How to do the Machine Model application in Fabric?

This is the model

here is how I supply the ML with the new inputs from lakehouse source.

# Initialize Spark session
spark = SparkSession.builder.getOrCreate()

# Load data from your Spark SQL environment or DataFrame
df = spark.sql("SELECT removal_reasons, reliability_tracked FROM lakehouse1.part_removal")

# Initialize the tokenizer
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')

# Tokenization function
def tokenize_text(text):
    tokens = tokenizer(text, padding="max_length", truncation=True, max_length=128, return_tensors="np")
    return tokens['input_ids'][0].tolist(), tokens['attention_mask'][0].tolist()

# Register UDF for tokenization
@udf(ArrayType(IntegerType()))
def udf_tokenize_input_ids(text):
    return tokenize_text(text)[0]

@udf(ArrayType(IntegerType()))
def udf_tokenize_attention_mask(text):
    return tokenize_text(text)[1]

# Apply the UDF to add tokenized columns
df = df.withColumn("input_ids", udf_tokenize_input_ids(col("removal_reasons")))
df = df.withColumn("attention_mask", udf_tokenize_attention_mask(col("removal_reasons")))

# Ensure the DataFrame has the correct format
df = df.select("input_ids", "attention_mask", "reliability_tracked")