[pre-commit.ci] pre-commit autoupdate (#176)

* [pre-commit.ci] pre-commit autoupdate

updates:
- [github.com/psf/black: 23.12.1 → 24.1.1](psf/black@23.12.1...24.1.1)

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

---------

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

.pre-commit-config.yaml

@@ -5,7 +5,7 @@ repos:
     - id: nbstripout
       files: ".ipynb"
   - repo: https://github.com/psf/black
-    rev: 23.12.1
+    rev: 24.1.1
     hooks:
     - id: black
   - repo: https://github.com/pycqa/flake8

ammico/display.py

@@ -479,12 +479,16 @@ def _right_output_analysis(
             detector_class = identify_function(
                 image_copy,
                 analyse_text=analyse_text,
-                model_names=[settings_text_model_names]
-                if (settings_text_model_names is not None)
-                else None,
-                revision_numbers=[settings_text_revision_numbers]
-                if (settings_text_revision_numbers is not None)
-                else None,
+                model_names=(
+                    [settings_text_model_names]
+                    if (settings_text_model_names is not None)
+                    else None
+                ),
+                revision_numbers=(
+                    [settings_text_revision_numbers]
+                    if (settings_text_revision_numbers is not None)
+                    else None
+                ),
             )
         elif detector_value == "EmotionDetector":
             detector_class = identify_function(
@@ -502,9 +506,11 @@ def _right_output_analysis(
                 image_copy,
                 analysis_type=setting_summary_analysis_type,
                 model_type=setting_summary_model,
-                list_of_questions=[setting_summary_list_of_questions]
-                if (setting_summary_list_of_questions is not None)
-                else None,
+                list_of_questions=(
+                    [setting_summary_list_of_questions]
+                    if (setting_summary_list_of_questions is not None)
+                    else None
+                ),
             )
         else:
             detector_class = identify_function(image_copy)

ammico/multimodal_search.py

@@ -534,9 +534,9 @@ def multimodal_search(
                     self.subdict[image_keys[key]][
                         "rank " + list(search_query[q].values())[0]
                     ] = places[q][key]
-                    self.subdict[image_keys[key]][
-                        list(search_query[q].values())[0]
-                    ] = similarity[key][q].item()
+                    self.subdict[image_keys[key]][list(search_query[q].values())[0]] = (
+                        similarity[key][q].item()
+                    )
                 else:
                     self.subdict[image_keys[key]][
                         "rank " + list(search_query[q].values())[0]
@@ -915,9 +915,9 @@ def image_text_match_reordering(
             avg_gradcams.append(local_avg_gradcams)
             itm_scores.append(local_itm_scores)
             itm_scores2.append(local_itm_scores2)
-            image_gradcam_with_itm[
-                list(search_query[index_text_query].values())[0]
-            ] = localimage_gradcam_with_itm
+            image_gradcam_with_itm[list(search_query[index_text_query].values())[0]] = (
+                localimage_gradcam_with_itm
+            )
         del (
             itm_model,
             vis_processor_itm,

ammico/text.py

@@ -220,9 +220,11 @@ def clean_text(self):
         """Clean the text from unrecognized words and any numbers."""
         templist = []
         for token in self.doc:
-            templist.append(
-                token.text
-            ) if token.pos_ != "NUM" and token.has_vector else None
+            (
+                templist.append(token.text)
+                if token.pos_ != "NUM" and token.has_vector
+                else None
+            )
         self.subdict["text_clean"] = " ".join(templist).rstrip().lstrip()
 
     def text_summary(self):