forgot to remove input_dataframes

splink/linker.py

@@ -1311,50 +1311,44 @@ def predict(
 
         """
 
+        pipeline = CTEPipeline()
+
         # If materialise_after_computing_term_frequencies=False and the user only
         # calls predict, it runs as a single pipeline with no materialisation
         # of anything.
 
-        # _initialise_df_concat_with_tf returns None if the table doesn't exist
-        # and only SQL is queued in this step.
-        nodes_with_tf = self._initialise_df_concat_with_tf(
-            materialise=materialise_after_computing_term_frequencies
+        pipeline = self._enqueue_df_concat_with_tf(
+            pipeline, materialise=materialise_after_computing_term_frequencies
         )
 
-        input_dataframes = []
-        if nodes_with_tf:
-            input_dataframes.append(nodes_with_tf)
-
         # If exploded blocking rules exist, we need to materialise
         # the tables of ID pairs
         exploding_br_with_id_tables = materialise_exploded_id_tables(self)
 
         sqls = block_using_rules_sqls(self)
-        for sql in sqls:
-            self._enqueue_sql(sql["sql"], sql["output_table_name"])
+        pipeline.enqueue_list_of_sqls(sqls)
 
         repartition_after_blocking = getattr(self, "repartition_after_blocking", False)
 
         # repartition after blocking only exists on the SparkLinker
         if repartition_after_blocking:
-            df_blocked = self._execute_sql_pipeline(input_dataframes)
-            input_dataframes.append(df_blocked)
+            df_blocked = self.db_api.sql_pipeline_to_splink_dataframe(pipeline)
+            pipeline.append_input_dataframe(df_blocked)
 
         sql = compute_comparison_vector_values_sql(
             self._settings_obj._columns_to_select_for_comparison_vector_values
         )
-        self._enqueue_sql(sql, "__splink__df_comparison_vectors")
+        pipeline.enqueue_sql(sql, "__splink__df_comparison_vectors")
 
         sqls = predict_from_comparison_vectors_sqls_using_settings(
             self._settings_obj,
             threshold_match_probability,
             threshold_match_weight,
             sql_infinity_expression=self._infinity_expression,
         )
-        for sql in sqls:
-            self._enqueue_sql(sql["sql"], sql["output_table_name"])
+        pipeline.enqueue_list_of_sqls(sqls)
 
-        predictions = self._execute_sql_pipeline(input_dataframes)
+        predictions = self.db_api.sql_pipeline_to_splink_dataframe(pipeline)
         self._predict_warning()
 
         [b.drop_materialised_id_pairs_dataframe() for b in exploding_br_with_id_tables]