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import gradio as gr | |
from sparknlp.base import * | |
from sparknlp.annotator import * | |
from sparknlp.pretrained import PretrainedPipeline | |
import sparknlp | |
print("Sparknlp Version: " + sparknlp.version()) | |
# Start SparkSession with Spark NLP | |
# start() functions has 4 parameters: gpu, spark23, spark24, and memory | |
# sparknlp.start(gpu=True) will start the session with GPU support | |
# sparknlp.start(spark23=True) is when you have Apache Spark 2.3.x installed | |
# sparknlp.start(spark24=True) is when you have Apache Spark 2.4.x installed | |
# sparknlp.start(memory="16G") to change the default driver memory in SparkSession | |
spark = sparknlp.start() | |
# Download a pre-trained pipeline | |
pipeline = PretrainedPipeline('explain_document_dl', lang='en') | |
# Your testing dataset | |
placeholder = """ | |
The Mona Lisa is a 16th century oil painting created by Leonardo. | |
It's held at the Louvre in Paris. | |
""" | |
print(spark) | |
def fn(text: str): | |
result = pipeline.annotate(text) | |
return result | |
iface = gr.Interface( | |
fn=fn, | |
inputs="text", | |
outputs="json", | |
title="Spark NLP explain_document_dl pipeline", | |
description=f"Spark object: {spark}", | |
examples=[placeholder], | |
) | |
if __name__ == "__main__": | |
iface.launch() | |