Update tools/forecaster.py

tools/forecaster.py

@@ -1,30 +1,13 @@
 import pandas as pd
-import matplotlib.pyplot as plt
+import plotly.graph_objects as go
 from statsmodels.tsa.arima.model import ARIMA
 
-def forecast_tool(file_path: str, date_col: str | None = None) -> str:
+def forecast_tool(file_path: str, date_col: str) -> 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'.
+    Forecast next 3 periods of 'Sales'. Returns text summary and saves forecast_plot.png.
     """
     df = pd.read_csv(file_path)
 
-    # 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:
@@ -38,8 +21,30 @@ def forecast_tool(file_path: str, date_col: str | None = None) -> str:
     model_fit = model.fit()
     forecast = model_fit.forecast(steps=3)
 
-    forecast_df = pd.DataFrame(forecast, columns=["Forecast"])
-    forecast_df.plot(title="Sales Forecast", figsize=(10, 6))
-    plt.savefig("forecast_plot.png")
-
-    return forecast_df.to_string()
+    # Interactive Plotly forecast with confidence interval
+    conf_int = model_fit.get_forecast(steps=3).conf_int()
+    future_index = forecast.index
+
+    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.write_image("forecast_plot.png")
+
+    return forecast.to_frame(name="Forecast").to_string()