reverse

app.py

@@ -148,7 +148,6 @@ def main():
                     else:
                         st.warning("unsaved embeddings will be lost.")
         else:
-
             vec_store = setup_db.load_vectorstore_from_excel("data/KBOB_Klassifizierung.xlsx")
             sst.page = "home"
             file = st.file_uploader("upload file", accept_multiple_files=False)
@@ -185,7 +184,8 @@ def main():
                         sorted_vec_table = my_2_sim_search.sim_search_fly(vec_table=vec_store, term=full_search)
                         st.success("sim search")
                         st.write(f"len of list of categories {len(list(sorted_vec_table.keys()))}")
-                        for category in list(sorted_vec_table.keys())[:3]:
+                        st.write(f"the most fitting category is {sorted_vec_table.keys()}")
+                        for category in list(sorted_vec_table.keys())[0]:
                             st.write(category)
                         for category in list(sorted_vec_table.keys())[-3:]:
                             st.write(category)

my_2_sim_search.py

@@ -38,7 +38,7 @@ def sim_search_load_db(database, term, add_to_db=True, debug=False):
         sim_search_dict[key] = dp * distance
 
     # sort with the biggest similarity
-    sorted_table = dict(sorted(sim_search_dict.items(), key=lambda item: item[1]), reversed=True)
+    sorted_table = dict(sorted(sim_search_dict.items(), key=lambda item: item[1], reverse=True))
 
     if debug:
         for key, value in sorted_table[:5]:
@@ -152,7 +152,7 @@ def sim_search_fly(vec_table, term, debug=False):
         sim_search_dict[key] = dp #* distance
 
     # sort with the biggest similarity
-    sorted_table = dict(sorted(sim_search_dict.items(), key=lambda item: item[1]), reversed=True)
+    sorted_table = dict(sorted(sim_search_dict.items(), key=lambda item: item[1], reverse=True))
 
     if debug:
         for key, value in sorted_table[:5]: