Cognitive Services - Analyze Text

import os

if os.environ.get("AZURE_SERVICE", None) == "Microsoft.ProjectArcadia":
    from pyspark.sql import SparkSession
    spark = SparkSession.builder.getOrCreate()
    from notebookutils.mssparkutils.credentials import getSecret
    os.environ['TEXT_API_KEY'] = getSecret("mmlspark-keys", "mmlspark-cs-key")

#put your service keys here
key = os.environ['TEXT_API_KEY']
location = 'eastus'

df = spark.createDataFrame(data=[
        ["en", "Hello Seattle"],
        ["en", "There once was a dog who lived in London and thought she was a human"]
    ],
    schema=["language","text"])

display(df)

from synapse.ml.cognitive import *

text_analyze = (TextAnalyze()
    .setLocation(location)
    .setSubscriptionKey(key)
    .setTextCol("text")
    .setOutputCol("textAnalysis")
    .setErrorCol("error")
    .setLanguageCol("language")
    # set the tasks to perform
    .setEntityRecognitionTasks([{"parameters": { "model-version": "latest"}}])
    .setKeyPhraseExtractionTasks([{"parameters": { "model-version": "latest"}}])
    # Uncomment these lines to add more tasks
    # .setEntityRecognitionPiiTasks([{"parameters": { "model-version": "latest"}}])
    # .setEntityLinkingTasks([{"parameters": { "model-version": "latest"}}])
    # .setSentimentAnalysisTasks([{"parameters": { "model-version": "latest"}}])
    )

df_results = text_analyze.transform(df)

display(df_results)

from pyspark.sql.functions import col

# reformat and display for easier viewing
display(
    df_results.select("language", "text", "error", col("textAnalysis").getItem(0)) # we are not batching so only have a single result
        .select("language", "text", "error", "textAnalysis[0].*") # explode the Text Analytics tasks into columns
)