Update app.py

app.py

@@ -110,14 +110,11 @@ def _style_time_axis(ax):
     ax.tick_params(axis="x", labelrotation=20, labelsize=9)
     ax.tick_params(axis="y", labelsize=9)
     ax.grid(True, which="major", alpha=0.25)
-    plt.margins(x=0.02, y=0.05)  # 留一點白邊
+    plt.margins(x=0.02, y=0.05)
 
 
 def _normalize_times(series: pd.Series) -> pd.Series:
-    """
-    把可能帶時區的時間序列，轉成「UTC 再去時區」的 naive datetime，
-    以避免 Matplotlib 在處理 tz-aware 時間造成不一致。
-    """
+    """把可能帶時區的時間序列轉成 naive datetime"""
     s = series.copy()
     if getattr(s.dt, "tz", None) is not None:
         s = s.dt.tz_convert("UTC").dt.tz_localize(None)
@@ -125,13 +122,13 @@ def _normalize_times(series: pd.Series) -> pd.Series:
 
 
 def _infer_bar_width_days(times: pd.Series) -> float:
-    """依時間間距估算柱寬（單位：天）。避免整片藍牆。"""
+    """依時間間距估算柱寬（單位：天）"""
     t = pd.Series(times)
     if len(t) < 2:
         return 60 / 86400  # 60秒
     diffs = t.astype("datetime64[ns]").diff().dt.total_seconds().dropna()
     med = diffs.median() if not diffs.empty else 60.0
-    return max(10.0, med * 0.8) / 86400.0  # 取 80% 的中位數，至少 10 秒
+    return max(10.0, med * 0.8) / 86400.0
 
 
 def render_line(df: pd.DataFrame, col: str):
@@ -147,9 +144,9 @@ def render_line(df: pd.DataFrame, col: str):
 
 
 def render_bar_or_dual(df: pd.DataFrame, second_col: str, first_col: str, dual_axis: bool):
-    # --- 修正點：正確轉換 Series 為原生 datetime（處理時區 + .dt.to_pydatetime()） ---
     times = _normalize_times(df["time"])
-    x = mdates.date2num(times.dt.to_pydatetime())
+    # ✅ 修正：to_pydatetime → to_list() 避免 FutureWarning
+    x = mdates.date2num(times.dt.to_pydatetime().tolist())
 
     fig, ax = plt.subplots(figsize=(6.5, 3.6))
     width = _infer_bar_width_days(times)
@@ -202,34 +199,25 @@ def _placeholder_fig():
 # Main pipeline
 # -----------------------------
 def pipeline(file, series_choice, dual_axis, rolling_window):
-    """
-    1) 讀 CSV（或示範）
-    2) 第一欄→折線與 rolling；第二欄→柱狀/雙軸
-    3) 產出 grafanalib Dashboard JSON
-    4) 回傳三張圖、dashboard.json 路徑、資料表（含 rolling）、demo.csv 路徑
-    """
     df = load_csv(file)
     numeric_cols = [c for c in df.columns if c != "time" and pd.api.types.is_numeric_dtype(df[c])]
     if not numeric_cols:
-        raise ValueError("未找到可繪圖的數值欄位。請提供至少一個數值欄位（除了 time 以外）。")
+        raise ValueError("未找到可繪圖的數值欄位。")
 
     chosen = [c for c in (series_choice or numeric_cols[:2]) if c in numeric_cols]
     if not chosen:
         chosen = numeric_cols[:2]
 
-    # Dashboard JSON
     dash_json = build_grafanalib_dashboard(chosen, bool(dual_axis), int(rolling_window))
     dash_json_str = json.dumps(dash_json, ensure_ascii=False, indent=2, default=str)
     with tempfile.NamedTemporaryFile(delete=False, suffix=".json", mode="w", encoding="utf-8") as f:
         f.write(dash_json_str)
         json_path = f.name
 
-    # Figures
     fig1 = render_line(df, chosen[0])
     fig2 = render_bar_or_dual(df, chosen[1], chosen[0], bool(dual_axis)) if len(chosen) > 1 else _placeholder_fig()
     fig3, df_with_roll = render_rolling(df.copy(), chosen[0], window=int(rolling_window))
 
-    # demo.csv（每次都用全新隨機）
     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)
@@ -242,7 +230,6 @@ def pipeline(file, series_choice, dual_axis, rolling_window):
 # Regenerate demo helper
 # -----------------------------
 def regenerate_demo(series_choice, dual_axis, rolling_window):
-    """忽略上傳檔案，使用全新隨機示範資料。"""
     return pipeline(file=None, series_choice=series_choice, dual_axis=dual_axis, rolling_window=rolling_window)
 
 
@@ -250,24 +237,18 @@ def regenerate_demo(series_choice, dual_axis, rolling_window):
 # UI
 # -----------------------------
 with gr.Blocks(theme=gr.themes.Soft()) as demo:
-    gr.Markdown(
-        "## Grafana-like 隨機示範\n"
-        "為手機優化：精簡時間軸、控制刻度、加上網格與間距。"
-    )
+    gr.Markdown("## Grafana-like 隨機示範\n手機優化：精簡時間軸、網格、隨機資料")
 
     with gr.Row():
         file_in = gr.File(label="上傳 CSV（可空，會用隨機示範資料）", file_types=[".csv"])
-        series_multiselect = gr.CheckboxGroup(
-            label="數值欄位（第一欄→折線/rolling；第二欄→柱狀/雙軸）",
-            choices=[]
-        )
+        series_multiselect = gr.CheckboxGroup(label="數值欄位", choices=[])
 
     with gr.Row():
         dual_axis_chk = gr.Checkbox(label="第二面板啟用雙軸", value=False)
         rolling_dd = gr.Dropdown(label="Rolling window", choices=["3", "5", "10", "20"], value="5")
 
     with gr.Row():
-        run_btn = gr.Button("用『上傳/當前資料』產生 Dashboard")
+        run_btn = gr.Button("產生 Dashboard")
         regen_btn = gr.Button("🔁 重新產生示範資料（隨機）")
 
     with gr.Row():
@@ -282,7 +263,6 @@ with gr.Blocks(theme=gr.themes.Soft()) as demo:
     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)
 
-    # 檔案上傳時，更新可選欄位與預覽
     def probe_columns(file):
         df = load_csv(file)
         numeric_cols = [c for c in df.columns if c != "time" and pd.api.types.is_numeric_dtype(df[c])]
@@ -290,7 +270,6 @@ with gr.Blocks(theme=gr.themes.Soft()) as demo:
 
     file_in.change(probe_columns, inputs=[file_in], outputs=[series_multiselect, df_view])
 
-    # 初次載入：以隨機示範資料跑一次並更新欄位清單
     def initial_load():
         f1, f2, f3, dash_json, json_path, df, demo_csv_path = pipeline(
             file=None, series_choice=[], dual_axis=False, rolling_window="5"
@@ -302,26 +281,18 @@ with gr.Blocks(theme=gr.themes.Soft()) as demo:
             False, "5"
         )
 
-    demo.load(
-        initial_load,
-        inputs=None,
-        outputs=[plot1, plot2, plot3, json_box, json_file, df_view, demo_csv_file,
-                 series_multiselect, dual_axis_chk, rolling_dd],
-    )
+    demo.load(initial_load,
+              inputs=None,
+              outputs=[plot1, plot2, plot3, json_box, json_file, df_view, demo_csv_file,
+                       series_multiselect, dual_axis_chk, rolling_dd])
 
-    # 用「上傳/當前資料」產生
-    run_btn.click(
-        pipeline,
-        inputs=[file_in, series_multiselect, dual_axis_chk, rolling_dd],
-        outputs=[plot1, plot2, plot3, json_box, json_file, df_view, demo_csv_file],
-    )
+    run_btn.click(pipeline,
+                  inputs=[file_in, series_multiselect, dual_axis_chk, rolling_dd],
+                  outputs=[plot1, plot2, plot3, json_box, json_file, df_view, demo_csv_file])
 
-    # 🔁 重新產生「隨機示範資料」
-    regen_btn.click(
-        regenerate_demo,
-        inputs=[series_multiselect, dual_axis_chk, rolling_dd],
-        outputs=[plot1, plot2, plot3, json_box, json_file, df_view, demo_csv_file],
-    )
+    regen_btn.click(regenerate_demo,
+                    inputs=[series_multiselect, dual_axis_chk, rolling_dd],
+                    outputs=[plot1, plot2, plot3, json_box, json_file, df_view, demo_csv_file])
 
 if __name__ == "__main__":
     demo.launch()