Removed unnecessary parameter

ucsinfer/__main__.py

@@ -3,7 +3,6 @@ import sys
 import csv
 import logging
 
-from sentence_transformers import SentenceTransformer
 import tqdm
 import click
 from tabulate import tabulate, SEPARATING_LINE
@@ -59,8 +58,7 @@ def recommend(text, paths, model, interactive, skip_ucs):
     the synonyms an explanations definied for each UCS subcategory. A list 
     of ranked subcategories is printed to the terminal for each PATH.
     """
-    m = SentenceTransformer(model)
-    ctx = InferenceContext(m, model)
+    ctx = InferenceContext(model)
 
     if text is not None:
         print_recommendation(None, text,  ctx, interactive_rename=False)
@@ -177,8 +175,7 @@ def evaluate(dataset, offset, limit, model, no_foley):
     classified according to their subject and not wether or not they were
     foley, and so these categories can be excluded with the --no-foley option.
     """
-    m = SentenceTransformer(model)
-    ctx = InferenceContext(m, model)
+    ctx = InferenceContext(model)
     reader = csv.reader(dataset)
 
     print(f"Evaluating model {model}...")

ucsinfer/inference.py

@@ -54,8 +54,8 @@ class InferenceContext:
     model: SentenceTransformer
     model_name: str
 
-    def __init__(self, model: SentenceTransformer, model_name: str):
-        self.model = model
+    def __init__(self, model_name: str):
+        self.model = SentenceTransformer(model_name)
         self.model_name = model_name
 
     @cached_property
@@ -64,9 +64,12 @@ class InferenceContext:
 
     @cached_property
     def embeddings(self) -> list[dict]:
-        cache_dir = platformdirs.user_cache_dir("us.squad51.ucsinfer", 'Squad 51')
-        embedding_cache = os.path.join(cache_dir, 
+        cache_dir = platformdirs.user_cache_dir("us.squad51.ucsinfer", 
+                                                'Squad 51')
+        embedding_cache = os.path.join(
+                cache_dir,                        
                 f"{self.model_name}-ucs_embedding.cache")
+
         embeddings = []
 
         if os.path.exists(embedding_cache):