Notice that in your example you have

plt.figure()

This actually clears the matplotlib figure, this is why we are getting a first white image then the actual plot,
once I removed it I got a single plot (no need for the manual reporting)

X, y = make_regression(
    n_samples=100,     # Number of samples
    n_features=10,     # Number of features
    noise=0.1,   # Number of informative features
    random_state=42    # For reproducibility
)

# Convert to DataFrame for better feature naming
feature_names = [f"Feature {i+1}" for i in range(X.shape[1])]
X = pd.DataFrame(X, columns=feature_names)

# Step 2: Split Data into Train/Test Sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Step 3: Train a Simple Model
model = RandomForestRegressor(random_state=42)
model.fit(X_train, y_train)

# Step 4: Use SHAP to Explain Model Predictions
explainer = shap.Explainer(model, X_train)
shap_values = explainer(X_test)  # Returns a SHAP Explanation object in new SHAP versions

shap_plot = shap.summary_plot(shap_values, X_test)  # Show top 5 features

plt.show()