Autopep

ucsinfer/__main__.py

@@ -52,7 +52,7 @@ def gather(paths, outfile):
 
     print(f"Found {len(scan_list)} files to process.")
 
-    for pair in tqdm.tqdm(scan_list, unit='files',file=sys.stderr):
+    for pair in tqdm.tqdm(scan_list, unit='files', file=sys.stderr):
         if desc := ffmpeg_description(pair[1]):
             table.writerow([pair[0], desc])
 
@@ -107,31 +107,31 @@ def evaluate(dataset, offset, limit):
 
     miss_counts = []
     for cat in cats:
-        miss_counts.append((cat, len([x for x in results \
-                if x['catid'] == cat and x['result'] == 'MISS'])))
+        miss_counts.append((cat, len([x for x in results
+                                      if x['catid'] == cat and x['result'] == 'MISS'])))
 
     miss_counts = sorted(miss_counts, key=lambda x: x[1])
 
     print(f" === RESULTS === ")
 
     table = [
-                ["Total records in sample:", f"{total}"],
-                ["Top Result:", f"{total_top}", 
-                 f"{float(total_top)/float(total):.2%}"],
-                ["Top 5 Result:", f"{total_top_5}", 
-                    f"{float(total_top_5)/float(total):.2%}"],
-                ["Top 10 Result:", f"{total_top_10}", 
-                    f"{float(total_top_10)/float(total):.2%}"],
-                SEPARATING_LINE,
-                ["UCS category count:", f"{len(ctx.catlist)}"],
-                ["Total categories in sample:", f"{total_cats}", 
-                    f"{float(total_cats)/float(len(ctx.catlist)):.2%}"],
-                [f"Most missed category ({miss_counts[-1][0]}):",
-                 f"{miss_counts[-1][1]}", 
-                 f"{float(miss_counts[-1][1])/float(total):.2%}"]
-            ]
+        ["Total records in sample:", f"{total}"],
+        ["Top Result:", f"{total_top}",
+         f"{float(total_top)/float(total):.2%}"],
+        ["Top 5 Result:", f"{total_top_5}",
+         f"{float(total_top_5)/float(total):.2%}"],
+        ["Top 10 Result:", f"{total_top_10}",
+         f"{float(total_top_10)/float(total):.2%}"],
+        SEPARATING_LINE,
+        ["UCS category count:", f"{len(ctx.catlist)}"],
+        ["Total categories in sample:", f"{total_cats}",
+         f"{float(total_cats)/float(len(ctx.catlist)):.2%}"],
+        [f"Most missed category ({miss_counts[-1][0]}):",
+         f"{miss_counts[-1][1]}",
+         f"{float(miss_counts[-1][1])/float(total):.2%}"]
+    ]
 
-    print(tabulate(table, headers=['','n','pct']))
+    print(tabulate(table, headers=['', 'n', 'pct']))
 
 
 if __name__ == '__main__':

ucsinfer/inference.py

@@ -11,6 +11,7 @@ import platformdirs
 
 from sentence_transformers import SentenceTransformer
 
+
 def classify_text_ranked(text, embeddings_list, model, limit=5):
     text_embedding = model.encode(text, convert_to_numpy=True)
     embeddings = np.array([info['Embedding'] for info in embeddings_list])
@@ -32,6 +33,7 @@ class Ucs(NamedTuple):
                    subcategory=d['SubCategory'],
                    explanations=d['Explanations'], synonymns=d['Synonyms'])
 
+
 def load_ucs() -> list[Ucs]:
     FILE_ROOT_DIR = os.path.dirname(os.path.abspath(__file__))
     cats = []
@@ -43,6 +45,7 @@ def load_ucs() -> list[Ucs]:
 
     return [Ucs.from_dict(cat) for cat in cats]
 
+
 class InferenceContext:
     """
     Maintains caches and resources for UCS category inference.
@@ -72,9 +75,9 @@ class InferenceContext:
 
             for cat_defn in self.catlist:
                 embeddings += [{
-                        'CatID': cat_defn.catid,
-                        'Embedding': self._encode_category(cat_defn)
-                               }]
+                    'CatID': cat_defn.catid,
+                    'Embedding': self._encode_category(cat_defn)
+                }]
 
             os.makedirs(os.path.dirname(embedding_cache), exist_ok=True)
             with open(embedding_cache, 'wb') as g:
@@ -84,9 +87,9 @@ class InferenceContext:
 
     def _encode_category(self, cat: Ucs) -> np.ndarray:
         sentence_components = [cat.explanations,
-                           cat.category, 
-                           cat.subcategory
-                           ]
+                               cat.category,
+                               cat.subcategory
+                               ]
         sentence_components += cat.synonymns
         sentence = ", ".join(sentence_components)
         return self.model.encode(sentence, convert_to_numpy=True)
@@ -108,9 +111,8 @@ class InferenceContext:
         :raises: StopIterator if CatId is not on the schedule 
         """
         i = (
-                (x.category, x.subcategory, x.explanations) \
-                        for x in self.catlist if x.catid == catid
-                        )
+            (x.category, x.subcategory, x.explanations)
+            for x in self.catlist if x.catid == catid
+        )
 
         return next(i)
-

ucsinfer/util.py

@@ -6,6 +6,7 @@ from re import match
 
 from .inference import Ucs
 
+
 def ffmpeg_description(path: str) -> Optional[str]:
     result = subprocess.run(['ffprobe', '-show_format', '-of',
                              'json', path], capture_output=True)