DOC Autogeneration of getting started code

examples/plot_model_card.py

@@ -108,13 +108,9 @@
     " max_leaf_nodes and max_depth."
 )
 model_card_authors = "skops_user"
-get_started_code = (
-    "import pickle\nwith open(pkl_filename, 'rb') as file:\n    clf = pickle.load(file)"
-)
 citation_bibtex = "bibtex\n@inproceedings{...,year={2020}}"
 model_card.add(
     citation_bibtex=citation_bibtex,
-    get_started_code=get_started_code,
     model_card_authors=model_card_authors,
     limitations=limitations,
     model_description=model_description,

skops/card/_model_card.py

@@ -136,7 +136,7 @@ def metadata_from_config(config_path: Union[str, Path]) -> CardData:
     task = config.get("sklearn", {}).get("task", None)
     if task:
         card_data.tags += [task]
-
+    card_data.model_file = config.get("sklearn", {}).get("model", {}).get("file")
     example_input = config.get("sklearn", {}).get("example_input", None)
     # Documentation on what the widget expects:
     # https://huggingface.co/docs/hub/models-widgets-examples
@@ -380,6 +380,16 @@ def _generate_card(self) -> ModelCard:
         # add evaluation results
 
         template_sections = copy.deepcopy(self._template_sections)
+        if self.metadata:
+            model_file = self.metadata.to_dict().get("model_file")
+            if model_file:
+                template_sections["get_started_code"] = (
+                    "import joblib\nimport json\nimport pandas as pd\nclf ="
+                    f' joblib.load({model_file})\nwith open("config.json") as f:\n   '
+                    " config ="
+                    " json.load(f)\n"
+                    'clf.predict(pd.DataFrame.from_dict(config["sklearn"]["example_input"]))'
+                )
         template_sections["eval_results"] = tabulate(
             list(self._eval_results.items()),
             headers=["Metric", "Value"],

skops/card/default_template.md

@@ -41,17 +41,10 @@ You can find the details about evaluation process and the evaluation results.
 
 Use the code below to get started with the model.
 
-<details>
-<summary> Click to expand </summary>
-
 ```python
 {{ get_started_code | default("[More Information Needed]", true)}}
 ```
 
-</details>
-
-
-
 
 # Model Card Authors
 

skops/card/tests/test_card.py

@@ -32,6 +32,26 @@ def model_card(model_diagram=True):
     yield card
 
 
+@pytest.fixture
+def model_card_metadata_from_config(destination_path):
+    X, y = load_iris(return_X_y=True, as_frame=True)
+    est = LogisticRegression(solver="liblinear").fit(X, y)
+    pkl_file = tempfile.mkstemp(suffix=".pkl", prefix="skops-test")[1]
+    with open(pkl_file, "wb") as f:
+        pickle.dump(est, f)
+    hub_utils.init(
+        model=pkl_file,
+        requirements=[f"scikit-learn=={sklearn.__version__}"],
+        dst=destination_path,
+        task="tabular-classification",
+        data=X,
+    )
+    card = Card(
+        est, model_diagram=True, metadata=metadata_from_config(destination_path)
+    )
+    yield card
+
+
 @pytest.fixture
 def destination_path():
     with tempfile.TemporaryDirectory(prefix="skops-test") as dir_path:
@@ -109,24 +129,20 @@ def test_add_metrics(destination_path, model_card):
     assert ("acc" in card) and ("f1" in card) and ("0.1" in card)
 
 
-def test_metadata_from_config_tabular_data(destination_path):
+def test_code_autogeneration(destination_path, model_card_metadata_from_config):
+    # test if getting started code is automatically generated
+    model_card_metadata_from_config.save(Path(destination_path) / "README.md")
+    metadata = metadata_load(local_path=Path(destination_path) / "README.md")
+    filename = metadata["model_file"]
+    with open(Path(destination_path) / "README.md") as f:
+        assert f"joblib.load({filename})" in f.read()
+
+
+def test_metadata_from_config_tabular_data(
+    model_card_metadata_from_config, destination_path
+):
     # test if widget data is correctly set in the README
-    X, y = load_iris(return_X_y=True, as_frame=True)
-    est = LogisticRegression(solver="liblinear").fit(X, y)
-    pkl_file = tempfile.mkstemp(suffix=".pkl", prefix="skops-test")[1]
-    with open(pkl_file, "wb") as f:
-        pickle.dump(est, f)
-    hub_utils.init(
-        model=pkl_file,
-        requirements=[f"scikit-learn=={sklearn.__version__}"],
-        dst=destination_path,
-        task="tabular-classification",
-        data=X,
-    )
-    card = Card(
-        est, model_diagram=True, metadata=metadata_from_config(destination_path)
-    )
-    card.save(Path(destination_path) / "README.md")
+    model_card_metadata_from_config.save(Path(destination_path) / "README.md")
     metadata = metadata_load(local_path=Path(destination_path) / "README.md")
     assert "widget" in metadata