trying to fix logo to show

app.py

@@ -1,448 +1,448 @@
-"""
-Decomposition Explorer
-Interactive tool for exploring time-series decomposition methods.
-Part of ISA 444: Business Forecasting at Miami University (Spring 2026).
-
-Deployed to HuggingFace Spaces as fmegahed/decomposition-explorer.
-"""
-
-import io
-import warnings
-
-import gradio as gr
-import matplotlib
-matplotlib.use("Agg")
-import matplotlib.pyplot as plt
-import numpy as np
-import pandas as pd
-from statsmodels.tsa.seasonal import STL, seasonal_decompose
-
-# ---------------------------------------------------------------------------
-# Color palette
-# ---------------------------------------------------------------------------
-CLR_PRIMARY = "#84d6d3"   # teal
-CLR_ACCENT  = "#C3142D"   # Miami red
-CLR_TREND   = "#C3142D"
-CLR_SEASON  = "#84d6d3"
-CLR_RESID   = "#666666"
-
-# ---------------------------------------------------------------------------
-# Built-in datasets
-# ---------------------------------------------------------------------------
-
-def _airline_passengers() -> pd.DataFrame:
-    """Classic Box-Jenkins airline passengers (1949-1960, monthly)."""
-    try:
-        from statsmodels.datasets import co2  # noqa: F401
-        import statsmodels.api as sm
-        data = sm.datasets.get_rdataset("AirPassengers", "datasets").data
-        dates = pd.date_range(start="1949-01-01", periods=len(data), freq="MS")
-        return pd.DataFrame({"ds": dates, "y": data.iloc[:, 0].values})
-    except Exception:
-        # Fallback: generate the well-known series manually
-        np.random.seed(0)
-        dates = pd.date_range("1949-01-01", "1960-12-01", freq="MS")
-        n = len(dates)
-        t = np.arange(n)
-        trend = 110 + 2.5 * t
-        seasonal_pattern = np.array(
-            [-24, -20, 2, -1, -5, 30, 47, 46, 14, -10, -25, -26]
-        )
-        season = np.tile(seasonal_pattern, n // 12 + 1)[:n]
-        noise = np.random.normal(0, 6, n)
-        y = trend + season * (1 + 0.02 * t) + noise
-        return pd.DataFrame({"ds": dates, "y": np.round(y, 1)})
-
-
-def _us_retail_employment() -> pd.DataFrame:
-    """Realistic synthetic monthly US retail employment (2000-2024)."""
-    np.random.seed(42)
-    dates = pd.date_range("2000-01-01", "2024-12-01", freq="MS")
-    n = len(dates)
-    t = np.arange(n)
-
-    # Trend: upward with dips around 2008-09 and 2020
-    trend = 15_000 + 12 * t
-    # 2008-2009 recession dip
-    recession_08 = -1400 * np.exp(-0.5 * ((t - 108) / 8) ** 2)
-    # 2020 COVID dip
-    covid_20 = -2800 * np.exp(-0.5 * ((t - 243) / 3) ** 2)
-    trend = trend + recession_08 + covid_20
-
-    # Seasonal pattern (retail peaks in Nov-Dec)
-    seasonal_pattern = np.array(
-        [-200, -350, -100, 50, 100, 150, 100, 80, -50, -100, 250, 500]
-    )
-    season = np.tile(seasonal_pattern, n // 12 + 1)[:n]
-
-    noise = np.random.normal(0, 60, n)
-    y = trend + season + noise
-    return pd.DataFrame({"ds": dates, "y": np.round(y, 1)})
-
-
-def _ohio_nonfarm() -> pd.DataFrame:
-    """Realistic synthetic monthly Ohio nonfarm employment (2010-2024)."""
-    np.random.seed(7)
-    dates = pd.date_range("2010-01-01", "2024-12-01", freq="MS")
-    n = len(dates)
-    t = np.arange(n)
-
-    trend = 5_100 + 4.5 * t
-    # COVID dip
-    covid = -650 * np.exp(-0.5 * ((t - 123) / 3) ** 2)
-    trend = trend + covid
-
-    seasonal_pattern = np.array(
-        [-80, -50, 30, 50, 70, 60, 20, 10, 30, 20, -30, -60]
-    )
-    season = np.tile(seasonal_pattern, n // 12 + 1)[:n]
-
-    noise = np.random.normal(0, 25, n)
-    y = trend + season + noise
-    return pd.DataFrame({"ds": dates, "y": np.round(y, 1)})
-
-
-BUILTIN_DATASETS = {
-    "Airline Passengers": _airline_passengers,
-    "US Retail Employment": _us_retail_employment,
-    "Ohio Nonfarm Employment": _ohio_nonfarm,
-}
-
-# ---------------------------------------------------------------------------
-# Helpers
-# ---------------------------------------------------------------------------
-
-def _load_dataset(name: str, csv_file) -> pd.DataFrame:
-    """Return a DataFrame with columns ds (datetime) and y (float)."""
-    if csv_file is not None:
-        try:
-            raw = pd.read_csv(csv_file.name if hasattr(csv_file, "name") else csv_file)
-            if "ds" not in raw.columns or "y" not in raw.columns:
-                raise ValueError("CSV must contain columns 'ds' and 'y'.")
-            raw["ds"] = pd.to_datetime(raw["ds"])
-            raw["y"] = pd.to_numeric(raw["y"], errors="coerce")
-            raw = raw.dropna(subset=["y"]).sort_values("ds").reset_index(drop=True)
-            return raw
-        except Exception as exc:
-            raise gr.Error(f"Could not read uploaded CSV: {exc}")
-    if name in BUILTIN_DATASETS:
-        return BUILTIN_DATASETS[name]()
-    raise gr.Error(f"Unknown dataset: {name}")
-
-
-def _ensure_odd(val: int) -> int:
-    """Force a value to be odd (required by statsmodels windows)."""
-    val = int(val)
-    return val if val % 2 == 1 else val + 1
-
-
-def _strength(residual: np.ndarray, component_plus_residual: np.ndarray) -> float:
-    """Compute strength of a component: max(0, 1 - Var(R)/Var(C+R))."""
-    var_r = np.nanvar(residual)
-    var_cr = np.nanvar(component_plus_residual)
-    if var_cr == 0:
-        return 0.0
-    return float(max(0.0, 1.0 - var_r / var_cr))
-
-# ---------------------------------------------------------------------------
-# Core decomposition + plotting
-# ---------------------------------------------------------------------------
-
-def decompose_and_plot(
-    dataset_name: str,
-    csv_file,
-    method: str,
-    period: int,
-    stl_seasonal: int,
-    stl_trend: int,
-    stl_robust: bool,
-):
-    """Run decomposition and return (matplotlib Figure, summary string)."""
-
-    # --- Load data --------------------------------------------------------
-    df = _load_dataset(dataset_name, csv_file)
-
-    if len(df) < 2 * period:
-        raise gr.Error(
-            f"Not enough observations ({len(df)}) for the chosen period ({period}). "
-            f"Need at least {2 * period} observations."
-        )
-
-    y_series = pd.Series(df["y"].values, index=df["ds"])
-
-    # --- Decompose --------------------------------------------------------
-    with warnings.catch_warnings():
-        warnings.simplefilter("ignore")
-
-        if method == "STL":
-            stl_seasonal = _ensure_odd(stl_seasonal)
-            stl_trend_val = _ensure_odd(stl_trend) if stl_trend > 0 else None
-            stl_obj = STL(
-                y_series,
-                period=int(period),
-                seasonal=stl_seasonal,
-                trend=stl_trend_val,
-                robust=bool(stl_robust),
-            )
-            result = stl_obj.fit()
-        else:
-            model_type = "additive" if "Additive" in method else "multiplicative"
-            result = seasonal_decompose(
-                y_series, model=model_type, period=int(period)
-            )
-
-    observed = result.observed
-    trend = result.trend
-    seasonal = result.seasonal
-    resid = result.resid
-
-    # --- Strength measures ------------------------------------------------
-    r = resid.values
-    t = trend.values
-    s = seasonal.values
-    mask = ~(np.isnan(r) | np.isnan(t) | np.isnan(s))
-    r_clean = r[mask]
-    t_clean = t[mask]
-    s_clean = s[mask]
-
-    f_trend = _strength(r_clean, t_clean + r_clean)
-    f_season = _strength(r_clean, s_clean + r_clean)
-
-    # --- Plot -------------------------------------------------------------
-    fig, axes = plt.subplots(4, 1, figsize=(10, 8), sharex=True)
-    fig.patch.set_facecolor("white")
-    for ax in axes:
-        ax.set_facecolor("white")
-        ax.grid(True, linewidth=0.3, alpha=0.5)
-
-    dates = observed.index
-
-    # 1. Observed
-    axes[0].plot(dates, observed, color=CLR_PRIMARY, linewidth=1.2)
-    axes[0].set_ylabel("Observed", fontsize=10, fontweight="bold")
-
-    # 2. Trend
-    axes[1].plot(dates, trend, color=CLR_TREND, linewidth=1.4)
-    axes[1].set_ylabel("Trend", fontsize=10, fontweight="bold")
-
-    # 3. Seasonal
-    axes[2].plot(dates, seasonal, color=CLR_SEASON, linewidth=1.0)
-    axes[2].set_ylabel("Seasonal", fontsize=10, fontweight="bold")
-
-    # 4. Residual
-    axes[3].plot(dates, resid, color=CLR_RESID, linewidth=0.8, alpha=0.8)
-    axes[3].set_ylabel("Remainder", fontsize=10, fontweight="bold")
-    axes[3].set_xlabel("Date", fontsize=10)
-
-    method_label = method if method == "STL" else method.replace("Classical ", "Classical – ")
-    fig.suptitle(
-        f"Decomposition  ·  {method_label}  ·  period = {period}",
-        fontsize=13,
-        fontweight="bold",
-        y=0.98,
-    )
-    fig.tight_layout(rect=[0, 0, 1, 0.96])
-
-    # --- Summary text -----------------------------------------------------
-    summary = (
-        f"Strength of Trend (F_T):        {f_trend:.4f}\n"
-        f"Strength of Seasonality (F_S):   {f_season:.4f}\n\n"
-        f"Formulas:\n"
-        f"  F_T = max(0, 1 − Var(R) / Var(T + R))\n"
-        f"  F_S = max(0, 1 − Var(R) / Var(S + R))"
-    )
-
-    return fig, summary
-
-# ---------------------------------------------------------------------------
-# Gradio UI
-# ---------------------------------------------------------------------------
-
-_THEME = gr.themes.Soft(
-    primary_hue=gr.themes.Color(
-        c50="#fef2f3", c100="#fde6e8", c200="#fbd0d5",
-        c300="#f7a4ae", c400="#f17182", c500="#C3142D",
-        c600="#b01228", c700="#8B0E1E", c800="#6e0b18",
-        c900="#5c0d17", c950="#33040a",
-    ),
-    secondary_hue=gr.themes.Color(
-        c50="#fef2f3", c100="#fde6e8", c200="#fbd0d5",
-        c300="#f7a4ae", c400="#f17182", c500="#C3142D",
-        c600="#b01228", c700="#8B0E1E", c800="#6e0b18",
-        c900="#5c0d17", c950="#33040a",
-    ),
-    neutral_hue=gr.themes.Color(
-        c50="#EDECE2", c100="#E5E4D9", c200="#DDDCD0",
-        c300="#C8C7BC", c400="#A3A299", c500="#858479",
-        c600="#6B6A61", c700="#53524B", c800="#3B3A35",
-        c900="#252420", c950="#151410",
-    ),
-    font=[gr.themes.GoogleFont("Inter"), "ui-sans-serif", "system-ui", "sans-serif"],
-)
-
-_CSS = """
-.gradio-container { max-width: 1280px !important; margin: auto; }
-footer { display: none !important; }
-.gr-button-primary { background: #C3142D !important; border: none !important; }
-.gr-button-primary:hover { background: #8B0E1E !important; }
-.gr-button-secondary { border-color: #C3142D !important; color: #C3142D !important; }
-.gr-button-secondary:hover { background: #8B0E1E !important; color: white !important; }
-.gr-input:focus { border-color: #C3142D !important; box-shadow: 0 0 0 2px rgba(195,20,45,0.2) !important; }
-"""
-
-
-def build_app() -> gr.Blocks:
-    with gr.Blocks(title="Decomposition Explorer v1.0") as app:
-        gr.HTML("""
-        <div style="display: flex; align-items: center; gap: 16px; padding: 16px 24px;
-                    background: linear-gradient(135deg, #C3142D 0%, #8B0E1E 100%);
-                    border-radius: 12px; margin-bottom: 16px; box-shadow: 0 4px 12px rgba(0,0,0,0.15);">
-            <img src="/file=beveled-m-min-size.png"
-                 alt="Miami University" style="height: 56px; filter: brightness(0) invert(1);">
-            <div>
-                <h1 style="margin: 0; color: white; font-size: 24px; font-weight: 700; letter-spacing: -0.5px;">
-                    Decomposition Explorer v1.0
-                </h1>
-                <p style="margin: 4px 0 0; color: rgba(255,255,255,0.85); font-size: 14px;">
-                    ISA 444: Business Forecasting &middot; Farmer School of Business &middot; Miami University
-                </p>
-            </div>
-        </div>
-        """)
-
-        gr.HTML("""
-        <div style="background: #EDECE2; border-left: 4px solid #C3142D; padding: 12px 16px;
-                    border-radius: 0 8px 8px 0; margin-bottom: 16px; font-size: 14px; color: #585E60;">
-            Interactive tool for exploring time-series decomposition methods (Classical and STL).
-            Choose a built-in dataset or upload your own CSV, adjust decomposition parameters, and
-            examine trend, seasonal, and remainder components along with strength measures.
-        </div>
-        """)
-
-        with gr.Row():
-            # --- Left column: controls ------------------------------------
-            with gr.Column(scale=1, min_width=280):
-                dataset_dd = gr.Dropdown(
-                    label="Dataset",
-                    choices=list(BUILTIN_DATASETS.keys()),
-                    value="Airline Passengers",
-                )
-                csv_upload = gr.File(
-                    label="Or upload CSV (columns: ds, y)",
-                    file_types=[".csv"],
-                    type="filepath",
-                )
-                method_radio = gr.Radio(
-                    label="Decomposition Method",
-                    choices=[
-                        "Classical (Additive)",
-                        "Classical (Multiplicative)",
-                        "STL",
-                    ],
-                    value="STL",
-                )
-                period_slider = gr.Slider(
-                    label="Period / Season Length",
-                    minimum=2,
-                    maximum=52,
-                    step=1,
-                    value=12,
-                )
-
-                # STL-specific controls
-                stl_group = gr.Group(visible=True)
-                with stl_group:
-                    gr.Markdown("**STL Parameters**")
-                    stl_seasonal_slider = gr.Slider(
-                        label="seasonal (seasonality window, odd)",
-                        minimum=7,
-                        maximum=51,
-                        step=2,
-                        value=13,
-                    )
-                    stl_trend_slider = gr.Slider(
-                        label="trend (trend window, odd; 0 = auto)",
-                        minimum=0,
-                        maximum=101,
-                        step=2,
-                        value=0,
-                    )
-                    stl_robust_cb = gr.Checkbox(
-                        label="robust (robust to outliers)",
-                        value=False,
-                    )
-
-            # --- Right column: output -------------------------------------
-            with gr.Column(scale=3):
-                plot_output = gr.Plot(label="Decomposition")
-                summary_box = gr.Textbox(
-                    label="Strength Measures",
-                    lines=5,
-                    interactive=False,
-                )
-
-        # --- Visibility toggle for STL controls ---------------------------
-        def toggle_stl(method):
-            return gr.Group(visible=(method == "STL"))
-
-        method_radio.change(
-            fn=toggle_stl,
-            inputs=[method_radio],
-            outputs=[stl_group],
-        )
-
-        # --- Gather all inputs --------------------------------------------
-        all_inputs = [
-            dataset_dd,
-            csv_upload,
-            method_radio,
-            period_slider,
-            stl_seasonal_slider,
-            stl_trend_slider,
-            stl_robust_cb,
-        ]
-        all_outputs = [plot_output, summary_box]
-
-        # --- Wire change events -------------------------------------------
-        for ctrl in all_inputs:
-            ctrl.change(
-                fn=decompose_and_plot,
-                inputs=all_inputs,
-                outputs=all_outputs,
-            )
-
-        # --- Initial load -------------------------------------------------
-        app.load(
-            fn=decompose_and_plot,
-            inputs=all_inputs,
-            outputs=all_outputs,
-        )
-
-        gr.HTML("""
-        <div style="margin-top: 24px; padding: 16px; background: #EDECE2; border-radius: 8px;
-                    text-align: center; font-size: 13px; color: #585E60; border-top: 2px solid #C3142D;">
-            <div style="margin-bottom: 4px;">
-                <strong style="color: #C3142D;">Developed by</strong>
-                <a href="https://miamioh.edu/fsb/directory/?up=/directory/megahefm"
-                   style="color: #C3142D; text-decoration: none; font-weight: 600;">
-                    Fadel M. Megahed
-                </a>
-                &middot; Gloss Professor of Analytics &middot; Miami University
-            </div>
-            <div style="font-size: 12px; color: #888;">
-                Version 1.0.0 &middot; Spring 2026 &middot;
-                <a href="https://github.com/fmegahed" style="color: #C3142D; text-decoration: none;">GitHub</a> &middot;
-                <a href="https://www.linkedin.com/in/fmegahed/" style="color: #C3142D; text-decoration: none;">LinkedIn</a>
-            </div>
-        </div>
-        """)
-
-    return app
-
-
-# ---------------------------------------------------------------------------
-# Entry point
-# ---------------------------------------------------------------------------
-if __name__ == "__main__":
-    demo = build_app()
-    demo.launch(theme=_THEME, css=_CSS, ssr_mode=False, allowed_paths=["beveled-m-min-size.png"])
+"""
+Decomposition Explorer
+Interactive tool for exploring time-series decomposition methods.
+Part of ISA 444: Business Forecasting at Miami University (Spring 2026).
+
+Deployed to HuggingFace Spaces as fmegahed/decomposition-explorer.
+"""
+
+import io
+import warnings
+
+import gradio as gr
+import matplotlib
+matplotlib.use("Agg")
+import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
+from statsmodels.tsa.seasonal import STL, seasonal_decompose
+
+# ---------------------------------------------------------------------------
+# Color palette
+# ---------------------------------------------------------------------------
+CLR_PRIMARY = "#84d6d3"   # teal
+CLR_ACCENT  = "#C3142D"   # Miami red
+CLR_TREND   = "#C3142D"
+CLR_SEASON  = "#84d6d3"
+CLR_RESID   = "#666666"
+
+# ---------------------------------------------------------------------------
+# Built-in datasets
+# ---------------------------------------------------------------------------
+
+def _airline_passengers() -> pd.DataFrame:
+    """Classic Box-Jenkins airline passengers (1949-1960, monthly)."""
+    try:
+        from statsmodels.datasets import co2  # noqa: F401
+        import statsmodels.api as sm
+        data = sm.datasets.get_rdataset("AirPassengers", "datasets").data
+        dates = pd.date_range(start="1949-01-01", periods=len(data), freq="MS")
+        return pd.DataFrame({"ds": dates, "y": data.iloc[:, 0].values})
+    except Exception:
+        # Fallback: generate the well-known series manually
+        np.random.seed(0)
+        dates = pd.date_range("1949-01-01", "1960-12-01", freq="MS")
+        n = len(dates)
+        t = np.arange(n)
+        trend = 110 + 2.5 * t
+        seasonal_pattern = np.array(
+            [-24, -20, 2, -1, -5, 30, 47, 46, 14, -10, -25, -26]
+        )
+        season = np.tile(seasonal_pattern, n // 12 + 1)[:n]
+        noise = np.random.normal(0, 6, n)
+        y = trend + season * (1 + 0.02 * t) + noise
+        return pd.DataFrame({"ds": dates, "y": np.round(y, 1)})
+
+
+def _us_retail_employment() -> pd.DataFrame:
+    """Realistic synthetic monthly US retail employment (2000-2024)."""
+    np.random.seed(42)
+    dates = pd.date_range("2000-01-01", "2024-12-01", freq="MS")
+    n = len(dates)
+    t = np.arange(n)
+
+    # Trend: upward with dips around 2008-09 and 2020
+    trend = 15_000 + 12 * t
+    # 2008-2009 recession dip
+    recession_08 = -1400 * np.exp(-0.5 * ((t - 108) / 8) ** 2)
+    # 2020 COVID dip
+    covid_20 = -2800 * np.exp(-0.5 * ((t - 243) / 3) ** 2)
+    trend = trend + recession_08 + covid_20
+
+    # Seasonal pattern (retail peaks in Nov-Dec)
+    seasonal_pattern = np.array(
+        [-200, -350, -100, 50, 100, 150, 100, 80, -50, -100, 250, 500]
+    )
+    season = np.tile(seasonal_pattern, n // 12 + 1)[:n]
+
+    noise = np.random.normal(0, 60, n)
+    y = trend + season + noise
+    return pd.DataFrame({"ds": dates, "y": np.round(y, 1)})
+
+
+def _ohio_nonfarm() -> pd.DataFrame:
+    """Realistic synthetic monthly Ohio nonfarm employment (2010-2024)."""
+    np.random.seed(7)
+    dates = pd.date_range("2010-01-01", "2024-12-01", freq="MS")
+    n = len(dates)
+    t = np.arange(n)
+
+    trend = 5_100 + 4.5 * t
+    # COVID dip
+    covid = -650 * np.exp(-0.5 * ((t - 123) / 3) ** 2)
+    trend = trend + covid
+
+    seasonal_pattern = np.array(
+        [-80, -50, 30, 50, 70, 60, 20, 10, 30, 20, -30, -60]
+    )
+    season = np.tile(seasonal_pattern, n // 12 + 1)[:n]
+
+    noise = np.random.normal(0, 25, n)
+    y = trend + season + noise
+    return pd.DataFrame({"ds": dates, "y": np.round(y, 1)})
+
+
+BUILTIN_DATASETS = {
+    "Airline Passengers": _airline_passengers,
+    "US Retail Employment": _us_retail_employment,
+    "Ohio Nonfarm Employment": _ohio_nonfarm,
+}
+
+# ---------------------------------------------------------------------------
+# Helpers
+# ---------------------------------------------------------------------------
+
+def _load_dataset(name: str, csv_file) -> pd.DataFrame:
+    """Return a DataFrame with columns ds (datetime) and y (float)."""
+    if csv_file is not None:
+        try:
+            raw = pd.read_csv(csv_file.name if hasattr(csv_file, "name") else csv_file)
+            if "ds" not in raw.columns or "y" not in raw.columns:
+                raise ValueError("CSV must contain columns 'ds' and 'y'.")
+            raw["ds"] = pd.to_datetime(raw["ds"])
+            raw["y"] = pd.to_numeric(raw["y"], errors="coerce")
+            raw = raw.dropna(subset=["y"]).sort_values("ds").reset_index(drop=True)
+            return raw
+        except Exception as exc:
+            raise gr.Error(f"Could not read uploaded CSV: {exc}")
+    if name in BUILTIN_DATASETS:
+        return BUILTIN_DATASETS[name]()
+    raise gr.Error(f"Unknown dataset: {name}")
+
+
+def _ensure_odd(val: int) -> int:
+    """Force a value to be odd (required by statsmodels windows)."""
+    val = int(val)
+    return val if val % 2 == 1 else val + 1
+
+
+def _strength(residual: np.ndarray, component_plus_residual: np.ndarray) -> float:
+    """Compute strength of a component: max(0, 1 - Var(R)/Var(C+R))."""
+    var_r = np.nanvar(residual)
+    var_cr = np.nanvar(component_plus_residual)
+    if var_cr == 0:
+        return 0.0
+    return float(max(0.0, 1.0 - var_r / var_cr))
+
+# ---------------------------------------------------------------------------
+# Core decomposition + plotting
+# ---------------------------------------------------------------------------
+
+def decompose_and_plot(
+    dataset_name: str,
+    csv_file,
+    method: str,
+    period: int,
+    stl_seasonal: int,
+    stl_trend: int,
+    stl_robust: bool,
+):
+    """Run decomposition and return (matplotlib Figure, summary string)."""
+
+    # --- Load data --------------------------------------------------------
+    df = _load_dataset(dataset_name, csv_file)
+
+    if len(df) < 2 * period:
+        raise gr.Error(
+            f"Not enough observations ({len(df)}) for the chosen period ({period}). "
+            f"Need at least {2 * period} observations."
+        )
+
+    y_series = pd.Series(df["y"].values, index=df["ds"])
+
+    # --- Decompose --------------------------------------------------------
+    with warnings.catch_warnings():
+        warnings.simplefilter("ignore")
+
+        if method == "STL":
+            stl_seasonal = _ensure_odd(stl_seasonal)
+            stl_trend_val = _ensure_odd(stl_trend) if stl_trend > 0 else None
+            stl_obj = STL(
+                y_series,
+                period=int(period),
+                seasonal=stl_seasonal,
+                trend=stl_trend_val,
+                robust=bool(stl_robust),
+            )
+            result = stl_obj.fit()
+        else:
+            model_type = "additive" if "Additive" in method else "multiplicative"
+            result = seasonal_decompose(
+                y_series, model=model_type, period=int(period)
+            )
+
+    observed = result.observed
+    trend = result.trend
+    seasonal = result.seasonal
+    resid = result.resid
+
+    # --- Strength measures ------------------------------------------------
+    r = resid.values
+    t = trend.values
+    s = seasonal.values
+    mask = ~(np.isnan(r) | np.isnan(t) | np.isnan(s))
+    r_clean = r[mask]
+    t_clean = t[mask]
+    s_clean = s[mask]
+
+    f_trend = _strength(r_clean, t_clean + r_clean)
+    f_season = _strength(r_clean, s_clean + r_clean)
+
+    # --- Plot -------------------------------------------------------------
+    fig, axes = plt.subplots(4, 1, figsize=(10, 8), sharex=True)
+    fig.patch.set_facecolor("white")
+    for ax in axes:
+        ax.set_facecolor("white")
+        ax.grid(True, linewidth=0.3, alpha=0.5)
+
+    dates = observed.index
+
+    # 1. Observed
+    axes[0].plot(dates, observed, color=CLR_PRIMARY, linewidth=1.2)
+    axes[0].set_ylabel("Observed", fontsize=10, fontweight="bold")
+
+    # 2. Trend
+    axes[1].plot(dates, trend, color=CLR_TREND, linewidth=1.4)
+    axes[1].set_ylabel("Trend", fontsize=10, fontweight="bold")
+
+    # 3. Seasonal
+    axes[2].plot(dates, seasonal, color=CLR_SEASON, linewidth=1.0)
+    axes[2].set_ylabel("Seasonal", fontsize=10, fontweight="bold")
+
+    # 4. Residual
+    axes[3].plot(dates, resid, color=CLR_RESID, linewidth=0.8, alpha=0.8)
+    axes[3].set_ylabel("Remainder", fontsize=10, fontweight="bold")
+    axes[3].set_xlabel("Date", fontsize=10)
+
+    method_label = method if method == "STL" else method.replace("Classical ", "Classical – ")
+    fig.suptitle(
+        f"Decomposition  ·  {method_label}  ·  period = {period}",
+        fontsize=13,
+        fontweight="bold",
+        y=0.98,
+    )
+    fig.tight_layout(rect=[0, 0, 1, 0.96])
+
+    # --- Summary text -----------------------------------------------------
+    summary = (
+        f"Strength of Trend (F_T):        {f_trend:.4f}\n"
+        f"Strength of Seasonality (F_S):   {f_season:.4f}\n\n"
+        f"Formulas:\n"
+        f"  F_T = max(0, 1 − Var(R) / Var(T + R))\n"
+        f"  F_S = max(0, 1 − Var(R) / Var(S + R))"
+    )
+
+    return fig, summary
+
+# ---------------------------------------------------------------------------
+# Gradio UI
+# ---------------------------------------------------------------------------
+
+_THEME = gr.themes.Soft(
+    primary_hue=gr.themes.Color(
+        c50="#fef2f3", c100="#fde6e8", c200="#fbd0d5",
+        c300="#f7a4ae", c400="#f17182", c500="#C3142D",
+        c600="#b01228", c700="#8B0E1E", c800="#6e0b18",
+        c900="#5c0d17", c950="#33040a",
+    ),
+    secondary_hue=gr.themes.Color(
+        c50="#fef2f3", c100="#fde6e8", c200="#fbd0d5",
+        c300="#f7a4ae", c400="#f17182", c500="#C3142D",
+        c600="#b01228", c700="#8B0E1E", c800="#6e0b18",
+        c900="#5c0d17", c950="#33040a",
+    ),
+    neutral_hue=gr.themes.Color(
+        c50="#EDECE2", c100="#E5E4D9", c200="#DDDCD0",
+        c300="#C8C7BC", c400="#A3A299", c500="#858479",
+        c600="#6B6A61", c700="#53524B", c800="#3B3A35",
+        c900="#252420", c950="#151410",
+    ),
+    font=[gr.themes.GoogleFont("Inter"), "ui-sans-serif", "system-ui", "sans-serif"],
+)
+
+_CSS = """
+.gradio-container { max-width: 1280px !important; margin: auto; }
+footer { display: none !important; }
+.gr-button-primary { background: #C3142D !important; border: none !important; }
+.gr-button-primary:hover { background: #8B0E1E !important; }
+.gr-button-secondary { border-color: #C3142D !important; color: #C3142D !important; }
+.gr-button-secondary:hover { background: #8B0E1E !important; color: white !important; }
+.gr-input:focus { border-color: #C3142D !important; box-shadow: 0 0 0 2px rgba(195,20,45,0.2) !important; }
+"""
+
+
+def build_app() -> gr.Blocks:
+    with gr.Blocks(title="Decomposition Explorer v1.0") as app:
+        gr.HTML("""
+        <div style="display: flex; align-items: center; gap: 16px; padding: 16px 24px;
+                    background: linear-gradient(135deg, #C3142D 0%, #8B0E1E 100%);
+                    border-radius: 12px; margin-bottom: 16px; box-shadow: 0 4px 12px rgba(0,0,0,0.15);">
+            <img src="beveled-m-min-size.png"
+                 alt="Miami University" style="height: 56px; filter: brightness(0) invert(1);">
+            <div>
+                <h1 style="margin: 0; color: white; font-size: 24px; font-weight: 700; letter-spacing: -0.5px;">
+                    Decomposition Explorer v1.0
+                </h1>
+                <p style="margin: 4px 0 0; color: rgba(255,255,255,0.85); font-size: 14px;">
+                    ISA 444: Business Forecasting &middot; Farmer School of Business &middot; Miami University
+                </p>
+            </div>
+        </div>
+        """)
+
+        gr.HTML("""
+        <div style="background: #EDECE2; border-left: 4px solid #C3142D; padding: 12px 16px;
+                    border-radius: 0 8px 8px 0; margin-bottom: 16px; font-size: 14px; color: #585E60;">
+            Interactive tool for exploring time-series decomposition methods (Classical and STL).
+            Choose a built-in dataset or upload your own CSV, adjust decomposition parameters, and
+            examine trend, seasonal, and remainder components along with strength measures.
+        </div>
+        """)
+
+        with gr.Row():
+            # --- Left column: controls ------------------------------------
+            with gr.Column(scale=1, min_width=280):
+                dataset_dd = gr.Dropdown(
+                    label="Dataset",
+                    choices=list(BUILTIN_DATASETS.keys()),
+                    value="Airline Passengers",
+                )
+                csv_upload = gr.File(
+                    label="Or upload CSV (columns: ds, y)",
+                    file_types=[".csv"],
+                    type="filepath",
+                )
+                method_radio = gr.Radio(
+                    label="Decomposition Method",
+                    choices=[
+                        "Classical (Additive)",
+                        "Classical (Multiplicative)",
+                        "STL",
+                    ],
+                    value="STL",
+                )
+                period_slider = gr.Slider(
+                    label="Period / Season Length",
+                    minimum=2,
+                    maximum=52,
+                    step=1,
+                    value=12,
+                )
+
+                # STL-specific controls
+                stl_group = gr.Group(visible=True)
+                with stl_group:
+                    gr.Markdown("**STL Parameters**")
+                    stl_seasonal_slider = gr.Slider(
+                        label="seasonal (seasonality window, odd)",
+                        minimum=7,
+                        maximum=51,
+                        step=2,
+                        value=13,
+                    )
+                    stl_trend_slider = gr.Slider(
+                        label="trend (trend window, odd; 0 = auto)",
+                        minimum=0,
+                        maximum=101,
+                        step=2,
+                        value=0,
+                    )
+                    stl_robust_cb = gr.Checkbox(
+                        label="robust (robust to outliers)",
+                        value=False,
+                    )
+
+            # --- Right column: output -------------------------------------
+            with gr.Column(scale=3):
+                plot_output = gr.Plot(label="Decomposition")
+                summary_box = gr.Textbox(
+                    label="Strength Measures",
+                    lines=5,
+                    interactive=False,
+                )
+
+        # --- Visibility toggle for STL controls ---------------------------
+        def toggle_stl(method):
+            return gr.Group(visible=(method == "STL"))
+
+        method_radio.change(
+            fn=toggle_stl,
+            inputs=[method_radio],
+            outputs=[stl_group],
+        )
+
+        # --- Gather all inputs --------------------------------------------
+        all_inputs = [
+            dataset_dd,
+            csv_upload,
+            method_radio,
+            period_slider,
+            stl_seasonal_slider,
+            stl_trend_slider,
+            stl_robust_cb,
+        ]
+        all_outputs = [plot_output, summary_box]
+
+        # --- Wire change events -------------------------------------------
+        for ctrl in all_inputs:
+            ctrl.change(
+                fn=decompose_and_plot,
+                inputs=all_inputs,
+                outputs=all_outputs,
+            )
+
+        # --- Initial load -------------------------------------------------
+        app.load(
+            fn=decompose_and_plot,
+            inputs=all_inputs,
+            outputs=all_outputs,
+        )
+
+        gr.HTML("""
+        <div style="margin-top: 24px; padding: 16px; background: #EDECE2; border-radius: 8px;
+                    text-align: center; font-size: 13px; color: #585E60; border-top: 2px solid #C3142D;">
+            <div style="margin-bottom: 4px;">
+                <strong style="color: #C3142D;">Developed by</strong>
+                <a href="https://miamioh.edu/fsb/directory/?up=/directory/megahefm"
+                   style="color: #C3142D; text-decoration: none; font-weight: 600;">
+                    Fadel M. Megahed
+                </a>
+                &middot; Gloss Professor of Analytics &middot; Miami University
+            </div>
+            <div style="font-size: 12px; color: #888;">
+                Version 1.0.0 &middot; Spring 2026 &middot;
+                <a href="https://github.com/fmegahed" style="color: #C3142D; text-decoration: none;">GitHub</a> &middot;
+                <a href="https://www.linkedin.com/in/fmegahed/" style="color: #C3142D; text-decoration: none;">LinkedIn</a>
+            </div>
+        </div>
+        """)
+
+    return app
+
+
+# ---------------------------------------------------------------------------
+# Entry point
+# ---------------------------------------------------------------------------
+if __name__ == "__main__":
+    demo = build_app()
+    demo.launch(theme=_THEME, css=_CSS, ssr_mode=False, allowed_paths=["beveled-m-min-size.png"])