Update inference.py

inference.py

@@ -0,0 +1,604 @@
+import numpy as np
+import pandas as pd
+import skops.io as sio
+import shap
+import plotly.graph_objects as go
+import os
+import sys
+import warnings
+
+warnings.filterwarnings("ignore", category=UserWarning, module="sklearn")
+
+# ---------------------------------------------------------------------------
+# Compatibility patch — inject _RemainderColsList if the installed sklearn
+# version does not have it (added in sklearn 1.4+). This allows .skops files
+# saved with a newer sklearn to load correctly on older environments.
+# ---------------------------------------------------------------------------
+import sklearn.compose._column_transformer as _ct
+if not hasattr(_ct, "_RemainderColsList"):
+    class _RemainderColsList(list):
+        """Minimal shim for sklearn._RemainderColsList (missing in this env)."""
+        def __init__(self, lst=None, future_dtype=None):
+            super().__init__(lst or [])
+            self.future_dtype = future_dtype
+    _ct._RemainderColsList = _RemainderColsList
+    import sklearn.compose
+    sklearn.compose._RemainderColsList = _RemainderColsList
+
+
+# ---------------------------------------------------------------------------
+# Column / feature definitions
+# ---------------------------------------------------------------------------
+
+NUM_COLUMNS = ["AGE", "NACS2YR"]
+CATEG_COLUMNS = [
+    "AGEGPFF",
+    "SEX",
+    "KPS",
+    "DONORF",
+    "GRAFTYPE",
+    "CONDGRPF",
+    "CONDGRP_FINAL",
+    "ATGF",
+    "GVHD_FINAL",
+    "HLA_FINAL",
+    "RCMVPR",
+    "EXCHTFPR",
+    "VOC2YPR",
+    "VOCFRQPR",
+    "SCATXRSN",
+]
+
+FEATURE_NAMES = NUM_COLUMNS + CATEG_COLUMNS
+
+OUTCOMES                = ["DEAD", "GF", "AGVHD", "CGVHD", "VOCPSHI", "STROKEHI", "DWOGF"]
+CLASSIFICATION_OUTCOMES = OUTCOMES
+
+REPORTING_OUTCOMES = [
+    "OS", "EFS", "GF", "DEAD",
+    "AGVHD", "CGVHD", "VOCPSHI", "STROKEHI",
+]
+
+OUTCOME_DESCRIPTIONS = {
+    "OS":       "Overall Survival",
+    "EFS":      "Event-Free Survival",
+    "DEAD":     "Total Mortality",
+    "GF":       "Graft Failure",
+    "AGVHD":    "Acute Graft-versus-Host Disease",
+    "CGVHD":    "Chronic Graft-versus-Host Disease",
+    "VOCPSHI":  "Vaso-Occlusive Crisis Post-HCT",
+    "STROKEHI": "Stroke Post-HCT",
+}
+
+SHAP_OUTCOMES = ["DEAD", "GF", "AGVHD", "CGVHD", "VOCPSHI", "STROKEHI", "OS", "EFS"]
+
+MODEL_DIR           = "."
+CONSENSUS_THRESHOLD = 0.5
+DEFAULT_N_BOOT_CI   = 500
+
+
+# ---------------------------------------------------------------------------
+# Model loading — skops
+# ---------------------------------------------------------------------------
+
+def _load_skops_model(fname):
+    try:
+        untrusted = sio.get_untrusted_types(file=fname)
+        return sio.load(fname, trusted=untrusted)
+    except Exception as e:
+        print(f"Error loading '{fname}': {e}")
+        sys.exit(1)
+
+
+preprocessor = _load_skops_model(os.path.join(MODEL_DIR, "preprocessor.skops"))
+
+classification_model_data = {}
+for _o in CLASSIFICATION_OUTCOMES:
+    _path = os.path.join(MODEL_DIR, f"ensemble_model_{_o}.skops")
+    if os.path.exists(_path):
+        classification_model_data[_o] = _load_skops_model(_path)
+    else:
+        print(f"Warning: Model for {_o} not found at {_path}. Skipping.")
+
+classification_models = {o: d["models"] for o, d in classification_model_data.items()}
+betas                 = {o: d["beta"]   for o, d in classification_model_data.items()}
+priors                = {o: d["prior"]  for o, d in classification_model_data.items()}
+consensus_thresholds  = {
+    o: d.get("consensus_threshold", CONSENSUS_THRESHOLD)
+    for o, d in classification_model_data.items()
+}
+
+# Calibrators — isotonic only; supports both old and new key names
+calibrators = {}
+for _o, _d in classification_model_data.items():
+    _cal      = None
+    _cal_type = _d.get("calibrator_type", None)
+
+    if "calibrator" in _d and _d["calibrator"] is not None:
+        if _cal_type is None or _cal_type == "isotonic":
+            _cal = _d["calibrator"]
+        else:
+            print(
+                f"Warning: outcome '{_o}' has calibrator_type='{_cal_type}'. "
+                "Skipping non-isotonic calibrator (isotonic-only policy)."
+            )
+    elif "isotonic_calibrator" in _d and _d["isotonic_calibrator"] is not None:
+        _cal = _d["isotonic_calibrator"]
+
+    calibrators[_o] = _cal
+
+# Alias expected by app.py
+isotonic_calibrators = calibrators
+
+oof_probs_calibrated = {
+    o: d.get("oof_probs_calibrated") for o, d in classification_model_data.items()
+}
+
+ohe                     = preprocessor.named_transformers_["cat"]
+ohe_feature_names       = ohe.get_feature_names_out(CATEG_COLUMNS)
+processed_feature_names = np.concatenate([NUM_COLUMNS, ohe_feature_names])
+
+
+# ---------------------------------------------------------------------------
+# SHAP background data
+# ---------------------------------------------------------------------------
+
+np.random.seed(23)
+_n_background = 500
+
+_background_data = {
+    "AGE":           np.random.uniform(5, 50, _n_background),
+    "NACS2YR":       np.random.randint(0, 5, _n_background),
+    "AGEGPFF":       np.random.choice(["<=10", "11-17", "18-29", "30-49", ">=50"], _n_background),
+    "SEX":           np.random.choice(["Male", "Female"], _n_background),
+    "KPS":           np.random.choice(["<90", "≥ 90"], _n_background),
+    "DONORF":        np.random.choice([
+                         "HLA identical sibling", "HLA mismatch relative",
+                         "Matched unrelated donor",
+                         "Mismatched unrelated donor or cord blood",
+                     ], _n_background),
+    "GRAFTYPE":      np.random.choice(["Bone marrow", "Peripheral blood", "Cord blood"], _n_background),
+    "CONDGRPF":      np.random.choice(["MAC", "RIC", "NMA"], _n_background),
+    "CONDGRP_FINAL": np.random.choice(["TBI/Cy", "Bu/Cy", "Flu/Bu", "Flu/Mel"], _n_background),
+    "ATGF":          np.random.choice(["ATG", "Alemtuzumab", "None"], _n_background),
+    "GVHD_FINAL":    np.random.choice(["CNI + MMF", "CNI + MTX", "Post-CY + siro +- MMF"], _n_background),
+    "HLA_FINAL":     np.random.choice(["8/8", "7/8", "≤ 6/8"], _n_background),
+    "RCMVPR":        np.random.choice(["Negative", "Positive"], _n_background),
+    "EXCHTFPR":      np.random.choice(["No", "Yes"], _n_background),
+    "VOC2YPR":       np.random.choice(["No", "Yes"], _n_background),
+    "VOCFRQPR":      np.random.choice(["< 3/yr", "≥ 3/yr"], _n_background),
+    "SCATXRSN":      np.random.choice([
+                         "CNS event", "Acute chest Syndrome",
+                         "Recurrent vaso-occlusive pain", "Recurrent priapism",
+                         "Excessive transfusion requirements/iron overload",
+                         "Cardio-pulmonary", "Chronic transfusion", "Asymptomatic",
+                         "Renal insufficiency", "Splenic sequestration",
+                         "Avascular necrosis", "Hodgkin lymphoma",
+                     ], _n_background),
+}
+
+_background_df = pd.DataFrame(_background_data)[FEATURE_NAMES]
+_X_background  = preprocessor.transform(_background_df)
+shap_background = shap.maskers.Independent(_X_background)
+
+
+# ---------------------------------------------------------------------------
+# Calibration helpers
+# ---------------------------------------------------------------------------
+
+def calibrate_probabilities_undersampling(p_s, beta):
+    p_s         = np.asarray(p_s, dtype=float)
+    numerator   = beta * p_s
+    denominator = np.maximum((beta - 1.0) * p_s + 1.0, 1e-10)
+    return np.clip(numerator / denominator, 0.0, 1.0)
+
+
+def predict_consensus_signed_voting(ensemble_models, X_test, threshold=0.5):
+    individual_probas = np.array(
+        [m.predict_proba(X_test)[:, 1] for m in ensemble_models]
+    )
+    binary_preds    = (individual_probas >= threshold).astype(int)
+    signed_votes    = np.where(binary_preds == 1, 1, -1)
+    avg_signed_vote = np.mean(signed_votes, axis=0)
+    consensus_pred  = (avg_signed_vote > 0).astype(int)
+    avg_proba       = np.mean(individual_probas, axis=0)
+    return consensus_pred, avg_proba, avg_signed_vote, individual_probas.flatten()
+
+
+def predict_consensus_majority(ensemble_models, X_test, threshold=0.5):
+    individual_probas = np.array(
+        [m.predict_proba(X_test)[:, 1] for m in ensemble_models]
+    )
+    avg_proba = np.mean(individual_probas, axis=0)
+    return avg_proba, individual_probas.flatten()
+
+
+# ---------------------------------------------------------------------------
+# Bootstrap CI
+# ---------------------------------------------------------------------------
+
+def bootstrap_ci_from_oof(
+    point_estimate: float,
+    oof_probs: np.ndarray,
+    n_boot: int = DEFAULT_N_BOOT_CI,
+    confidence: float = 0.95,
+    random_state: int = 42,
+) -> tuple:
+    if oof_probs is None or len(oof_probs) == 0:
+        return float(point_estimate), float(point_estimate)
+
+    oof_probs  = np.asarray(oof_probs, dtype=float)
+    rng        = np.random.RandomState(random_state)
+    grand_mean = np.mean(oof_probs)
+    n          = len(oof_probs)
+
+    boot_means = np.array([
+        np.mean(rng.choice(oof_probs, size=n, replace=True))
+        for _ in range(n_boot)
+    ])
+
+    shift      = point_estimate - grand_mean
+    boot_means = boot_means + shift
+
+    alpha = 1.0 - confidence
+    lo = float(np.clip(np.percentile(boot_means, 100 * alpha / 2),       0.0, 1.0))
+    hi = float(np.clip(np.percentile(boot_means, 100 * (1 - alpha / 2)), 0.0, 1.0))
+    return lo, hi
+
+
+# ---------------------------------------------------------------------------
+# Calibration dispatch
+# ---------------------------------------------------------------------------
+
+def _calibrate_point(outcome: str, raw_prob: float, use_calibration: bool) -> float:
+    beta   = betas[outcome]
+    p_beta = float(calibrate_probabilities_undersampling([raw_prob], beta)[0])
+
+    if not use_calibration:
+        return p_beta
+
+    cal = calibrators.get(outcome)
+    if cal is None:
+        return p_beta
+
+    return float(cal.transform([p_beta])[0])
+
+
+# ---------------------------------------------------------------------------
+# Main prediction functions
+# ---------------------------------------------------------------------------
+
+def predict_all_outcomes(
+    user_inputs,
+    use_calibration: bool = True,
+    use_signed_voting: bool = True,
+    n_boot_ci: int = DEFAULT_N_BOOT_CI,
+):
+    if isinstance(user_inputs, dict):
+        input_df = pd.DataFrame([user_inputs])
+    else:
+        input_df = pd.DataFrame([user_inputs], columns=FEATURE_NAMES)
+
+    input_df = input_df[FEATURE_NAMES]
+    X        = preprocessor.transform(input_df)
+
+    probs, intervals = {}, {}
+
+    for o in CLASSIFICATION_OUTCOMES:
+        if o not in classification_models:
+            continue
+
+        threshold = consensus_thresholds.get(o, CONSENSUS_THRESHOLD)
+
+        if use_signed_voting:
+            _, uncalib_arr, _, _ = predict_consensus_signed_voting(
+                classification_models[o], X, threshold
+            )
+        else:
+            uncalib_arr, _ = predict_consensus_majority(
+                classification_models[o], X, threshold
+            )
+
+        raw_prob   = float(uncalib_arr[0])
+        event_prob = _calibrate_point(o, raw_prob, use_calibration)
+
+        lo, hi = bootstrap_ci_from_oof(
+            point_estimate=event_prob,
+            oof_probs=oof_probs_calibrated.get(o),
+            n_boot=n_boot_ci,
+        )
+
+        probs[o]     = event_prob
+        intervals[o] = (lo, hi)
+
+    # OS = 1 - P(DEAD)
+    if "DEAD" in probs:
+        p_dead      = probs["DEAD"]
+        probs["OS"] = float(1.0 - p_dead)
+
+        dead_lo, dead_hi = intervals["DEAD"]
+        intervals["OS"]  = (
+            float(np.clip(1.0 - dead_hi, 0, 1)),
+            float(np.clip(1.0 - dead_lo, 0, 1)),
+        )
+
+    # EFS = 1 - P(DWOGF) - P(GF)
+    if "DWOGF" in probs and "GF" in probs:
+        p_dwogf      = probs["DWOGF"]
+        p_gf         = probs["GF"]
+        probs["EFS"] = float(np.clip(1.0 - p_dwogf - p_gf, 0.0, 1.0))
+
+        oof_dwogf = oof_probs_calibrated.get("DWOGF")
+        oof_gf    = oof_probs_calibrated.get("GF")
+
+        if oof_dwogf is not None and oof_gf is not None:
+            oof_dwogf = np.asarray(oof_dwogf, dtype=float)
+            oof_gf    = np.asarray(oof_gf,    dtype=float)
+            n_min     = min(len(oof_dwogf), len(oof_gf))
+            oof_dwogf = oof_dwogf[:n_min]
+            oof_gf    = oof_gf[:n_min]
+
+            rng         = np.random.RandomState(42)
+            grand_dwogf = np.mean(oof_dwogf)
+            grand_gf    = np.mean(oof_gf)
+            shift_dwogf = p_dwogf - grand_dwogf
+            shift_gf    = p_gf    - grand_gf
+
+            efs_boot = np.array([
+                np.clip(
+                    1.0
+                    - (np.mean(rng.choice(oof_dwogf, size=n_min, replace=True)) + shift_dwogf)
+                    - (np.mean(rng.choice(oof_gf,    size=n_min, replace=True)) + shift_gf),
+                    0.0, 1.0,
+                )
+                for _ in range(n_boot_ci)
+            ])
+            efs_lo           = float(np.percentile(efs_boot, 2.5))
+            efs_hi           = float(np.percentile(efs_boot, 97.5))
+            intervals["EFS"] = (efs_lo, efs_hi)
+        else:
+            intervals["EFS"] = (probs["EFS"], probs["EFS"])
+
+    return probs, intervals
+
+
+def predict_with_comparison(user_inputs, n_boot_ci: int = DEFAULT_N_BOOT_CI):
+    cal_probs,   cal_intervals   = predict_all_outcomes(user_inputs, True,  True, n_boot_ci)
+    uncal_probs, uncal_intervals = predict_all_outcomes(user_inputs, False, True, n_boot_ci)
+    return (cal_probs, cal_intervals), (uncal_probs, uncal_intervals)
+
+
+# ---------------------------------------------------------------------------
+# SHAP helpers
+# ---------------------------------------------------------------------------
+
+def _get_shap_values_for_model_outcome(user_inputs, model_outcome, invert, X_proc):
+    """Return per-model SHAP values (shape: n_models × n_processed_features)."""
+    all_model_shap_vals = []
+    for rf_model in classification_models[model_outcome]:
+        explainer = shap.TreeExplainer(rf_model, model_output="probability", data=shap_background)
+        shap_vals = explainer.shap_values(X_proc)
+
+        if isinstance(shap_vals, list):
+            shap_vals = shap_vals[1]
+        elif shap_vals.ndim == 3 and shap_vals.shape[2] == 2:
+            shap_vals = shap_vals[:, :, 1]
+
+        sv = shap_vals[0]
+        if invert:
+            sv = -sv
+        all_model_shap_vals.append(sv)
+
+    return np.array(all_model_shap_vals)
+
+
+def compute_shap_values_with_direction(user_inputs, outcome, max_display=10):
+    if isinstance(user_inputs, dict):
+        input_df = pd.DataFrame([user_inputs])
+    else:
+        input_df = pd.DataFrame([user_inputs], columns=FEATURE_NAMES)
+
+    X_proc = preprocessor.transform(input_df)
+
+    processed_to_orig = {f: f for f in NUM_COLUMNS}
+    for pf in ohe_feature_names:
+        processed_to_orig[pf] = pf.split("_", 1)[0]
+
+    if outcome == "OS":
+        raw_shap = _get_shap_values_for_model_outcome(user_inputs, "DEAD", invert=True, X_proc=X_proc)
+    elif outcome == "EFS":
+        shap_dwogf = _get_shap_values_for_model_outcome(user_inputs, "DWOGF", invert=True, X_proc=X_proc)
+        shap_gf    = _get_shap_values_for_model_outcome(user_inputs, "GF",    invert=True, X_proc=X_proc)
+        raw_shap   = np.concatenate([shap_dwogf, shap_gf], axis=0)
+    else:
+        raw_shap = _get_shap_values_for_model_outcome(user_inputs, outcome, invert=False, X_proc=X_proc)
+
+    unique_orig_features = list(dict.fromkeys(processed_to_orig.values()))
+    n_models             = len(raw_shap)
+
+    model_shap_by_orig = np.zeros((n_models, len(unique_orig_features)))
+    for model_idx in range(n_models):
+        agg_by_orig = {}
+        for i, pf in enumerate(processed_feature_names):
+            orig = processed_to_orig[pf]
+            agg_by_orig.setdefault(orig, 0.0)
+            agg_by_orig[orig] += raw_shap[model_idx, i]
+        for feat_idx, feat_name in enumerate(unique_orig_features):
+            model_shap_by_orig[model_idx, feat_idx] = agg_by_orig.get(feat_name, 0.0)
+
+    mean_shap_vals = np.mean(model_shap_by_orig, axis=0)
+
+    rng                  = np.random.RandomState(42)
+    bootstrap_shap_means = np.array([
+        np.mean(model_shap_by_orig[rng.choice(n_models, size=n_models, replace=True)], axis=0)
+        for _ in range(DEFAULT_N_BOOT_CI)
+    ])
+    shap_ci_low  = np.percentile(bootstrap_shap_means, 2.5,  axis=0)
+    shap_ci_high = np.percentile(bootstrap_shap_means, 97.5, axis=0)
+
+    order = np.argsort(-np.abs(mean_shap_vals))
+
+    top_feat_names = []
+    for i in order[:max_display]:
+        feat_name = unique_orig_features[i]
+        if feat_name in user_inputs:
+            val = user_inputs[feat_name]
+            if isinstance(val, float) and val != int(val):
+                display_name = f"{feat_name} = {val:.2f}"
+            elif isinstance(val, (int, float)):
+                display_name = f"{feat_name} = {int(val)}"
+            else:
+                val_str = str(val)
+                if len(val_str) > 20:
+                    val_str = val_str[:17] + "..."
+                display_name = f"{feat_name} = {val_str}"
+        else:
+            display_name = feat_name
+        top_feat_names.append(display_name)
+
+    top_feat_names = top_feat_names[::-1]
+    top_shap_vals  = mean_shap_vals[order][:max_display][::-1]
+    top_ci_low     = shap_ci_low[order][:max_display][::-1]
+    top_ci_high    = shap_ci_high[order][:max_display][::-1]
+
+    return top_feat_names, top_shap_vals, top_ci_low, top_ci_high
+
+
+def create_shap_plot(user_inputs, outcome, max_display=10):
+    feat_names, shap_vals, ci_low, ci_high = compute_shap_values_with_direction(
+        user_inputs, outcome, max_display
+    )
+
+    colors      = ["blue" if v >= 0 else "red" for v in shap_vals]
+    error_minus = shap_vals - ci_low
+    error_plus  = ci_high - shap_vals
+
+    fig = go.Figure()
+    fig.add_trace(go.Bar(
+        y=feat_names,
+        x=shap_vals,
+        orientation="h",
+        marker=dict(color=colors),
+        showlegend=False,
+        error_x=dict(
+            type="data",
+            symmetric=False,
+            array=error_plus,
+            arrayminus=error_minus,
+            color="gray",
+            thickness=1.5,
+            width=4,
+        ),
+    ))
+    fig.add_vline(x=0, line_width=1, line_color="black")
+
+    fig.update_layout(
+        title=dict(
+            text=OUTCOME_DESCRIPTIONS.get(outcome, outcome),
+            x=0.5, xanchor="center",
+            font=dict(size=14, color="black"),
+        ),
+        xaxis_title="SHAP value",
+        yaxis_title="",
+        height=400,
+        margin=dict(l=120, r=60, t=50, b=50),
+        plot_bgcolor="white",
+        paper_bgcolor="white",
+        xaxis=dict(showgrid=True, gridcolor="lightgray", zeroline=True,
+                   zerolinecolor="black", zerolinewidth=1),
+        yaxis=dict(showgrid=False),
+    )
+    return fig
+
+
+def create_all_shap_plots(user_inputs, max_display=10):
+    return {o: create_shap_plot(user_inputs, o, max_display) for o in SHAP_OUTCOMES}
+
+
+
+def icon_array(probability, outcome):
+    outcome_labels = {
+        "DEAD":     ("Death",           "Overall Survival"),
+        "GF":       ("Graft Failure",   "No Graft Failure"),
+        "AGVHD":    ("AGVHD",           "No AGVHD"),
+        "CGVHD":    ("CGVHD",           "No CGVHD"),
+        "VOCPSHI":  ("VOC Post-HCT",    "No VOC Post-HCT"),
+        "STROKEHI": ("Stroke Post-HCT", "No Stroke Post-HCT"),
+    }
+
+    event_label, no_event_label = outcome_labels.get(outcome, ("Event", "No Event"))
+    n_total    = 100
+    n_event    = round(probability * n_total)
+    n_no_event = n_total - n_event
+    cols, rows = 10, 10
+
+    shapes = []
+    icon_idx = 0
+
+    for row in range(rows - 1, -1, -1):   # top → bottom
+        for col in range(cols):            # left → right
+            color = "#ff6b6b" if icon_idx < n_event else "#4ecdc4"
+            x0 = col * 1.2
+            y0 = row * 1.6
+
+            # --- head (circle) ---
+            cx, cy_head, hr = x0 + 0.5, y0 + 1.35, 0.22
+            shapes.append(dict(
+                type="circle", xref="x", yref="y",
+                x0=cx - hr, y0=cy_head - hr,
+                x1=cx + hr, y1=cy_head + hr,
+                fillcolor=color, line=dict(color=color, width=0),
+            ))
+
+            # --- body (pentagon-ish path) ---
+            shapes.append(dict(
+                type="path", xref="x", yref="y",
+                path=(
+                    f"M {x0+0.18},{y0+1.10} "
+                    f"L {x0+0.82},{y0+1.10} "
+                    f"L {x0+0.90},{y0+0.55} "
+                    f"L {x0+0.60},{y0+0.55} "
+                    f"L {x0+0.60},{y0+0.0} "
+                    f"L {x0+0.40},{y0+0.0} "
+                    f"L {x0+0.40},{y0+0.55} "
+                    f"L {x0+0.10},{y0+0.55} Z"
+                ),
+                fillcolor=color, line=dict(color=color, width=0),
+            ))
+            icon_idx += 1
+
+    fig = go.Figure()
+    fig.update_layout(
+        title=dict(
+            text=(
+                f"<b>{OUTCOME_DESCRIPTIONS.get(outcome, outcome)}</b><br>"
+                f"<span style='font-size:12px;color:#ff6b6b'>■ {event_label}: {n_event}%</span>"
+                f"&nbsp;&nbsp;"
+                f"<span style='font-size:12px;color:#4ecdc4'>■ {no_event_label}: {n_no_event}%</span>"
+            ),
+            x=0.5, xanchor="center",
+            font=dict(size=14, color="black"),
+        ),
+        shapes=shapes,
+        xaxis=dict(
+            range=[-0.3, cols * 1.2 + 0.1],
+            showgrid=False, zeroline=False, showticklabels=False,
+        ),
+        yaxis=dict(
+            range=[-0.3, rows * 1.6 + 0.3],
+            showgrid=False, zeroline=False, showticklabels=False,
+            scaleanchor="x", scaleratio=1,
+        ),
+        height=460,
+        width=430,
+        margin=dict(l=10, r=10, t=90, b=10),
+        plot_bgcolor="white",
+        paper_bgcolor="white",
+    )
+    return fig
+
+
+
+
+
+