tools/forecaster.py

# 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 to /tmp via our safe write monkey-patch, returns forecast table as text.
    """
    # 1) Load
    df = pd.read_csv(file_path)

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

    # 3) Coerce metric to numeric, drop invalid
    df[value_col] = pd.to_numeric(df[value_col], errors="coerce")
    series = df.set_index(date_col)[value_col].dropna()

    if series.empty:
        return f"❌ Column '{value_col}' has no valid numeric data after coercion."

    # 4) Fit ARIMA
    try:
        model     = ARIMA(series, order=(1, 1, 1))
        model_fit = model.fit()
    except Exception as e:
        return f"❌ ARIMA fitting failed: {e}"

    # 5) Forecast & plot
    forecast = model_fit.forecast(steps=3)
    fig = go.Figure()
    fig.add_scatter(x=series.index, y=series, 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")  # goes into /tmp thanks to our monkey-patch

    # 6) Return textual table
    return forecast.to_frame(name="Forecast").to_string()