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import streamlit as st
import sparknlp
import os
import pandas as pd
from sparknlp.base import *
from sparknlp.annotator import *
from pyspark.ml import Pipeline
from sparknlp.pretrained import PretrainedPipeline
# Page configuration
st.set_page_config(
layout="wide",
page_title="Spark NLP Demos App",
initial_sidebar_state="auto"
)
# CSS for styling
st.markdown("""
<style>
.main-title {
font-size: 36px;
color: #4A90E2;
font-weight: bold;
text-align: center;
}
.section p, .section ul {
color: #666666;
}
</style>
""", unsafe_allow_html=True)
@st.cache_resource
def init_spark():
return sparknlp.start()
@st.cache_resource
def create_pipeline(model):
documentAssembler = DocumentAssembler()\
.setInputCol("text")\
.setOutputCol("document")
use = UniversalSentenceEncoder.pretrained()\
.setInputCols(["document"])\
.setOutputCol("sentence_embeddings")
sentimentdl = ClassifierDLModel.pretrained(model)\
.setInputCols(["sentence_embeddings"])\
.setOutputCol("sentiment")
nlpPipeline = Pipeline(stages = [documentAssembler, use, sentimentdl])
return nlpPipeline
def fit_data(pipeline, data):
empty_df = spark.createDataFrame([['']]).toDF('text')
pipeline_model = pipeline.fit(empty_df)
model = LightPipeline(pipeline_model)
results = model.fullAnnotate(data)[0]
return results['sentiment'][0].result
# Set up the page layout
st.markdown('<div class="main-title">Detect Sarcastic Tweets with Spark NLP</div>', unsafe_allow_html=True)
# Sidebar content
model = st.sidebar.selectbox(
"Choose the pretrained model",
["classifierdl_use_sarcasm"],
help="For more info about the models visit: https://sparknlp.org/models"
)
# Reference notebook link in sidebar
link = """
<a href="https://colab.research.google.com/github/JohnSnowLabs/spark-nlp-workshop/blob/master/tutorials/streamlit_notebooks/SENTIMENT_EN_SARCASM.ipynb">
<img src="https://colab.research.google.com/assets/colab-badge.svg" style="zoom: 1.3" alt="Open In Colab"/>
</a>
"""
st.sidebar.markdown('Reference notebook:')
st.sidebar.markdown(link, unsafe_allow_html=True)
# Load examples
examples = [
"Love getting home from work knowing that in less than 8hours you're getting up to go back there again.",
"Oh my gosh! Can you imagine @JessieJ playing piano on her tour while singing a song. I would die and go to heaven. #sheisanangel",
"Dear Teva, thank you for waking me up every few hours by howling. Your just trying to be mother natures alarm clock.",
"The United States is a signatory to this international convention",
"If I could put into words how much I love waking up at am on Tuesdays I would",
"@pdomo Don't forget that Nick Foles is also the new Tom Brady. What a preseason! #toomanystudQBs #thankgodwedonthavetebow",
"I cant even describe how excited I am to go cook noodles for hours",
"@Will_Piper should move back up fella. I'm already here... On my own... Having loads of fun",
"Tweeting at work... Having sooooo much fun and honestly not bored at all #countdowntillfinish",
"I can do what I want to. I play by my own rules"
]
selected_text = st.selectbox("Select a sample", examples)
custom_input = st.text_input("Try it for yourself!")
if custom_input:
selected_text = custom_input
elif selected_text:
selected_text = selected_text
st.subheader('Selected Text')
st.write(selected_text)
st.subheader('Selected Text')
st.write(selected_text)
# Initialize Spark and create pipeline
spark = init_spark()
pipeline = create_pipeline(model)
output = fit_data(pipeline, selected_text)
# Display output sentence
if output in ['neutral', 'normal']:
st.markdown("""<h3>This seems like <span style="color: #209DDC">{}</span> news. <span style="font-size:35px;">🙂</span></h3>""".format(output), unsafe_allow_html=True)
elif output == 'sarcasm':
st.markdown("""<h3>This seems like a <span style="color: #B64434">{}</span> tweet. <span style="font-size:35px;">🙃</span></h3>""".format('sarcastic'), unsafe_allow_html=True)
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