Update tools/forecaster.py

tools/forecaster.py

@@ -1,50 +1,32 @@
 import pandas as pd
-import plotly.graph_objects as go
 from statsmodels.tsa.arima.model import ARIMA
+import plotly.graph_objects as go
 
-def forecast_tool(file_path: str, date_col: str) -> str:
+def forecast_metric_tool(file_path: str, date_col: str, value_col: str):
     """
-    Forecast next 3 periods of 'Sales'. Returns text summary and saves forecast_plot.png.
+    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"❌ Column '{date_col}' cannot be parsed as dates."
+        return f"❌ '{date_col}' not parseable as dates."
 
-    if "Sales" not in df.columns:
-        return "❌ CSV must contain a 'Sales' column."
+    if value_col not in df.columns:
+        return f"❌ '{value_col}' column missing."
 
     df.set_index(date_col, inplace=True)
-    model = ARIMA(df["Sales"], order=(1, 1, 1))
+    model = ARIMA(df[value_col], order=(1, 1, 1))
     model_fit = model.fit()
     forecast = model_fit.forecast(steps=3)
 
-    # Interactive Plotly forecast with confidence interval
-    conf_int = model_fit.get_forecast(steps=3).conf_int()
-    future_index = forecast.index
-
+    # Plot
     fig = go.Figure()
-    fig.add_scatter(x=df.index, y=df["Sales"], mode="lines", name="Sales")
-    fig.add_scatter(x=future_index, y=forecast, mode="lines", name="Forecast")
-    fig.add_scatter(
-        x=future_index,
-        y=conf_int.iloc[:, 0],
-        mode="lines",
-        fill=None,
-        line=dict(width=0),
-        showlegend=False,
-    )
-    fig.add_scatter(
-        x=future_index,
-        y=conf_int.iloc[:, 1],
-        mode="lines",
-        fill="tonexty",
-        name="95% CI",
-        line=dict(width=0),
-    )
-    fig.update_layout(title="Sales Forecast", template="plotly_dark")
+    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()