tools/forecaster.py

import pandas as pd
from statsmodels.tsa.arima.model import ARIMA
import plotly.graph_objects as go

def forecast_metric_tool(file_path: str, date_col: str, value_col: str):
    """
    Forecast next 3 periods for any numeric metric.
    Saves PNG and returns forecast DataFrame as text.
    """
    df = pd.read_csv(file_path)

    try:
        df[date_col] = pd.to_datetime(df[date_col])
    except Exception:
        return f"❌ '{date_col}' not parseable as dates."

    if value_col not in df.columns:
        return f"❌ '{value_col}' column missing."

    df.set_index(date_col, inplace=True)
    model = ARIMA(df[value_col], order=(1, 1, 1))
    model_fit = model.fit()
    forecast = model_fit.forecast(steps=3)

    # Plot
    fig = go.Figure()
    fig.add_scatter(x=df.index, y=df[value_col], mode="lines", name=value_col)
    fig.add_scatter(x=forecast.index, y=forecast, mode="lines", name="Forecast")
    fig.update_layout(title=f"{value_col} Forecast", template="plotly_dark")
    fig.write_image("forecast_plot.png")

    return forecast.to_frame(name="Forecast").to_string()