Spark SQL - Replace schema/table based on input parameters

I have a simple PySpark code on Databricks which reads the data from a bucket, few minor transformations and writes to delta table.

Currently following below steps. I have hardcoded catalog/schema/table names for now. I need to dynamically replace catalog/schema/table based on parameters. With PySpark, we can easily replace values, is there anyway we can replace values for <catalog_name>.<schema_name>.<table_name> used in final SQL insert, based on input parameters?

1. Read input data and create dataframe
2. basic transformation
3. create temp table from dataframe using createOrReplaceTempView()
4. write to delta table as below

df = spark.read.option("header", "true") .schema(schema_name).csv(source_path)

df.createOrReplaceTempView(temp_table)

# how can we replace the names based on input parameter/variable
insert into <"name_of_catalog">.<"name_of_schema">.<"name_of_table">
select * from temp_table;