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# 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, saving
the PNG under /tmp and returning the forecast table as text.
"""
# 1) Load & parse dates
df = pd.read_csv(file_path)
try:
df[date_col] = pd.to_datetime(df[date_col])
except Exception:
return f"❌ Could not parse '{date_col}' as dates."
# 2) Coerce metric to numeric & drop invalid rows
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 for '{value_col}'."
# 3) Sort by date, set index, then collapse any duplicate timestamps
df = df.sort_values(date_col).set_index(date_col)
# If you have multiple rows for the same timestamp, take their mean
df = df[[value_col]].groupby(level=0).mean()
# 4) Infer frequency (e.g. 'D', 'M', etc.) and reindex
freq = pd.infer_freq(df.index)
if freq is None:
freq = "D" # fallback to daily
df = df.asfreq(freq)
# 5) Fit ARIMA
try:
model = ARIMA(df[value_col], order=(1, 1, 1))
model_fit = model.fit()
except Exception as e:
return f"❌ ARIMA fitting failed: {e}"
# 6) Forecast with a proper DatetimeIndex
fc_res = model_fit.get_forecast(steps=3)
forecast = fc_res.predicted_mean # pd.Series indexed by future dates
# 7) Plot history + forecast
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+markers", name="Forecast")
fig.update_layout(
title=f"{value_col} Forecast",
xaxis_title=date_col,
yaxis_title=value_col,
template="plotly_dark",
)
fig.write_image("forecast_plot.png") # safely lands in /tmp via monkey-patch
# 8) Return the forecast table as plain text
return forecast.to_frame(name="Forecast").to_string()
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