text: import numpy as np
  import plotly.graph_objects as go
  
  
  def visualize_distribution(topic_model,
                             probabilities: np.ndarray,
                             min_probability: float = 0.015,
                             width: int = 800,
                             height: int = 600) -> go.Figure:
      """ Visualize the distribution of topic probabilities
  
      Arguments:
          topic_model: A fitted BERTopic instance.
          probabilities: An array of probability scores
          min_probability: The minimum probability score to visualize.
                           All others are ignored.
          width: The width of the figure.
          height: The height of the figure.
  
      Usage:
  
      Make sure to fit the model before and only input the
      probabilities of a single document:
  
      ```python
      topic_model.visualize_distribution(probabilities[0])
      ```
  
      Or if you want to save the resulting figure:
  
      ```python
      fig = topic_model.visualize_distribution(probabilities[0])
      fig.write_html("path/to/file.html")
      ```
      <iframe src="../../tutorial/visualization/probabilities.html"
      style="width:1000px; height: 500px; border: 0px;""></iframe>
      """
      if len(probabilities.shape) != 1:
          raise ValueError("This visualization cannot be used if you have set `calculate_probabilities` to False "
                           "as it uses the topic probabilities of all topics. ")
      if len(probabilities[probabilities > min_probability]) == 0:
          raise ValueError("There are no values where `min_probability` is higher than the "
                           "probabilities that were supplied. Lower `min_probability` to prevent this error.")
      if not topic_model.calculate_probabilities:
          raise ValueError("This visualization cannot be used if you have set `calculate_probabilities` to False "
                           "as it uses the topic probabilities. ")
  
      # Get values and indices equal or exceed the minimum probability
      labels_idx = np.argwhere(probabilities >= min_probability).flatten()
      vals = probabilities[labels_idx].tolist()
  
      # Create labels
      labels = []
      for idx in labels_idx:
          words = topic_model.get_topic(idx)
          if words:
              label = [word[0] for word in words[:5]]
              label = f"<b>Topic {idx}</b>: {'_'.join(label)}"
              label = label[:40] + "..." if len(label) > 40 else label
              labels.append(label)
          else:
              vals.remove(probabilities[idx])
  
      # Create Figure
      fig = go.Figure(go.Bar(
          x=vals,
          y=labels,
          marker=dict(
              color='#C8D2D7',
              line=dict(
                  color='#6E8484',
                  width=1),
          ),
          orientation='h')
      )
  
      fig.update_layout(
          xaxis_title="Probability",
          title={
              'text': "<b>Topic Probability Distribution",
              'y': .95,
              'x': 0.5,
              'xanchor': 'center',
              'yanchor': 'top',
              'font': dict(
                  size=22,
                  color="Black")
          },
          template="simple_white",
          width=width,
          height=height,
          hoverlabel=dict(
              bgcolor="white",
              font_size=16,
              font_family="Rockwell"
          ),
      )
  
      return fig
  