adding model_name to caching

utils/interp_space_utils.py

@@ -828,7 +828,7 @@ def compute_predicted_author(task_authors_df: pd.DataFrame, col_name: str) -> in
     return predicted_author
 
 
-def compute_precomputed_regions(bg_proj, bg_ids, q_proj, c_proj, mystery_id, candidate_ids, n_neighbors=7):
+def compute_precomputed_regions(bg_proj, bg_ids, q_proj, c_proj, model_name, n_neighbors=7):
     """
     Compute precomputed regions for mystery author and candidates.
     
@@ -843,7 +843,7 @@ def compute_precomputed_regions(bg_proj, bg_ids, q_proj, c_proj, mystery_id, can
         dict: mapping region names to bounding boxes and author lists
     """
     print("Computing sugested regions for zoom...")
-    key = f"{hashlib.md5((str(q_proj.tolist()) + str(c_proj.tolist()) + str(n_neighbors)).encode()).hexdigest()}"
+    key = f"{hashlib.md5((model_name + str(q_proj.tolist()) + str(c_proj.tolist()) + str(n_neighbors)).encode()).hexdigest()}"
     
     if os.path.exists(REGION_CACHE):
         with open(REGION_CACHE, 'rb') as f:

utils/visualizations.py

@@ -519,7 +519,7 @@ def visualize_clusters_plotly(iid, cfg, instances, model_radio, custom_model_inp
     candidate_ids = task_authors_df['authorID'].iloc[1:4].tolist()  # 3 candidate IDs
 
     precomputed_regions = compute_precomputed_regions(
-        bg_proj_for_regions, bg_ids_for_regions, q_proj, c_proj, mystery_id, candidate_ids
+        bg_proj_for_regions, bg_ids_for_regions, q_proj, c_proj, model_name
     )
     
     # Create choices for radio buttons