Update app.py

app.py

@@ -31,22 +31,21 @@ def make_demo_dataframe() -> pd.DataFrame:
 def load_csv(file: gr.File | None) -> pd.DataFrame:
     """讀 CSV（需含時間欄）；若未上傳則回傳示範資料。統一轉為 Asia/Taipei 並依時間排序。"""
     if file is None:
-        return make_demo_dataframe()
-
-    df = pd.read_csv(file.name)
-    time_col_candidates = ["time", "timestamp", "datetime", "date"]
-    time_col = next((c for c in time_col_candidates if c in df.columns), None)
-    if time_col is None:
-        raise ValueError("CSV 檔需包含時間欄位，例如：time / timestamp / datetime / date")
-
-    df[time_col] = pd.to_datetime(df[time_col])
-    df.rename(columns={time_col: "time"}, inplace=True)
-
-    # 統一時區
-    if getattr(df["time"].dt, "tz", None) is None:
-        df["time"] = df["time"].dt.tz_localize(TAIPEI)
+        df = make_demo_dataframe()
     else:
-        df["time"] = df["time"].dt.tz_convert(TAIPEI)
+        df = pd.read_csv(file.name)
+        time_col_candidates = ["time", "timestamp", "datetime", "date"]
+        time_col = next((c for c in time_col_candidates if c in df.columns), None)
+        if time_col is None:
+            raise ValueError("CSV 檔需包含時間欄位，例如：time / timestamp / datetime / date")
+        df[time_col] = pd.to_datetime(df[time_col])
+        df.rename(columns={time_col: "time"}, inplace=True)
+
+        # 統一時區
+        if getattr(df["time"].dt, "tz", None) is None:
+            df["time"] = df["time"].dt.tz_localize(TAIPEI)
+        else:
+            df["time"] = df["time"].dt.tz_convert(TAIPEI)
 
     df = df.sort_values("time").reset_index(drop=True)
     return df
@@ -83,7 +82,6 @@ def build_grafanalib_dashboard(series_columns: list[str], dual_axis: bool, rolli
         title = f"Event Count (Bar) - {series_columns[1]}"
 
         if dual_axis:
-            # 在 JSON 中同面板加上第一欄（折線）；實際右軸指定在 Grafana 端做 series override
             targets.append(Target(expr=f"{series_columns[0]}", legendFormat=f"{series_columns[0]} (line)"))
             lines = True
             bars = True
@@ -147,10 +145,10 @@ def render_bar_or_dual(df: pd.DataFrame, second_col: str, first_col: str, dual_a
         ax2.set_ylabel(first_col)
         title = f"Bar+Line (Dual Axis) - {second_col} / {first_col}"
 
-        # 簡單處理圖例
-        lines_labels = [(l, l.get_label()) for l in ax.get_legend_handles_labels()[0]]
-        lines_labels += [(l, l.get_label()) for l in ax2.get_legend_handles_labels()[0]]
-        ax.legend([h for h, _ in lines_labels], [lab for _, lab in lines_labels], loc="upper left")
+        # 合併圖例
+        h1, l1 = ax.get_legend_handles_labels()
+        h2, l2 = ax2.get_legend_handles_labels()
+        ax.legend(h1 + h2, l1 + l2, loc="upper left")
     else:
         ax.legend(loc="upper left")
 
@@ -179,11 +177,10 @@ def render_rolling(df: pd.DataFrame, col: str, window: int = 5):
 # -----------------------------
 def pipeline(file, series_choice, dual_axis, rolling_window):
     """
-    流程：
-      1) 讀 CSV（或示範）
-      2) 決定欄位（第一欄做折線 + rolling；第二欄做柱狀或雙軸混合）
-      3) 產出 grafanalib Dashboard JSON（反映 dual_axis 與 rolling_window）
-      4) 回傳三張圖與下載檔（以「路徑」回傳）
+    1) 讀 CSV（或示範）
+    2) 決定欄位（第一欄做折線 + rolling；第二欄做柱狀或雙軸混合）
+    3) 產出 grafanalib Dashboard JSON（反映 dual_axis 與 rolling_window）
+    4) 回傳三張圖、dashboard.json 下載路徑、資料表（含 rolling 欄位）、demo.csv 路徑
     """
     df = load_csv(file)
 
@@ -206,7 +203,7 @@ def pipeline(file, series_choice, dual_axis, rolling_window):
         f.write(dash_json_str)
         json_path = f.name
 
-    # Figures
+    # 圖形
     fig1 = render_line(df, chosen[0])
     if len(chosen) > 1:
         fig2 = render_bar_or_dual(df, chosen[1], chosen[0], bool(dual_axis))
@@ -218,7 +215,13 @@ def pipeline(file, series_choice, dual_axis, rolling_window):
 
     fig3, df_with_roll = render_rolling(df.copy(), chosen[0], window=int(rolling_window))
 
-    return fig1, fig2, fig3, dash_json_str, json_path, df_with_roll
+    # 產生 demo CSV 檔案（每次呼叫都更新內容）
+    demo_df = make_demo_dataframe()
+    with tempfile.NamedTemporaryFile(delete=False, suffix=".csv", mode="w", encoding="utf-8") as f:
+        demo_df.to_csv(f, index=False)
+        demo_csv_path = f.name
+
+    return fig1, fig2, fig3, dash_json_str, json_path, df_with_roll, demo_csv_path
 
 
 # -----------------------------
@@ -226,11 +229,11 @@ def pipeline(file, series_choice, dual_axis, rolling_window):
 # -----------------------------
 with gr.Blocks(theme=gr.themes.Soft()) as demo:
     gr.Markdown(
-        "# grafanalib 生成 Dashboard + Gradio 呈現\n"
+        "## grafanalib + Gradio 示範\n"
         "- 圖1：折線（第一個數值欄）\n"
-        "- 圖2：柱狀（第二個數值欄）或「雙軸：左柱狀(第二欄) + 右折線(第一欄)」\n"
-        "- 圖3：第一個欄位的 Rolling Mean（下拉可調視窗：3/5/10/20）\n\n"
-        "**說明**：grafanalib 產生可匯入 Grafana 的 Dashboard JSON；下方 Matplotlib 圖為 Space 端即時呈現。"
+        "- 圖2：柱狀（第二個數值欄）或雙軸（左柱狀=第二欄、右折線=第一欄）\n"
+        "- 圖3：第一個欄位的 Rolling Mean（下拉可調 3/5/10/20）\n\n"
+        "✅ 可下載 `dashboard.json` 與 **示範資料 `demo.csv`**；載入頁面即顯示示範資料"
     )
 
     with gr.Row():
@@ -251,9 +254,10 @@ with gr.Blocks(theme=gr.themes.Soft()) as demo:
         plot2 = gr.Plot(label="圖 2：Bar / Dual Axis")
         plot3 = gr.Plot(label="圖 3：Rolling Mean")
 
-    json_box = gr.Code(label="grafanalib Dashboard JSON（可匯入真正的 Grafana）", language="json")
+    json_box = gr.Code(label="grafanalib Dashboard JSON", language="json")
     json_file = gr.File(label="下載 dashboard.json")
-    df_view = gr.Dataframe(label="資料預覽（包含 rolling 欄位）", wrap=True)
+    demo_csv_file = gr.File(label="下載示範資料 demo.csv")
+    df_view = gr.Dataframe(label="資料預覽（含 rolling 欄位）", wrap=True)
 
     # 依檔案動態更新欄位選單與表格
     def probe_columns(file):
@@ -262,23 +266,26 @@ with gr.Blocks(theme=gr.themes.Soft()) as demo:
         default_select = numeric_cols[:2]
         return gr.CheckboxGroup(choices=numeric_cols, value=default_select), df
 
-    # 初次載入：以 Demo 資料跑一次
+    # 初次載入：以 Demo 資料跑一次（預設就顯示示範資料、三張圖、兩個下載檔）
     def initial_load():
-        f1, f2, f3, dash_json, json_path, df = pipeline(
+        f1, f2, f3, dash_json, json_path, df, demo_csv_path = pipeline(
             file=None, series_choice=[], dual_axis=False, rolling_window="5"
         )
         numeric_cols = [c for c in df.columns if c != "time" and pd.api.types.is_numeric_dtype(df[c])]
         default_select = numeric_cols[:2]
         return (
-            f1, f2, f3, dash_json, json_path, df,
-            gr.CheckboxGroup(choices=numeric_cols, value=default_select),
-            False, "5"
+            f1, f2, f3,           # 三張圖
+            dash_json, json_path, # JSON 顯示與下載
+            df,                   # DataFrame 顯示（含 rolling 欄位）
+            gr.CheckboxGroup(choices=numeric_cols, value=default_select),  # 欄位多選
+            False, "5",           # dual-axis 預設 & rolling window 預設
+            demo_csv_path         # demo.csv 下載
         )
 
     demo.load(
         initial_load,
         inputs=None,
-        outputs=[plot1, plot2, plot3, json_box, json_file, df_view, series_multiselect, dual_axis_chk, rolling_dd],
+        outputs=[plot1, plot2, plot3, json_box, json_file, df_view, series_multiselect, dual_axis_chk, rolling_dd, demo_csv_file],
     )
 
     file_in.change(
@@ -290,7 +297,7 @@ with gr.Blocks(theme=gr.themes.Soft()) as demo:
     run_btn.click(
         pipeline,
         inputs=[file_in, series_multiselect, dual_axis_chk, rolling_dd],
-        outputs=[plot1, plot2, plot3, json_box, json_file, df_view],
+        outputs=[plot1, plot2, plot3, json_box, json_file, df_view, demo_csv_file],
     )
 
 if __name__ == "__main__":