Update tools/forecaster.py

tools/forecaster.py

@@ -1,16 +1,45 @@
-
 import pandas as pd
 import matplotlib.pyplot as plt
 from statsmodels.tsa.arima.model import ARIMA
 
-def forecast_tool(file_path: str) -> str:
+def forecast_tool(file_path: str, date_col: str | None = None) -> str:
+    """
+    Forecast the next 3 periods of the 'Sales' column.
+    • If date_col is provided, use it.
+    • Otherwise auto‑detect the first column that can be parsed as dates.
+
+    Returns human‑readable summary and saves 'forecast_plot.png'.
+    """
     df = pd.read_csv(file_path)
-    df['Month'] = pd.to_datetime(df['Month'])
-    df.set_index('Month', inplace=True)
-    model = ARIMA(df['Sales'], order=(1, 1, 1))
+
+    # Auto‑detect date column if not specified
+    if date_col is None:
+        for col in df.columns:
+            try:
+                pd.to_datetime(df[col])
+                date_col = col
+                break
+            except Exception:
+                continue
+        if date_col is None:
+            return "❌ No parseable date column found."
+
+    # Parse the date column
+    try:
+        df[date_col] = pd.to_datetime(df[date_col])
+    except Exception:
+        return f"❌ Column '{date_col}' cannot be parsed as dates."
+
+    if "Sales" not in df.columns:
+        return "❌ CSV must contain a 'Sales' column."
+
+    df.set_index(date_col, inplace=True)
+    model = ARIMA(df["Sales"], order=(1, 1, 1))
     model_fit = model.fit()
     forecast = model_fit.forecast(steps=3)
-    df_forecast = pd.DataFrame(forecast, columns=['Forecast'])
-    df_forecast.plot(title="Sales Forecast", figsize=(10, 6))
+
+    forecast_df = pd.DataFrame(forecast, columns=["Forecast"])
+    forecast_df.plot(title="Sales Forecast", figsize=(10, 6))
     plt.savefig("forecast_plot.png")
-    return "Generated forecast_plot.png"
+
+    return forecast_df.to_string()