removed unused code

app.py

@@ -55,7 +55,7 @@ def validate_ground_truth(gt1, gt2, gt3):
     return index, f"Candidate {index+1} is marked as the ground truth author."
 
 
-def app(share=False, use_cluster_feats=False):
+def app(share=False):#, use_cluster_feats=False):
     instances, instance_ids = get_instances(cfg['instances_to_explain_path'])
 
     interp      = load_interp_space(cfg)
@@ -392,12 +392,12 @@ def app(share=False, use_cluster_feats=False):
         llm_style_feats_analysis = gr.State()
         visible_zoomed_authors = gr.State()
 
-        if use_cluster_feats:
-            # ── Dynamic Cluster Choice dropdown ──────────────────────────────────
-            gr.HTML(instruction_callout("Choose a cluster from the dropdown below to inspect whether its features appear in the mystery author’s text."))
-            cluster_dropdown.visible = True
-        else:
-            gr.HTML(instruction_callout("Zoom in on the plot to select a set of background authors and see the presence of the top features from this set in candidate and mystery authors."))
+        # if use_cluster_feats:
+        #     # ── Dynamic Cluster Choice dropdown ──────────────────────────────────
+        #     gr.HTML(instruction_callout("Choose a cluster from the dropdown below to inspect whether its features appear in the mystery author’s text."))
+        #     cluster_dropdown.visible = True
+        # else:
+        gr.HTML(instruction_callout("Zoom in on the plot to select a set of background authors and see the presence of the top features from this set in candidate and mystery authors."))
            
         with gr.Row():
             # ── LLM Features Column ──────────────────────────────────
@@ -448,21 +448,21 @@ def app(share=False, use_cluster_feats=False):
         )
         
         # Populate feature list based on selection. 
-        if use_cluster_feats:
-            # Use cluster-based flow
-            cluster_dropdown.change(
-                fn=on_cluster_change,
-                inputs=[cluster_dropdown, style_map_state],
-                outputs=[features_rb, gram2vec_rb , feature_list_state] 
-                #adding feature_list_state to persisit all llm features in the app state
-            )
-        else:
-
-            axis_ranges.change(
-                fn=handle_zoom_with_retries, 
-                inputs=[axis_ranges, bg_proj_state, bg_lbls_state, bg_authors_df, task_authors_embeddings_df], 
-                outputs=[features_rb, gram2vec_rb , llm_style_feats_analysis, feature_list_state, visible_zoomed_authors]
-            )
+        # if use_cluster_feats:
+        #     # Use cluster-based flow
+        #     cluster_dropdown.change(
+        #         fn=on_cluster_change,
+        #         inputs=[cluster_dropdown, style_map_state],
+        #         outputs=[features_rb, gram2vec_rb , feature_list_state] 
+        #         #adding feature_list_state to persisit all llm features in the app state
+        #     )
+        # else:
+
+        axis_ranges.change(
+            fn=handle_zoom_with_retries, 
+            inputs=[axis_ranges, bg_proj_state, bg_lbls_state, bg_authors_df, task_authors_embeddings_df], 
+            outputs=[features_rb, gram2vec_rb , llm_style_feats_analysis, feature_list_state, visible_zoomed_authors]
+        )
 
 
         # ── Show combined feature‐span highlights ──
@@ -543,6 +543,6 @@ def app(share=False, use_cluster_feats=False):
 
 if __name__ == "__main__":
     parser = argparse.ArgumentParser()
-    parser.add_argument("--use_cluster_feats", action="store_true", help="Use cluster-based selection for features")
+    # parser.add_argument("--use_cluster_feats", action="store_true", help="Use cluster-based selection for features")
     args = parser.parse_args()
-    app(share=True, use_cluster_feats=args.use_cluster_feats)
+    app(share=True)#, use_cluster_feats=args.use_cluster_feats)

utils/visualizations.py

@@ -411,33 +411,8 @@ def visualize_clusters_plotly(iid, cfg, instances, model_radio, custom_model_inp
     # split
     q_proj    = proj[0]
     c_proj    = proj[1:4]
-    #bg_proj   = proj[4:4+len(bg_lbls)]
     bg_proj   = proj
 
-    # cent_proj = proj[4+len(bg_lbls):]
-
-
-    # find nearest centroid
-    # dists = np.linalg.norm(cent_proj - q_proj, axis=1)
-    # idx   = int(np.argmin(dists))
-    # cluster_label_query = cent_lbl[idx]
-    # features of the nearest centroid to display
-    # feature_list = style_names[cluster_label_query]
-
-    # cluster_labels_per_candidate = [
-    #     cent_lbl[int(np.argmin(np.linalg.norm(cent_proj - c_proj[i], axis=1)))]
-    #     for i in range(c_proj.shape[0])
-    # ]
-
-    # prepare colorscale
-    # n_cent = len(cent_lbl)
-    # cent_colors = sample_colorscale("algae", [i/(n_cent-1) for i in range(n_cent)])
-    # map each cluster label to its color
-    # color_map = { label: cent_colors[i] for i, label in enumerate(cent_lbl) }
-
-    # uncomment the following line to show background authors
-    ## background author colors pulled from their cluster label
-    # bg_colors = [ color_map[label] for label in bg_lbls ]
 
     # 2) build Plotly figure
     fig = go.Figure()
@@ -450,13 +425,6 @@ def visualize_clusters_plotly(iid, cfg, instances, model_radio, custom_model_inp
         # Enable zoom events
         dragmode='zoom'  
     )
-    
-    # fig.update_layout(
-    #     template='plotly_white',
-    #     margin=dict(l=40,r=40,t=60,b=40),
-    #     autosize=True,
-    #     hovermode='closest')
-
 
     # uncomment the following line to show background authors
     ## background authors (light grey dots)
@@ -468,20 +436,6 @@ def visualize_clusters_plotly(iid, cfg, instances, model_radio, custom_model_inp
         hoverinfo='skip'
     ))
 
-    # centroids (rainbow colors + hovertext of your top-k features)
-    # hover_texts = [
-    #     f"Cluster {lbl}<br>" + "<br>".join(style_names[lbl])
-    #     for lbl in cent_lbl
-    # ]
-    # fig.add_trace(go.Scattergl(
-    #     x=cent_proj[:,0], y=cent_proj[:,1],
-    #     mode='markers',
-    #     marker=dict(symbol='triangle-up', size=10, color="#d3d3d3"),#color=cent_colors
-    #     name='Cluster centroids',
-    #     hovertext=hover_texts,
-    #     hoverinfo='text'
-    # ))
-
     # three candidates
     marker_syms = ['diamond','pentagon','x']
     for i in range(3):
@@ -557,25 +511,7 @@ def visualize_clusters_plotly(iid, cfg, instances, model_radio, custom_model_inp
         )
 
     print('Done processing....')
-    # Prepare outputs for the new cluster‐dropdown UI
-    # all_clusters = sorted(style_names.keys())
-    # --- build display names for the dropdown ---
-    # sorted_labels = sorted([int(lbl) for lbl in cent_lbl])
-    # display_clusters = []
-    # for lbl in sorted_labels:
-    #     name = f"Cluster {lbl}"
-    #     if lbl == cluster_label_query:
-    #         name += " (closest to mystery author)"
-    #     matching_indices = [i + 1 for i, val in enumerate(cluster_labels_per_candidate) if int(val) == lbl]
-    #     if matching_indices:
-    #         if len(matching_indices) == 1:
-    #             name += f" (closest to Candidate {matching_indices[0]} author)"
-    #         else:
-    #             candidate_str = ", ".join(f"Candidate {i}" for i in matching_indices)
-    #             name += f" (closest to {candidate_str} authors)"
-    #     display_clusters.append(name)
-    # print(f"All clusters: {all_clusters}")
-    # return: figure, dropdown payload, full style_map
+    
     return (
       fig,
     #   update(choices=display_clusters, value=display_clusters[cluster_label_query]),