# tools/forecaster.py
import os
import tempfile
import pandas as pd
from statsmodels.tsa.arima.model import ARIMA
import plotly.graph_objects as go
from typing import Tuple, Union


def forecast_metric_tool(
    file_path: str,
    date_col: str,
    value_col: str,
    periods: int = 3,
    output_dir: str = "/tmp"
) -> Union[Tuple[pd.DataFrame, str], str]:
    """
    Load CSV or Excel, parse a time series metric, fit ARIMA(1,1,1),
    forecast next `periods` steps, return DataFrame and PNG path.

    Returns:
      - (forecast_df, plot_path) on success
      - error string starting with '❌' on failure
    """
    # Load data
    ext = os.path.splitext(file_path)[1].lower()
    try:
        df = pd.read_excel(file_path) if ext in ('.xls', '.xlsx') else pd.read_csv(file_path)
    except Exception as exc:
        return f"❌ Failed to load file: {exc}"

    # Validate columns
    missing = [c for c in (date_col, value_col) if c not in df.columns]
    if missing:
        return f"❌ Missing column(s): {', '.join(missing)}"

    # Parse and clean
    try:
        df[date_col] = pd.to_datetime(df[date_col], errors='coerce')
    except Exception:
        return f"❌ Could not parse '{date_col}' as dates."
    df[value_col] = pd.to_numeric(df[value_col], errors='coerce')
    df = df.dropna(subset=[date_col, value_col])
    if df.empty:
        return f"❌ No valid data after cleaning '{date_col}'/'{value_col}'"

    # Aggregate duplicates and sort
    df = (
        df[[date_col, value_col]]
        .groupby(date_col, as_index=True)
        .mean()
        .sort_index()
    )

    # Infer frequency
    freq = pd.infer_freq(df.index) or 'D'
    try:
        df = df.asfreq(freq)
    except Exception:
        df = df[~df.index.duplicated(keep='first')].asfreq(freq)

    # Fit ARIMA
    try:
        model = ARIMA(df[value_col], order=(1, 1, 1))
        fit = model.fit()
    except Exception as exc:
        return f"❌ ARIMA fitting failed: {exc}"

    # Forecast
    try:
        pred = fit.get_forecast(steps=periods)
        forecast = pred.predicted_mean
    except Exception as exc:
        return f"❌ Forecast generation failed: {exc}"
    forecast_df = forecast.to_frame(name='Forecast')

    # Plot history + forecast
    fig = go.Figure(
        data=[
            go.Scatter(x=df.index, y=df[value_col], mode='lines', name='History'),
            go.Scatter(x=forecast.index, y=forecast, mode='lines+markers', name='Forecast')
        ]
    )
    fig.update_layout(
        title=f"{value_col} Forecast",
        xaxis_title=date_col,
        yaxis_title=value_col,
        template='plotly_dark'
    )

    # Save PNG
    os.makedirs(output_dir, exist_ok=True)
    tmp = tempfile.NamedTemporaryFile(suffix='.png', prefix='forecast_', dir=output_dir, delete=False)
    plot_path = tmp.name
    tmp.close()
    try:
        fig.write_image(plot_path, scale=2)
    except Exception as exc:
        return f"❌ Plot saving failed: {exc}"

    return forecast_df, plot_path