_for_chart,
            forecast_data=forecast_decimated,
            confidence_levels=confidence_levels,
            title=chart_title,
            y_axis_label=y_label,
            backtest_data=backtest_df
        )
        logger.info(f"Chart created: {type(fig)}")

        # Create metrics display
        metrics = {
            'inference_time': forecast_result['inference_time'],
            'data_points': len(df),
            'horizon': horizon
        }
        logger.info(f"Creating metrics display: {metrics}")

        # Add backtest metrics if available
        if backtest_metrics:
            metrics_components = dbc.Row([
                dbc.Col(create_metrics_display(metrics, forecast_result['inference_time']), md=6),
                dbc.Col(create_backtest_metrics_display(backtest_metrics), md=6)
            ])
        else:
            metrics_components = dbc.Row(create_metrics_display(
                metrics,
                forecast_result['inference_time']
            ))

        logger.info("✓ Forecast generation successful - returning chart and metrics")
        logger.info("CALLBACK: generate_forecast - EXIT (success)")
        logger.info("=" * 80)

        return fig, metrics_components, {'display': 'block'}, ''

    except Exception as e:
        logger.error(f"✗ EXCEPTION in generate_forecast: {str(e)}", exc_info=True)
        logger.info("CALLBACK: generate_forecast - EXIT (exception)")
        logger.info("=" * 80)
        return create_empty_chart(f"Error: {str(e)}"), '', {'display': 'none'}, ''


# Health check endpoint
@app.server.route('/health')
def health_check():
    """Health check endpoint for deployment monitoring"""
    status = {
        'status': 'healthy' if model_service.is_loaded else 'degraded',
        'model_loaded': model_service.is_loaded,
        'model_variant': model_service.model_variant,
        'device': model_service.device
    }
    return status


# Run the app
if __name__ == '__main__':
    # Setup directories
    setup_directories()

    logger.info(f"Starting Chronos 2 Forecasting App")
    logger.info(f"Configuration: {CONFIG}")

    # Get host and port from environment variables (for HuggingFace Spaces, Render, etc.)
    import os
    host = os.getenv('HOST', '127.0.0.1')
    port = int(os.getenv('PORT', '7860'))  # 7860 is HuggingFace Spaces default
    debug = os.getenv('DEBUG', 'True').lower() == 'true'

    # Run the app
    app.run_server(
        host=host,
        port=port,
        debug=debug
    )