experiment

functions/calc_matches.py

@@ -19,7 +19,6 @@ def calc_matches(filtered_df, project_df, similarity_matrix, top_x):
 
     # Create mapping dictionaries
     filtered_df_index_map = {i: index for i, index in enumerate(filtered_df_indices)}
-    st.write(filtered_df_index_map)
     project_df_index_map = {i: index for i, index in enumerate(project_df_indices)}
 
     # Select submatrix based on indices from both dataframes
@@ -40,10 +39,12 @@ def calc_matches(filtered_df, project_df, similarity_matrix, top_x):
     top_n_row_indices = row_indices[top_n_indices]
     top_n_col_indices = col_indices[top_n_indices]
 
-    original_row_indices = filtered_df_indices[top_n_row_indices]
-    original_col_indices = project_df_indices[top_n_col_indices]
+    original_row_indices = filtered_df_index_map[top_n_row_indices]
+    original_col_indices = project_df_index_map[top_n_col_indices]
 
     st.write(match_matrix.shape)
+    st.write(original_row_indices)
+    st.write(original_col_indices)
 
     # Get the linear indices of the top 'top_x' values
     # (flattened index to handle the sparse matrix more effectively)