Cannot interpret SVM model using Shapash - python-3.x

Question

Currently, I'm exploring machine learning interpretability tools for one of my project. I found Shapash quite a new tool and many people suggesting to use it to create a few easily interpretable charts for ML model. When I tried it with RandomForestClassifier it worked fine and generate a webpage full of different charts but the same I cannot achieve while using SVM(just exploring this library, not focusing on the perfect ML model for a problem).
Note - using Shapash link here
#Fit blackbox model
svc = svm.SVC()
svc.fit(X_train_smote, y_train_smote)
y_pred = svc.predict(X_test)
print(f"F1 Score {f1_score(y_test, y_pred, average='macro')}")
print(f"Accuracy {accuracy_score(y_test, y_pred)}")
from shapash import SmartExplainer
xpl = SmartExplainer(model=svc)
error which I'm getting -
---------------------------------------------------------------------------
Exception Traceback (most recent call last)
/tmp/ipykernel_13648/1233939729.py in <module>
----> 1 xpl = SmartExplainer(model=svc)
~/Python_AI/ai_env/lib/python3.8/site-packages/shapash/explainer/smart_explainer.py in __init__(self, model, backend, preprocessing, postprocessing, features_groups, features_dict, label_dict, title_story, palette_name, colors_dict, **kwargs)
194 if isinstance(backend, str):
195 backend_cls = get_backend_cls_from_name(backend)
--> 196 self.backend = backend_cls(
197 model=self.model, preprocessing=preprocessing, **kwargs)
198 elif isinstance(backend, BaseBackend):
~/Python_AI/ai_env/lib/python3.8/site-packages/shapash/backend/shap_backend.py in __init__(self, model, preprocessing, explainer_args, explainer_compute_args)
16 self.explainer_args = explainer_args if explainer_args else {}
17 self.explainer_compute_args = explainer_compute_args if explainer_compute_args else {}
---> 18 self.explainer = shap.Explainer(model=model, **self.explainer_args)
19
20 def run_explainer(self, x: pd.DataFrame) -> dict:
~/Python_AI/ai_env/lib/python3.8/site-packages/shap/explainers/_explainer.py in __init__(self, model, masker, link, algorithm, output_names, feature_names, **kwargs)
166 # if we get here then we don't know how to handle what was given to us
167 else:
--> 168 raise Exception("The passed model is not callable and cannot be analyzed directly with the given masker! Model: " + str(model))
169
170 # build the right subclass
Exception: The passed model is not callable and cannot be analyzed directly with the given masker! Model: SVC()