Update pages/market_rent_estimation.py

pages/market_rent_estimation.py

@@ -47,7 +47,7 @@ def main():
     filltered_rows_embeding = distances_matrix[mask]
     selected_row_embeding = distances_matrix[row_index]
     distances_to_properties = np.linalg.norm(filltered_rows_embeding - selected_row_embeding, axis=1)
-    df_properties_filtered["score_pred"] = distances_to_properties
+    df_properties_filtered["Similarity score"] = distances_to_properties
     reordered_df_properties_filtered = df_properties_filtered.sort_values("score_pred", ascending=False)
     
     df_properties_filtered = df_properties_filtered.reset_index()
@@ -57,9 +57,9 @@ def main():
     #comps page
     with tab1:
         filtered_data = reordered_df_properties_filtered[["google_ola", "market_costar", "submarket_costar", 'distance_from_first (km)', "execution_date", "rented_sf", "building_sf", "year_built", "office_rate", "min_clear_height", "max_clear_height", "docks", "drive_ins", "rent_combined", "lat", "long"]]#pd.concat([filtered_data2])
-        comps_scores = sorted_distances
+        # comps_scores = sorted_distances
 
-        filtered_data.insert(loc=1, column='Similarity score', value=comps_scores)
+        # filtered_data.insert(loc=1, column='Similarity score', value=comps_scores)
 
         # Formatting the DataFrame
         filtered_data['Similarity score'] = ((1 - filtered_data['Similarity score']) * 100).apply(lambda x: f"{x:.2f}")