import pandas as pd
import matplotlib.pyplot as plt
import gradio as gr
import tempfile
import os
from datetime import datetime

from statsforecast import StatsForecast
from statsforecast.models import (
    HistoricAverage,
    Naive,
    SeasonalNaive,
    WindowAverage,
    SeasonalWindowAverage,
    AutoETS,
    AutoARIMA
)

from utilsforecast.evaluation import evaluate
from utilsforecast.losses import *

# Function to load and process uploaded CSV
def load_data(file):
    if file is None:
        return None, "Please upload a CSV file"
    try:
        df = pd.read_csv(file)
        required_cols = ['unique_id', 'ds', 'y']
        missing_cols = [col for col in required_cols if col not in df.columns]
        if missing_cols:
            return None, f"Missing required columns: {', '.join(missing_cols)}"
        
        df['ds'] = pd.to_datetime(df['ds'])
        df = df.sort_values(['unique_id', 'ds']).reset_index(drop=True)
        
        # Check for NaN values
        if df['y'].isna().any():
            return None, "Data contains missing values in the 'y' column"
            
        return df, "Data loaded successfully!"
    except Exception as e:
        return None, f"Error loading data: {str(e)}"

# Function to generate and return a plot
def create_forecast_plot(forecast_df, original_df, title="Forecasting Results"):
    plt.figure(figsize=(12, 7))
    unique_ids = forecast_df['unique_id'].unique()
    forecast_cols = [col for col in forecast_df.columns if col not in ['unique_id', 'ds', 'cutoff']]
    
    colors = plt.cm.tab10.colors
    
    for i, unique_id in enumerate(unique_ids):
        original_data = original_df[original_df['unique_id'] == unique_id]
        plt.plot(original_data['ds'], original_data['y'], 'k-', linewidth=2, label=f'{unique_id} (Actual)')
        
        forecast_data = forecast_df[forecast_df['unique_id'] == unique_id]
        for j, col in enumerate(forecast_cols):
            if col in forecast_data.columns:
                plt.plot(forecast_data['ds'], forecast_data[col], 
                         color=colors[j % len(colors)], 
                         linestyle='--', 
                         linewidth=1.5,
                         label=f'{col}')

    plt.title(title, fontsize=16)
    plt.xlabel('Date', fontsize=12)
    plt.ylabel('Value', fontsize=12)
    plt.grid(True, alpha=0.3)
    plt.legend(loc='best')
    plt.tight_layout()
    
    # Format date labels better
    fig = plt.gcf()
    ax = plt.gca()
    fig.autofmt_xdate()
    
    return fig

# Function to create a plot for future forecasts
def create_future_forecast_plot(forecast_df, original_df):
    plt.figure(figsize=(12, 7))
    unique_ids = forecast_df['unique_id'].unique()
    forecast_cols = [col for col in forecast_df.columns if col not in ['unique_id', 'ds']]
    
    colors = plt.cm.tab10.colors
    
    for i, unique_id in enumerate(unique_ids):
        # Plot historical data
        original_data = original_df[original_df['unique_id'] == unique_id]
        plt.plot(original_data['ds'], original_data['y'], 'k-', linewidth=2, label=f'{unique_id} (Historical)')
        
        # Plot forecast data with shaded vertical line separator
        forecast_data = forecast_df[forecast_df['unique_id'] == unique_id]
        
        # Add vertical line at the forecast start
        if not forecast_data.empty and not original_data.empty:
            forecast_start = forecast_data['ds'].min()
            plt.axvline(x=forecast_start, color='gray', linestyle='--', alpha=0.5)
            
        for j, col in enumerate(forecast_cols):
            if col in forecast_data.columns:
                plt.plot(forecast_data['ds'], forecast_data[col], 
                         color=colors[j % len(colors)], 
                         linestyle='--', 
                         linewidth=1.5,
                         label=f'{col}')

    plt.title('Future Forecast', fontsize=16)
    plt.xlabel('Date', fontsize=12)
    plt.ylabel('Value', fontsize=12)
    plt.grid(True, alpha=0.3)
    plt.legend(loc='best')
    plt.tight_layout()
    
    # Format date labels better
    fig = plt.gcf()
    ax = plt.gca()
    fig.autofmt_xdate()
    
    return fig

# Function to export results to CSV
def export_results(eval_df, cv_results, future_forecasts):
    timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
    
    # Create temp directory if it doesn't exist
    temp_dir = tempfile.mkdtemp()
    
    result_files = []
    
    if eval_df is not None:
        eval_path = os.path.join(temp_dir, f"evaluation_metrics_{timestamp}.csv")
        eval_df.to_csv(eval_path, index=False)
        result_files.append(eval_path)
        
    if cv_results is not None:
        cv_path = os.path.join(temp_dir, f"cross_validation_results_{timestamp}.csv")
        cv_results.to_csv(cv_path, index=False)
        result_files.append(cv_path)
        
    if future_forecasts is not None:
        forecast_path = os.path.join(temp_dir, f"forecasts_{timestamp}.csv")
        future_forecasts.to_csv(forecast_path, index=False)
        result_files.append(forecast_path)
    
    return result_files

# Main forecasting logic
def run_forecast(
    file,
    frequency,
    eval_strategy,
    horizon,
    step_size,
    num_windows,
    use_historical_avg,
    use_naive,
    use_seasonal_naive,
    seasonality,
    use_window_avg,
    window_size,
    use_seasonal_window_avg,
    seasonal_window_size,
    use_autoets,
    use_autoarima,
    future_horizon
):
    df, message = load_data(file)
    if df is None:
        return None, None, None, None, None, None, message

    models = []
    model_aliases = []

    if use_historical_avg:
        models.append(HistoricAverage(alias='historical_average'))
        model_aliases.append('historical_average')
    if use_naive:
        models.append(Naive(alias='naive'))
        model_aliases.append('naive')
    if use_seasonal_naive:
        models.append(SeasonalNaive(season_length=seasonality, alias='seasonal_naive'))
        model_aliases.append('seasonal_naive')
    if use_window_avg:
        models.append(WindowAverage(window_size=window_size, alias='window_average'))
        model_aliases.append('window_average')
    if use_seasonal_window_avg:
        models.append(SeasonalWindowAverage(season_length=seasonality, window_size=seasonal_window_size, alias='seasonal_window_average'))
        model_aliases.append('seasonal_window_average')
    if use_autoets:
        models.append(AutoETS(alias='autoets', season_length=seasonality))
        model_aliases.append('autoets')
    if use_autoarima:
        models.append(AutoARIMA(alias='autoarima', season_length=seasonality))
        model_aliases.append('autoarima')

    if not models:
        return None, None, None, None, None, None, "Please select at least one forecasting model"

    sf = StatsForecast(models=models, freq=frequency, n_jobs=-1)

    try:
        # Run cross-validation
        if eval_strategy == "Cross Validation":
            cv_results = sf.cross_validation(df=df, h=horizon, step_size=step_size, n_windows=num_windows)
            evaluation = evaluate(df=cv_results, metrics=[bias, mae, rmse, mape], models=model_aliases)
            eval_df = pd.DataFrame(evaluation).reset_index()
            fig_validation = create_forecast_plot(cv_results, df, "Cross Validation Results")
        else:  # Fixed window
            cv_results = sf.cross_validation(df=df, h=horizon, step_size=10, n_windows=1)  # any step size for 1 window
            evaluation = evaluate(df=cv_results, metrics=[bias, mae, rmse, mape], models=model_aliases)
            eval_df = pd.DataFrame(evaluation).reset_index()
            fig_validation = create_forecast_plot(cv_results, df, "Fixed Window Validation Results")

        # Generate future forecasts
        future_forecasts = sf.forecast(df=df, h=future_horizon)
        fig_future = create_future_forecast_plot(future_forecasts, df)
        
        # Export results
        export_files = export_results(eval_df, cv_results, future_forecasts)
        
        return eval_df, cv_results, fig_validation, future_forecasts, fig_future, export_files, "Analysis completed successfully!"

    except Exception as e:
        return None, None, None, None, None, None, f"Error during forecasting: {str(e)}"

# Sample CSV file generation
def download_sample():
    sample_data = """unique_id,ds,y
^GSPC,2023-01-03,3824.139892578125
^GSPC,2023-01-04,3852.969970703125
^GSPC,2023-01-05,3808.10009765625
^GSPC,2023-01-06,3895.080078125
^GSPC,2023-01-09,3892.090087890625
^GSPC,2023-01-10,3919.25
^GSPC,2023-01-11,3969.610107421875
^GSPC,2023-01-12,3983.169921875
^GSPC,2023-01-13,3999.090087890625
^GSPC,2023-01-17,3990.969970703125
^GSPC,2023-01-18,3928.860107421875
^GSPC,2023-01-19,3898.85009765625
^GSPC,2023-01-20,3972.610107421875
^GSPC,2023-01-23,4019.81005859375
^GSPC,2023-01-24,4016.949951171875
^GSPC,2023-01-25,4016.219970703125
^GSPC,2023-01-26,4060.429931640625
^GSPC,2023-01-27,4070.56005859375
^GSPC,2023-01-30,4017.77001953125
^GSPC,2023-01-31,4076.60009765625
^GSPC,2023-02-01,4119.2099609375
^GSPC,2023-02-02,4179.759765625
^GSPC,2023-02-03,4136.47998046875
^GSPC,2023-02-06,4111.080078125
^GSPC,2023-02-07,4164
^GSPC,2023-02-08,4117.85986328125
^GSPC,2023-02-09,4081.5
^GSPC,2023-02-10,4090.4599609375
^GSPC,2023-02-13,4137.2900390625
^GSPC,2023-02-14,4136.1298828125
^GSPC,2023-02-15,4147.60009765625
^GSPC,2023-02-16,4090.409912109375
^GSPC,2023-02-17,4079.090087890625
^GSPC,2023-02-21,3997.340087890625
^GSPC,2023-02-22,3991.050048828125
^GSPC,2023-02-23,4012.320068359375
^GSPC,2023-02-24,3970.0400390625
^GSPC,2023-02-27,3982.239990234375
^GSPC,2023-02-28,3970.14990234375
^GSPC,2023-03-01,3951.389892578125
^GSPC,2023-03-02,3981.35009765625
^GSPC,2023-03-03,4045.639892578125
^GSPC,2023-03-06,4048.419921875
^GSPC,2023-03-07,3986.3701171875
^GSPC,2023-03-08,3992.010009765625
^GSPC,2023-03-09,3918.320068359375
^GSPC,2023-03-10,3861.590087890625
^GSPC,2023-03-13,3855.760009765625
^GSPC,2023-03-14,3919.2900390625
^GSPC,2023-03-15,3891.929931640625
^GSPC,2023-03-16,3960.280029296875
^GSPC,2023-03-17,3916.639892578125
^GSPC,2023-03-20,3951.570068359375
^GSPC,2023-03-21,4002.8701171875
^GSPC,2023-03-22,3936.969970703125
^GSPC,2023-03-23,3948.719970703125
^GSPC,2023-03-24,3970.989990234375
^GSPC,2023-03-27,3977.530029296875
^GSPC,2023-03-28,3971.27001953125
^GSPC,2023-03-29,4027.81005859375
^GSPC,2023-03-30,4050.830078125
^GSPC,2023-03-31,4109.31005859375
^GSPC,2023-04-03,4124.509765625
^GSPC,2023-04-04,4100.60009765625
^GSPC,2023-04-05,4090.3798828125
^GSPC,2023-04-06,4105.02001953125
^GSPC,2023-04-10,4109.10986328125
^GSPC,2023-04-11,4108.93994140625
^GSPC,2023-04-12,4091.949951171875
^GSPC,2023-04-13,4146.22021484375
^GSPC,2023-04-14,4137.64013671875
^GSPC,2023-04-17,4151.31982421875
^GSPC,2023-04-18,4154.8701171875
^GSPC,2023-04-19,4154.52001953125
^GSPC,2023-04-20,4129.7900390625
^GSPC,2023-04-21,4133.52001953125
^GSPC,2023-04-24,4137.0400390625
^GSPC,2023-04-25,4071.6298828125
^GSPC,2023-04-26,4055.989990234375
^GSPC,2023-04-27,4135.35009765625
^GSPC,2023-04-28,4169.47998046875
^GSPC,2023-05-01,4167.8701171875
^GSPC,2023-05-02,4119.580078125
^GSPC,2023-05-03,4090.75
^GSPC,2023-05-04,4061.219970703125
^GSPC,2023-05-05,4136.25
^GSPC,2023-05-08,4138.1201171875
^GSPC,2023-05-09,4119.169921875
^GSPC,2023-05-10,4137.64013671875
^GSPC,2023-05-11,4130.6201171875
^GSPC,2023-05-12,4124.080078125
^GSPC,2023-05-15,4136.27978515625
^GSPC,2023-05-16,4109.89990234375
^GSPC,2023-05-17,4158.77001953125
^GSPC,2023-05-18,4198.0498046875
^GSPC,2023-05-19,4191.97998046875
^GSPC,2023-05-22,4192.6298828125
^GSPC,2023-05-23,4145.580078125
^GSPC,2023-05-24,4115.240234375
^GSPC,2023-05-25,4151.27978515625
^GSPC,2023-05-26,4205.4501953125
^GSPC,2023-05-30,4205.52001953125
^GSPC,2023-05-31,4179.830078125
^GSPC,2023-06-01,4221.02001953125
^GSPC,2023-06-02,4282.3701171875
^GSPC,2023-06-05,4273.7900390625
^GSPC,2023-06-06,4283.85009765625
^GSPC,2023-06-07,4267.52001953125
^GSPC,2023-06-08,4293.93017578125
^GSPC,2023-06-09,4298.85986328125
^GSPC,2023-06-12,4338.93017578125
^GSPC,2023-06-13,4369.009765625
^GSPC,2023-06-14,4372.58984375
^GSPC,2023-06-15,4425.83984375
^GSPC,2023-06-16,4409.58984375
^GSPC,2023-06-20,4388.7099609375
^GSPC,2023-06-21,4365.68994140625
^GSPC,2023-06-22,4381.89013671875
^GSPC,2023-06-23,4348.330078125
^GSPC,2023-06-26,4328.81982421875
^GSPC,2023-06-27,4378.41015625
^GSPC,2023-06-28,4376.85986328125
^GSPC,2023-06-29,4396.43994140625
^GSPC,2023-06-30,4450.3798828125
^GSPC,2023-07-03,4455.58984375
^GSPC,2023-07-05,4446.81982421875
^GSPC,2023-07-06,4411.58984375
^GSPC,2023-07-07,4398.9501953125
^GSPC,2023-07-10,4409.52978515625
^GSPC,2023-07-11,4439.259765625
^GSPC,2023-07-12,4472.16015625
^GSPC,2023-07-13,4510.0400390625
^GSPC,2023-07-14,4505.419921875
^GSPC,2023-07-17,4522.7900390625
^GSPC,2023-07-18,4554.97998046875
^GSPC,2023-07-19,4565.72021484375
^GSPC,2023-07-20,4534.8701171875
^GSPC,2023-07-21,4536.33984375
^GSPC,2023-07-24,4554.64013671875
^GSPC,2023-07-25,4567.4599609375
^GSPC,2023-07-26,4566.75
^GSPC,2023-07-27,4537.41015625
^GSPC,2023-07-28,4582.22998046875
^GSPC,2023-07-31,4588.9599609375
^GSPC,2023-08-01,4576.72998046875
^GSPC,2023-08-02,4513.39013671875
^GSPC,2023-08-03,4501.89013671875
^GSPC,2023-08-04,4478.02978515625
^GSPC,2023-08-07,4518.43994140625
^GSPC,2023-08-08,4499.3798828125
^GSPC,2023-08-09,4467.7099609375
^GSPC,2023-08-10,4468.830078125
^GSPC,2023-08-11,4464.0498046875
^GSPC,2023-08-14,4489.72021484375
^GSPC,2023-08-15,4437.85986328125
^GSPC,2023-08-16,4404.330078125
^GSPC,2023-08-17,4370.35986328125
^GSPC,2023-08-18,4369.7099609375
^GSPC,2023-08-21,4399.77001953125
^GSPC,2023-08-22,4387.5498046875
^GSPC,2023-08-23,4436.009765625
^GSPC,2023-08-24,4376.31005859375
^GSPC,2023-08-25,4405.7099609375
^GSPC,2023-08-28,4433.31005859375
^GSPC,2023-08-29,4497.6298828125
^GSPC,2023-08-30,4514.8701171875
^GSPC,2023-08-31,4507.66015625
^GSPC,2023-09-01,4515.77001953125
^GSPC,2023-09-05,4496.830078125
^GSPC,2023-09-06,4465.47998046875
^GSPC,2023-09-07,4451.14013671875
^GSPC,2023-09-08,4457.490234375
^GSPC,2023-09-11,4487.4599609375
^GSPC,2023-09-12,4461.89990234375
^GSPC,2023-09-13,4467.43994140625
^GSPC,2023-09-14,4505.10009765625
^GSPC,2023-09-15,4450.31982421875
^GSPC,2023-09-18,4453.52978515625
^GSPC,2023-09-19,4443.9501953125
^GSPC,2023-09-20,4402.2001953125
^GSPC,2023-09-21,4330
^GSPC,2023-09-22,4320.06005859375
^GSPC,2023-09-25,4337.43994140625
^GSPC,2023-09-26,4273.52978515625
^GSPC,2023-09-27,4274.509765625
^GSPC,2023-09-28,4299.7001953125
^GSPC,2023-09-29,4288.0498046875
^GSPC,2023-10-02,4288.39013671875
^GSPC,2023-10-03,4229.4501953125
^GSPC,2023-10-04,4263.75
^GSPC,2023-10-05,4258.18994140625
^GSPC,2023-10-06,4308.5
^GSPC,2023-10-09,4335.66015625
^GSPC,2023-10-10,4358.240234375
^GSPC,2023-10-11,4376.9501953125
^GSPC,2023-10-12,4349.60986328125
^GSPC,2023-10-13,4327.77978515625
^GSPC,2023-10-16,4373.6298828125
^GSPC,2023-10-17,4373.2001953125
^GSPC,2023-10-18,4314.60009765625
^GSPC,2023-10-19,4278
^GSPC,2023-10-20,4224.16015625
^GSPC,2023-10-23,4217.0400390625
^GSPC,2023-10-24,4247.68017578125
^GSPC,2023-10-25,4186.77001953125
^GSPC,2023-10-26,4137.22998046875
^GSPC,2023-10-27,4117.3701171875
^GSPC,2023-10-30,4166.81982421875
^GSPC,2023-10-31,4193.7998046875
^GSPC,2023-11-01,4237.85986328125
^GSPC,2023-11-02,4317.77978515625
^GSPC,2023-11-03,4358.33984375
^GSPC,2023-11-06,4365.97998046875
^GSPC,2023-11-07,4378.3798828125
^GSPC,2023-11-08,4382.77978515625
^GSPC,2023-11-09,4347.35009765625
^GSPC,2023-11-10,4415.240234375
^GSPC,2023-11-13,4411.5498046875
^GSPC,2023-11-14,4495.7001953125
^GSPC,2023-11-15,4502.8798828125
^GSPC,2023-11-16,4508.240234375
^GSPC,2023-11-17,4514.02001953125
^GSPC,2023-11-20,4547.3798828125
^GSPC,2023-11-21,4538.18994140625
^GSPC,2023-11-22,4556.6201171875
^GSPC,2023-11-24,4559.33984375
^GSPC,2023-11-27,4550.43017578125
^GSPC,2023-11-28,4554.89013671875
^GSPC,2023-11-29,4550.580078125
^GSPC,2023-11-30,4567.7998046875
^GSPC,2023-12-01,4594.6298828125
^GSPC,2023-12-04,4569.77978515625
^GSPC,2023-12-05,4567.18017578125
^GSPC,2023-12-06,4549.33984375
^GSPC,2023-12-07,4585.58984375
^GSPC,2023-12-08,4604.3701171875
^GSPC,2023-12-11,4622.43994140625
^GSPC,2023-12-12,4643.7001953125
^GSPC,2023-12-13,4707.08984375
^GSPC,2023-12-14,4719.5498046875
^GSPC,2023-12-15,4719.18994140625
^GSPC,2023-12-18,4740.56005859375
^GSPC,2023-12-19,4768.3701171875
^GSPC,2023-12-20,4698.35009765625
^GSPC,2023-12-21,4746.75
^GSPC,2023-12-22,4754.6298828125
^GSPC,2023-12-26,4774.75
^GSPC,2023-12-27,4781.580078125
^GSPC,2023-12-28,4783.35009765625
^GSPC,2023-12-29,4769.830078125
^GSPC,2024-01-02,4742.830078125
^GSPC,2024-01-03,4704.81005859375
^GSPC,2024-01-04,4688.68017578125
^GSPC,2024-01-05,4697.240234375
^GSPC,2024-01-08,4763.5400390625
^GSPC,2024-01-09,4756.5
^GSPC,2024-01-10,4783.4501953125
^GSPC,2024-01-11,4780.240234375
^GSPC,2024-01-12,4783.830078125
^GSPC,2024-01-16,4765.97998046875
^GSPC,2024-01-17,4739.2099609375
^GSPC,2024-01-18,4780.93994140625
^GSPC,2024-01-19,4839.81005859375
^GSPC,2024-01-22,4850.43017578125
^GSPC,2024-01-23,4864.60009765625
^GSPC,2024-01-24,4868.5498046875
^GSPC,2024-01-25,4894.16015625
^GSPC,2024-01-26,4890.97021484375
^GSPC,2024-01-29,4927.93017578125
^GSPC,2024-01-30,4924.97021484375
^GSPC,2024-01-31,4845.64990234375
^GSPC,2024-02-01,4906.18994140625
^GSPC,2024-02-02,4958.60986328125
^GSPC,2024-02-05,4942.81005859375
^GSPC,2024-02-06,4954.22998046875
^GSPC,2024-02-07,4995.06005859375
^GSPC,2024-02-08,4997.91015625
^GSPC,2024-02-09,5026.60986328125
^GSPC,2024-02-12,5021.83984375
^GSPC,2024-02-13,4953.169921875
^GSPC,2024-02-14,5000.6201171875
^GSPC,2024-02-15,5029.72998046875
^GSPC,2024-02-16,5005.56982421875
^GSPC,2024-02-20,4975.509765625
^GSPC,2024-02-21,4981.7998046875
^GSPC,2024-02-22,5087.02978515625
^GSPC,2024-02-23,5088.7998046875
^GSPC,2024-02-26,5069.52978515625
^GSPC,2024-02-27,5078.18017578125
^GSPC,2024-02-28,5069.759765625
^GSPC,2024-02-29,5096.27001953125
^GSPC,2024-03-01,5137.080078125
^GSPC,2024-03-04,5130.9501953125
^GSPC,2024-03-05,5078.64990234375
^GSPC,2024-03-06,5104.759765625
^GSPC,2024-03-07,5157.35986328125
^GSPC,2024-03-08,5123.68994140625
^GSPC,2024-03-11,5117.93994140625
^GSPC,2024-03-12,5175.27001953125
^GSPC,2024-03-13,5165.31005859375
^GSPC,2024-03-14,5150.47998046875
^GSPC,2024-03-15,5117.08984375
^GSPC,2024-03-18,5149.419921875
^GSPC,2024-03-19,5178.509765625
^GSPC,2024-03-20,5224.6201171875
^GSPC,2024-03-21,5241.52978515625
^GSPC,2024-03-22,5234.18017578125
^GSPC,2024-03-25,5218.18994140625
^GSPC,2024-03-26,5203.580078125
^GSPC,2024-03-27,5248.490234375
^GSPC,2024-03-28,5254.35009765625
^GSPC,2024-04-01,5243.77001953125
^GSPC,2024-04-02,5205.81005859375
^GSPC,2024-04-03,5211.490234375
^GSPC,2024-04-04,5147.2099609375
^GSPC,2024-04-05,5204.33984375
^GSPC,2024-04-08,5202.39013671875
^GSPC,2024-04-09,5209.91015625
^GSPC,2024-04-10,5160.64013671875
^GSPC,2024-04-11,5199.06005859375
^GSPC,2024-04-12,5123.41015625
^GSPC,2024-04-15,5061.81982421875
^GSPC,2024-04-16,5051.41015625
^GSPC,2024-04-17,5022.2099609375
^GSPC,2024-04-18,5011.1201171875
^GSPC,2024-04-19,4967.22998046875
^GSPC,2024-04-22,5010.60009765625
^GSPC,2024-04-23,5070.5498046875
^GSPC,2024-04-24,5071.6298828125
^GSPC,2024-04-25,5048.419921875
^GSPC,2024-04-26,5099.9599609375
^GSPC,2024-04-29,5116.169921875
^GSPC,2024-04-30,5035.68994140625
^GSPC,2024-05-01,5018.39013671875
^GSPC,2024-05-02,5064.2001953125
^GSPC,2024-05-03,5127.7900390625
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^GSPC,2024-05-07,5187.7001953125
^GSPC,2024-05-08,5187.669921875
^GSPC,2024-05-09,5214.080078125
^GSPC,2024-05-10,5222.68017578125
^GSPC,2024-05-13,5221.419921875
^GSPC,2024-05-14,5246.68017578125
^GSPC,2024-05-15,5308.14990234375
^GSPC,2024-05-16,5297.10009765625
^GSPC,2024-05-17,5303.27001953125
^GSPC,2024-05-20,5308.1298828125
^GSPC,2024-05-21,5321.41015625
^GSPC,2024-05-22,5307.009765625
^GSPC,2024-05-23,5267.83984375
^GSPC,2024-05-24,5304.72021484375
^GSPC,2024-05-28,5306.0400390625
^GSPC,2024-05-29,5266.9501953125
^GSPC,2024-05-30,5235.47998046875
^GSPC,2024-05-31,5277.509765625
^GSPC,2024-06-03,5283.39990234375
^GSPC,2024-06-04,5291.33984375
^GSPC,2024-06-05,5354.02978515625
^GSPC,2024-06-06,5352.9599609375
^GSPC,2024-06-07,5346.990234375
^GSPC,2024-06-10,5360.7900390625
^GSPC,2024-06-11,5375.31982421875
^GSPC,2024-06-12,5421.02978515625
^GSPC,2024-06-13,5433.740234375
^GSPC,2024-06-14,5431.60009765625
^GSPC,2024-06-17,5473.22998046875
^GSPC,2024-06-18,5487.02978515625
^GSPC,2024-06-20,5473.169921875
^GSPC,2024-06-21,5464.6201171875
^GSPC,2024-06-24,5447.8701171875
^GSPC,2024-06-25,5469.2998046875
^GSPC,2024-06-26,5477.89990234375
^GSPC,2024-06-27,5482.8701171875
^GSPC,2024-06-28,5460.47998046875
^GSPC,2024-07-01,5475.08984375
^GSPC,2024-07-02,5509.009765625
^GSPC,2024-07-03,5537.02001953125
^GSPC,2024-07-05,5567.18994140625
^GSPC,2024-07-08,5572.85009765625
^GSPC,2024-07-09,5576.97998046875
^GSPC,2024-07-10,5633.91015625
^GSPC,2024-07-11,5584.5400390625
^GSPC,2024-07-12,5615.35009765625
^GSPC,2024-07-15,5631.22021484375
^GSPC,2024-07-16,5667.2001953125
^GSPC,2024-07-17,5588.27001953125
^GSPC,2024-07-18,5544.58984375
^GSPC,2024-07-19,5505
^GSPC,2024-07-22,5564.41015625
^GSPC,2024-07-23,5555.740234375
^GSPC,2024-07-24,5427.1298828125
^GSPC,2024-07-25,5399.22021484375
^GSPC,2024-07-26,5459.10009765625
^GSPC,2024-07-29,5463.5400390625
^GSPC,2024-07-30,5436.43994140625
^GSPC,2024-07-31,5522.2998046875
^GSPC,2024-08-01,5446.68017578125
^GSPC,2024-08-02,5346.56005859375
^GSPC,2024-08-05,5186.330078125
^GSPC,2024-08-06,5240.02978515625
^GSPC,2024-08-07,5199.5
^GSPC,2024-08-08,5319.31005859375
^GSPC,2024-08-09,5344.16015625
^GSPC,2024-08-12,5344.39013671875
^GSPC,2024-08-13,5434.43017578125
^GSPC,2024-08-14,5455.2099609375
^GSPC,2024-08-15,5543.22021484375
^GSPC,2024-08-16,5554.25
^GSPC,2024-08-19,5608.25
^GSPC,2024-08-20,5597.1201171875
^GSPC,2024-08-21,5620.85009765625
^GSPC,2024-08-22,5570.64013671875
^GSPC,2024-08-23,5634.60986328125
^GSPC,2024-08-26,5616.83984375
^GSPC,2024-08-27,5625.7998046875
^GSPC,2024-08-28,5592.18017578125
^GSPC,2024-08-29,5591.9599609375
^GSPC,2024-08-30,5648.39990234375
^GSPC,2024-09-03,5528.93017578125
^GSPC,2024-09-04,5520.06982421875
^GSPC,2024-09-05,5503.41015625
^GSPC,2024-09-06,5408.419921875
^GSPC,2024-09-09,5471.0498046875
^GSPC,2024-09-10,5495.52001953125
^GSPC,2024-09-11,5554.1298828125
^GSPC,2024-09-12,5595.759765625
^GSPC,2024-09-13,5626.02001953125
^GSPC,2024-09-16,5633.08984375
^GSPC,2024-09-17,5634.580078125
^GSPC,2024-09-18,5618.259765625
^GSPC,2024-09-19,5713.64013671875
^GSPC,2024-09-20,5702.5498046875
^GSPC,2024-09-23,5718.56982421875
^GSPC,2024-09-24,5732.93017578125
^GSPC,2024-09-25,5722.259765625
^GSPC,2024-09-26,5745.3701171875
^GSPC,2024-09-27,5738.169921875
^GSPC,2024-09-30,5762.47998046875
^GSPC,2024-10-01,5708.75
^GSPC,2024-10-02,5709.5400390625
^GSPC,2024-10-03,5699.93994140625
^GSPC,2024-10-04,5751.06982421875
^GSPC,2024-10-07,5695.93994140625
^GSPC,2024-10-08,5751.1298828125
^GSPC,2024-10-09,5792.0400390625
^GSPC,2024-10-10,5780.0498046875
^GSPC,2024-10-11,5815.02978515625
^GSPC,2024-10-14,5859.85009765625
^GSPC,2024-10-15,5815.259765625
^GSPC,2024-10-16,5842.47021484375
^GSPC,2024-10-17,5841.47021484375
^GSPC,2024-10-18,5864.669921875
^GSPC,2024-10-21,5853.97998046875
^GSPC,2024-10-22,5851.2001953125
^GSPC,2024-10-23,5797.419921875
^GSPC,2024-10-24,5809.85986328125
^GSPC,2024-10-25,5808.1201171875
^GSPC,2024-10-28,5823.52001953125
^GSPC,2024-10-29,5832.919921875
^GSPC,2024-10-30,5813.669921875
^GSPC,2024-10-31,5705.4501953125
^GSPC,2024-11-01,5728.7998046875
^GSPC,2024-11-04,5712.68994140625
^GSPC,2024-11-05,5782.759765625
^GSPC,2024-11-06,5929.0400390625
^GSPC,2024-11-07,5973.10009765625
^GSPC,2024-11-08,5995.5400390625
^GSPC,2024-11-11,6001.35009765625
^GSPC,2024-11-12,5983.990234375
^GSPC,2024-11-13,5985.3798828125
^GSPC,2024-11-14,5949.169921875
^GSPC,2024-11-15,5870.6201171875
^GSPC,2024-11-18,5893.6201171875
^GSPC,2024-11-19,5916.97998046875
^GSPC,2024-11-20,5917.10986328125
^GSPC,2024-11-21,5948.7099609375
^GSPC,2024-11-22,5969.33984375
^GSPC,2024-11-25,5987.3701171875
^GSPC,2024-11-26,6021.6298828125
^GSPC,2024-11-27,5998.740234375
^GSPC,2024-11-29,6032.3798828125
^GSPC,2024-12-02,6047.14990234375
^GSPC,2024-12-03,6049.8798828125
^GSPC,2024-12-04,6086.490234375
^GSPC,2024-12-05,6075.10986328125
^GSPC,2024-12-06,6090.27001953125
^GSPC,2024-12-09,6052.85009765625
^GSPC,2024-12-10,6034.91015625
^GSPC,2024-12-11,6084.18994140625
^GSPC,2024-12-12,6051.25
^GSPC,2024-12-13,6051.08984375
^GSPC,2024-12-16,6074.080078125
^GSPC,2024-12-17,6050.60986328125
^GSPC,2024-12-18,5872.16015625
^GSPC,2024-12-19,5867.080078125
^GSPC,2024-12-20,5930.85009765625
^GSPC,2024-12-23,5974.06982421875
^GSPC,2024-12-24,6040.0400390625
^GSPC,2024-12-26,6037.58984375
^GSPC,2024-12-27,5970.83984375
^GSPC,2024-12-30,5906.93994140625
^GSPC,2024-12-31,5881.6298828125
^GSPC,2025-01-02,5868.5498046875
^GSPC,2025-01-03,5942.47021484375
^GSPC,2025-01-06,5975.3798828125
^GSPC,2025-01-07,5909.02978515625
^GSPC,2025-01-08,5918.25
^GSPC,2025-01-10,5827.0400390625
^GSPC,2025-01-13,5836.22021484375
^GSPC,2025-01-14,5842.91015625
^GSPC,2025-01-15,5949.91015625
^GSPC,2025-01-16,5937.33984375
^GSPC,2025-01-17,5996.66015625
^GSPC,2025-01-21,6049.240234375
^GSPC,2025-01-22,6086.3701171875
^GSPC,2025-01-23,6118.7099609375
^GSPC,2025-01-24,6101.240234375
^GSPC,2025-01-27,6012.27978515625
^GSPC,2025-01-28,6067.7001953125
^GSPC,2025-01-29,6039.31005859375
^GSPC,2025-01-30,6071.169921875
^GSPC,2025-01-31,6040.52978515625
^GSPC,2025-02-03,5994.56982421875
^GSPC,2025-02-04,6037.8798828125
^GSPC,2025-02-05,6061.47998046875
^GSPC,2025-02-06,6083.56982421875
^GSPC,2025-02-07,6025.990234375
^GSPC,2025-02-10,6066.43994140625
^GSPC,2025-02-11,6068.5
^GSPC,2025-02-12,6051.97021484375
^GSPC,2025-02-13,6115.06982421875
^GSPC,2025-02-14,6114.6298828125
^GSPC,2025-02-18,6129.580078125
^GSPC,2025-02-19,6144.14990234375
^GSPC,2025-02-20,6117.52001953125
^GSPC,2025-02-21,6013.1298828125
^GSPC,2025-02-24,5983.25
^GSPC,2025-02-25,5955.25
^GSPC,2025-02-26,5956.06005859375
^GSPC,2025-02-27,5861.56982421875
^GSPC,2025-02-28,5954.5
^GSPC,2025-03-03,5849.72021484375
^GSPC,2025-03-04,5778.14990234375
^GSPC,2025-03-05,5842.6298828125
^GSPC,2025-03-06,5738.52001953125
^GSPC,2025-03-07,5770.2001953125
^GSPC,2025-03-10,5614.56005859375
^GSPC,2025-03-11,5572.06982421875
^GSPC,2025-03-12,5599.2998046875
^GSPC,2025-03-13,5521.52001953125
^GSPC,2025-03-14,5638.93994140625
^GSPC,2025-03-17,5675.1201171875
^GSPC,2025-03-18,5614.66015625
^GSPC,2025-03-19,5675.2900390625
^GSPC,2025-03-20,5662.89013671875
^GSPC,2025-03-21,5667.56005859375
^GSPC,2025-03-24,5767.56982421875
^GSPC,2025-03-25,5776.64990234375
^GSPC,2025-03-26,5712.2001953125
^GSPC,2025-03-27,5693.31005859375
^GSPC,2025-03-28,5580.93994140625
^GSPC,2025-03-31,5611.85009765625
"""
    temp = tempfile.NamedTemporaryFile(delete=False, suffix=".csv", mode='w', newline='')
    temp.write(sample_data)
    temp.close()
    return temp.name

# Global theme
theme = gr.themes.Soft(
    primary_hue="blue",
    secondary_hue="indigo",
    neutral_hue="gray"
)

# Gradio interface
with gr.Blocks(title="Time Series Forecasting App", theme=theme) as app:
    gr.Markdown("# 📈 Time Series Forecasting App")
    gr.Markdown("Upload a CSV with `unique_id`, `ds`, and `y` columns to apply forecasting models.")

    with gr.Row():
        with gr.Column(scale=2):
            file_input = gr.File(label="Upload CSV file", file_types=[".csv"])

            download_btn = gr.Button("Download Sample Data", variant="secondary")
            download_output = gr.File(label="Click to download", visible=True)
            download_btn.click(fn=download_sample, outputs=download_output)

            with gr.Accordion("Data & Validation Settings", open=True):
                frequency = gr.Dropdown(
                    choices=[
                        ("Hourly", "H"), 
                        ("Daily", "D"), 
                        ("Weekly", "WS"), 
                        ("Monthly", "MS"), 
                        ("Quarterly", "QS"), 
                        ("Yearly", "YS")
                    ], 
                    label="Data Frequency", 
                    value="D"
                )
                
                # Evaluation Strategy
                eval_strategy = gr.Radio(
                    choices=["Fixed Window", "Cross Validation"], 
                    label="Evaluation Strategy", 
                    value="Cross Validation"
                )
                
                # Fixed Window settings
                with gr.Group(visible=True) as fixed_window_box:
                    gr.Markdown("### Fixed Window Settings")
                    horizon = gr.Slider(1, 100, value=10, step=1, label="Validation Horizon (steps ahead to predict)")
                
                # Cross Validation settings
                with gr.Group(visible=True) as cv_box:
                    gr.Markdown("### Cross Validation Settings")
                    with gr.Row():
                        step_size = gr.Slider(1, 50, value=10, step=1, label="Step Size (distance between windows)")
                        num_windows = gr.Slider(1, 20, value=5, step=1, label="Number of Windows")
                
                # Future forecast settings (always visible)
                with gr.Group():
                    gr.Markdown("### Future Forecast Settings")
                    future_horizon = gr.Slider(1, 100, value=10, step=1, label="Future Forecast Horizon (steps to predict)")

            with gr.Accordion("Model Configuration", open=True):
                gr.Markdown("## Basic Models")
                with gr.Row():
                    use_historical_avg = gr.Checkbox(label="Historical Average", value=True)
                    use_naive = gr.Checkbox(label="Naive", value=True)
                
                # Common seasonality parameter at the top level
                with gr.Group():
                    gr.Markdown("### Seasonality Configuration")
                    gr.Markdown("This seasonality period affects Seasonal Naive, Seasonal Window Average, AutoETS, and AutoARIMA models")
                    seasonality = gr.Number(label="Seasonality Period", value=5)
                
                gr.Markdown("### Seasonal Models")
                with gr.Row():
                    use_seasonal_naive = gr.Checkbox(label="Seasonal Naive", value=True)
                
                gr.Markdown("### Window-based Models")
                with gr.Row():
                    use_window_avg = gr.Checkbox(label="Window Average", value=True)
                    window_size = gr.Number(label="Window Size", value=10)
                
                with gr.Row():
                    use_seasonal_window_avg = gr.Checkbox(label="Seasonal Window Average", value=True)
                    seasonal_window_size = gr.Number(label="Seasonal Window Size", value=2)
                
                gr.Markdown("### Advanced Models (use seasonality from above)")
                with gr.Row():
                    use_autoets = gr.Checkbox(label="AutoETS (Exponential Smoothing)", value=True)
                    use_autoarima = gr.Checkbox(label="AutoARIMA", value=True)

        with gr.Column(scale=3):
            message_output = gr.Textbox(label="Status Message")
            
            with gr.Tabs() as tabs:
                with gr.TabItem("Validation Results"):
                    eval_output = gr.Dataframe(label="Evaluation Metrics")
                    validation_plot = gr.Plot(label="Validation Plot")
                    validation_output = gr.Dataframe(label="Validation Data", visible=False)
                    
                    with gr.Row():
                        show_data_btn = gr.Button("Show Validation Data")
                        hide_data_btn = gr.Button("Hide Validation Data", visible=False)
                    
                    def show_data():
                        return gr.update(visible=True), gr.update(visible=True), gr.update(visible=False)
                    
                    def hide_data():
                        return gr.update(visible=False), gr.update(visible=False), gr.update(visible=True)
                    
                    show_data_btn.click(
                        fn=show_data,
                        outputs=[validation_output, hide_data_btn, show_data_btn]
                    )
                    
                    hide_data_btn.click(
                        fn=hide_data,
                        outputs=[validation_output, hide_data_btn, show_data_btn]
                    )
                
                with gr.TabItem("Future Forecast"):
                    forecast_plot = gr.Plot(label="Future Forecast Plot")
                    forecast_output = gr.Dataframe(label="Future Forecast Data", visible=False)
                    
                    with gr.Row():
                        show_forecast_btn = gr.Button("Show Forecast Data")
                        hide_forecast_btn = gr.Button("Hide Forecast Data", visible=False)
                    
                    def show_forecast():
                        return gr.update(visible=True), gr.update(visible=True), gr.update(visible=False)
                    
                    def hide_forecast():
                        return gr.update(visible=False), gr.update(visible=False), gr.update(visible=True)
                    
                    show_forecast_btn.click(
                        fn=show_forecast,
                        outputs=[forecast_output, hide_forecast_btn, show_forecast_btn]
                    )
                    
                    hide_forecast_btn.click(
                        fn=hide_forecast,
                        outputs=[forecast_output, hide_forecast_btn, show_forecast_btn]
                    )
                
                with gr.TabItem("Export Results"):
                    export_files = gr.Files(label="Download Results")

    with gr.Row(visible=True) as run_row:
        submit_btn = gr.Button("Run Validation and Forecast", variant="primary", size="lg")
    
    # Update visibility of the appropriate box based on evaluation strategy
    def update_eval_boxes(strategy):
        return (gr.update(visible=strategy == "Fixed Window"), 
                gr.update(visible=strategy == "Cross Validation"))
        
    eval_strategy.change(
        fn=update_eval_boxes,
        inputs=[eval_strategy],
        outputs=[fixed_window_box, cv_box]
    )

    # Run forecast when button is clicked
    submit_btn.click(
        fn=run_forecast,
        inputs=[
            file_input, frequency, eval_strategy, horizon, step_size, num_windows,
            use_historical_avg, use_naive, use_seasonal_naive, seasonality,
            use_window_avg, window_size, use_seasonal_window_avg, seasonal_window_size,
            use_autoets, use_autoarima, future_horizon
        ],
        outputs=[eval_output, validation_output, validation_plot, forecast_output, forecast_plot, export_files, message_output]
    )

if __name__ == "__main__":
    app.launch(share=False)