christine version control

app.py

@@ -144,6 +144,7 @@ import networkx as nx
 import plotly.graph_objs as go
 from ast import literal_eval as make_tuple
 import random
+import math
 
 import os
 print(os.getenv('MODEL_REPO_ID'))
@@ -766,13 +767,24 @@ def create_interface():
                             feature_matches = gr.CheckboxGroup(label="Matching Features", choices=[])
                             add_button = gr.Button("Add Selected Features")
 
+                            # def search_feature_labels(search_text):
+                            #     if not search_text:
+                            #         return gr.CheckboxGroup(choices=[])
+                            #     matches = [f for f in subject_data[current_subject]['feature_analysis'] if search_text.lower() in f['label'].lower()]
+                            #     matches = sorted(matches, key=lambda x: x['pearson_correlation'], reverse=True)
+                            #     matches = [f"{f['label']} ({f['index']})" for f in matches]
+                                
+                            #     return gr.CheckboxGroup(choices=matches[:10])
+
                             def search_feature_labels(search_text):
                                 if not search_text:
                                     return gr.CheckboxGroup(choices=[])
                                 matches = [f for f in subject_data[current_subject]['feature_analysis'] if search_text.lower() in f['label'].lower()]
+                                for match in matches:
+                                    if math.isnan(match['pearson_correation']):
+                                        match['pearson_correlation'] = 0
                                 matches = sorted(matches, key=lambda x: x['pearson_correlation'], reverse=True)
                                 matches = [f"{f['label']} ({f['index']})" for f in matches]
-                                
                                 return gr.CheckboxGroup(choices=matches[:10])
 
                             feature_search.change(search_feature_labels, inputs=[feature_search], outputs=[feature_matches])
@@ -899,13 +911,24 @@ def create_interface():
                        
                         
 
+                        # def search_feature_labels(search_text, current_subject):
+                        #     if not search_text:
+                        #         return gr.CheckboxGroup(choices=[])
+                        #     matches = [f for f in subject_data[current_subject]['feature_analysis'] if search_text.lower() in f['label'].lower()]
+                        #     matches = sorted(matches, key=lambda x: x['pearson_correlation'], reverse=True)
+                        #     matches = [f"{f['label']} ({f['index']})" for f in matches]
+                            
+                        #     return gr.CheckboxGroup(choices=matches[:10])
+
                         def search_feature_labels(search_text, current_subject):
                             if not search_text:
                                 return gr.CheckboxGroup(choices=[])
                             matches = [f for f in subject_data[current_subject]['feature_analysis'] if search_text.lower() in f['label'].lower()]
+                            for match in matches:
+                                if math.isnan(match['pearson_correation']):
+                                    match['pearson_correlation'] = 0
                             matches = sorted(matches, key=lambda x: x['pearson_correlation'], reverse=True)
                             matches = [f"{f['label']} ({f['index']})" for f in matches]
-                            
                             return gr.CheckboxGroup(choices=matches[:10])
 
                         feature_search.change(search_feature_labels, inputs=[feature_search, subject], outputs=[feature_matches])
@@ -947,11 +970,25 @@ def create_interface():
 
                         # family_info = gr.Markdown()
 
+                        # def search_feature_families(search_text, current_subject):
+                        #     family_analysis = subject_data[current_subject]['family_analysis']
+                        #     if not search_text:
+                        #         return gr.CheckboxGroup(choices=[])
+                        #     matches = [family for family in family_analysis if search_text.lower() in family['superfeature'].lower()]
+                            
+                        #     matches = sorted(matches, key=lambda x: x['family_pearson'], reverse=True)
+                        #     matches = [family['superfeature'] for family in matches]
+                        #     matches = list(dict.fromkeys(matches))
+                        #     return gr.CheckboxGroup(choices=matches[:10])  # Limit to top 10 matches
+
                         def search_feature_families(search_text, current_subject):
                             family_analysis = subject_data[current_subject]['family_analysis']
                             if not search_text:
                                 return gr.CheckboxGroup(choices=[])
                             matches = [family for family in family_analysis if search_text.lower() in family['superfeature'].lower()]
+                            for family in matches:
+                                if math.isnan(family['family_pearson']):
+                                    family['family_pearson'] = 0
                             matches = sorted(matches, key=lambda x: x['family_pearson'], reverse=True)
                             matches = [family['superfeature'] for family in matches]
                             matches = list(dict.fromkeys(matches))