A. Download the pre-trained model from the Snowflake stage to the Streamlit application's local storage and perform the inference using a standard Python machine learning library.

B. Create a Snowflake external function (UDF) that loads the pre-trained model from the Snowflake stage and performs the inference. Call this UDF from the Streamlit application, passing the customer features as input.

C. Use Streamlit's caching capabilities Cst.cache_resource') to load the pre-trained model from the Snowflake stage into the Streamlit application's memory. Then, perform the inference directly within the Streamlit application.

D. Create a Snowflake stored procedure that loads the pre-trained model from the Snowflake stage and performs the inference using SQL commands (if the model is compatible). Call this stored procedure from the Streamlit application.

E. Implement Snowpark and load the model from the Snowflake stage, transforming and sending the data to the model with Snowpark session in Snowflake, leveraging Streamlit for visualization.