Allow execution without plotly

Makefile

@@ -1,6 +1,13 @@
 test:
 	pytest
 
+test-no-plotly:
+	uv sync --extra test
+	uv pip uninstall plotly
+	pytest tests/test_other.py -k plotly --pdb
+	uv sync --extra test
+	pytest tests/test_other.py -k plotly
+
 coverage:
 	pytest --cov
 

bertopic/_bertopic.py

@@ -26,6 +26,7 @@
 from collections import defaultdict, Counter
 from scipy.sparse import csr_matrix
 from scipy.cluster import hierarchy as sch
+from importlib.util import find_spec
 
 # Typing
 import sys
@@ -34,7 +35,21 @@
     from typing import Literal
 else:
     from typing_extensions import Literal
-from typing import List, Tuple, Union, Mapping, Any, Callable, Iterable
+from typing import List, Tuple, Union, Mapping, Any, Callable, Iterable, TYPE_CHECKING
+
+# Plotting
+if find_spec("plotly") is None:
+    from bertopic._utils import MockPlotlyModule
+
+    plotting = MockPlotlyModule()
+
+else:
+    from bertopic import plotting
+
+    if TYPE_CHECKING:
+        import plotly.graph_objs as go
+        import matplotlib.figure as fig
+
 
 # Models
 try:
@@ -53,7 +68,6 @@
 from sklearn.feature_extraction.text import CountVectorizer, TfidfTransformer
 
 # BERTopic
-from bertopic import plotting
 from bertopic.cluster import BaseCluster
 from bertopic.backend import BaseEmbedder
 from bertopic.representation._mmr import mmr
@@ -73,9 +87,6 @@
 )
 import bertopic._save_utils as save_utils
 
-# Visualization
-import plotly.graph_objects as go
-
 logger = MyLogger()
 logger.configure("WARNING")
 
@@ -2405,7 +2416,7 @@ def visualize_topics(
         title: str = "<b>Intertopic Distance Map</b>",
         width: int = 650,
         height: int = 650,
-    ) -> go.Figure:
+    ) -> "go.Figure":
         """Visualize topics, their sizes, and their corresponding words.
 
         This visualization is highly inspired by LDAvis, a great visualization
@@ -2463,7 +2474,7 @@ def visualize_documents(
         title: str = "<b>Documents and Topics</b>",
         width: int = 1200,
         height: int = 750,
-    ) -> go.Figure:
+    ) -> "go.Figure":
         """Visualize documents and their topics in 2D.
 
         Arguments:
@@ -2565,7 +2576,7 @@ def visualize_document_datamap(
         topic_prefix: bool = False,
         datamap_kwds: dict = {},
         int_datamap_kwds: dict = {},
-    ):
+    ) -> "fig.Figure":
         """Visualize documents and their topics in 2D as a static plot for publication using
         DataMapPlot. This works best if there are between 5 and 60 topics. It is therefore best
         to use a sufficiently large `min_topic_size` or set `nr_topics` when building the model.
@@ -2676,7 +2687,7 @@ def visualize_hierarchical_documents(
         title: str = "<b>Hierarchical Documents and Topics</b>",
         width: int = 1200,
         height: int = 750,
-    ) -> go.Figure:
+    ) -> "go.Figure":
         """Visualize documents and their topics in 2D at different levels of hierarchy.
 
         Arguments:
@@ -2788,7 +2799,7 @@ def visualize_term_rank(
         title: str = "<b>Term score decline per Topic</b>",
         width: int = 800,
         height: int = 500,
-    ) -> go.Figure:
+    ) -> "go.Figure":
         """Visualize the ranks of all terms across all topics.
 
         Each topic is represented by a set of words. These words, however,
@@ -2853,7 +2864,7 @@ def visualize_topics_over_time(
         title: str = "<b>Topics over Time</b>",
         width: int = 1250,
         height: int = 450,
-    ) -> go.Figure:
+    ) -> "go.Figure":
         """Visualize topics over time.
 
         Arguments:
@@ -2909,7 +2920,7 @@ def visualize_topics_per_class(
         title: str = "<b>Topics per Class</b>",
         width: int = 1250,
         height: int = 900,
-    ) -> go.Figure:
+    ) -> "go.Figure":
         """Visualize topics per class.
 
         Arguments:
@@ -2963,7 +2974,7 @@ def visualize_distribution(
         title: str = "<b>Topic Probability Distribution</b>",
         width: int = 800,
         height: int = 600,
-    ) -> go.Figure:
+    ) -> "go.Figure":
         """Visualize the distribution of topic probabilities.
 
         Arguments:
@@ -3070,7 +3081,7 @@ def visualize_hierarchy(
         linkage_function: Callable[[csr_matrix], np.ndarray] = None,
         distance_function: Callable[[csr_matrix], csr_matrix] = None,
         color_threshold: int = 1,
-    ) -> go.Figure:
+    ) -> "go.Figure":
         """Visualize a hierarchical structure of the topics.
 
         A ward linkage function is used to perform the
@@ -3166,7 +3177,7 @@ def visualize_heatmap(
         title: str = "<b>Similarity Matrix</b>",
         width: int = 800,
         height: int = 800,
-    ) -> go.Figure:
+    ) -> "go.Figure":
         """Visualize a heatmap of the topic's similarity matrix.
 
         Based on the cosine similarity matrix between c-TF-IDFs or semantic embeddings of the topics,
@@ -3226,7 +3237,7 @@ def visualize_barchart(
         width: int = 250,
         height: int = 250,
         autoscale: bool = False,
-    ) -> go.Figure:
+    ) -> "go.Figure":
         """Visualize a barchart of selected topics.
 
         Arguments:

bertopic/_utils.py

@@ -4,7 +4,7 @@
 from collections.abc import Iterable
 from scipy.sparse import csr_matrix
 from scipy.spatial.distance import squareform
-from typing import Optional, Union, Tuple
+from typing import Optional, Union, Tuple, Any
 
 
 class MyLogger:
@@ -226,3 +226,13 @@ def to_ndarray(array: Union[np.ndarray, csr_matrix]) -> np.ndarray:
             repr_, ctfidf_used = embeddings, False
 
     return to_ndarray(repr_) if output_ndarray else repr_, ctfidf_used
+
+
+class MockPlotlyModule:
+    """Mock module that raises an error when plotly functions are called."""
+
+    def __getattr__(self, name: str) -> Any:
+        def mock_function(*args, **kwargs):
+            raise ImportError(f"Plotly is required to use '{name}'. Install it with uv pip install plotly")
+
+        return mock_function

docs/getting_started/tips_and_tricks/tips_and_tricks.md

@@ -196,11 +196,11 @@ embeddings = normalize(embeddings)
 
 The default embedding model in BERTopic is one of the amazing sentence-transformers models, namely `"all-MiniLM-L6-v2"`. Although this model performs well out of the box, it typically needs a GPU to transform the documents into embeddings in a reasonable time. Moreover, the installation requires `pytorch` which often results in a rather large environment, memory-wise.
 
-Fortunately, it is possible to install BERTopic without `sentence-transformers`, `UMAP`, and/or `HDBSCAN`. This can be to reduce your docker images for inference or when you do not use `pytorch` but for instance [Model2Vec](https://github.com/MinishLab/model2vec) instead. The installation can be done as follows:
+Fortunately, it is possible to install BERTopic without `sentence-transformers`, `UMAP`, `HDBSCAN` and/or `plotly`. This can be to reduce your docker images for inference or when you do not use `pytorch` but for instance [Model2Vec](https://github.com/MinishLab/model2vec) instead. The installation can be done as follows:
 
 ```bash
 pip install --no-deps bertopic
-pip install --upgrade numpy pandas scikit-learn tqdm plotly pyyaml
+pip install --upgrade numpy pandas scikit-learn tqdm pyyaml
 ```
 
 This installs a bare-bones version of BERTopic. If you want to use UMAP and Model2Vec for instance, you'll need to first install them:

tests/test_other.py

@@ -1,4 +1,10 @@
 from bertopic import BERTopic
+from bertopic.dimensionality import BaseDimensionalityReduction
+
+try:
+    from plotly.graph_objects import Figure
+except ImportError:
+    Figure = None
 
 
 def test_load_save_model():
@@ -20,3 +26,20 @@ def test_get_params():
     assert params["n_gram_range"] == (1, 1)
     assert params["min_topic_size"] == 10
     assert params["language"] == "english"
+
+
+def test_no_plotly():
+    model = BERTopic(
+        language="Dutch",
+        embedding_model=None,
+        min_topic_size=2,
+        top_n_words=1,
+        umap_model=BaseDimensionalityReduction(),
+    )
+    model.fit(["hello", "hi", "goodbye", "goodbye", "whats up"] * 10)
+
+    try:
+        out = model.visualize_topics()
+        assert isinstance(out, Figure) if Figure else False
+    except ImportError as e:
+        assert "Plotly is required to use" in str(e)