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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) | |
def init_spark(): | |
return sparknlp.start() | |
def create_pipeline(model): | |
documentAssembler = DocumentAssembler()\ | |
.setInputCol("text")\ | |
.setOutputCol("document") | |
use = UniversalSentenceEncoder.pretrained("tfhub_use", "en")\ | |
.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">State-of-the-Art Emotion Detecter in Tweets with Spark NLP</div>', unsafe_allow_html=True) | |
# Sidebar content | |
model = st.sidebar.selectbox( | |
"Choose the pretrained model", | |
["classifierdl_use_emotion"], | |
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_EMOTION.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 = [ | |
"I am SO happy the news came out in time for my birthday this weekend! My inner 7-year-old cannot WAIT!", | |
"That moment when you see your friend in a commercial. Hahahaha!", | |
"My soul has just been pierced by the most evil look from @rickosborneorg. A mini panic attack & chill in bones followed soon after.", | |
"For some reason I woke up thinkin it was Friday then I got to school and realized its really Monday -_-", | |
"I'd probably explode into a jillion pieces from the inablility to contain all of my if I had a Whataburger patty melt right now. #drool", | |
"These are not emotions. They are simply irrational thoughts feeding off of an emotion", | |
"Found out im gonna be with sarah bo barah in ny for one day!!! Eggcitement :)", | |
"That awkward moment when you find a perfume box full of sensors!", | |
"Just home from group celebration - dinner at Trattoria Gianni, then Hershey Felder's performance - AMAZING!!", | |
"Nooooo! My dad turned off the internet so I can't listen to band music!" | |
] | |
st.subheader("Automatically identify Joy, Surprise, Fear, Sadness in Tweets using out pretrained Spark NLP DL classifier.") | |
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) | |
# Initialize Spark and create pipeline | |
spark = init_spark() | |
pipeline = create_pipeline(model) | |
output = fit_data(pipeline, selected_text) | |
# Display output sentence | |
if output == 'joy': | |
st.markdown("""<h3>This seems like a <span style="color: #f0a412">{}</span> tweet. <span style="font-size:35px;">😂</span></h3>""".format('joyous'), unsafe_allow_html=True) | |
elif output == 'surprise': | |
st.markdown("""<h3>This seems like a <span style="color: #209DDC">{}</span> tweet. <span style="font-size:35px;">😊</span></h3>""".format('surprised'), unsafe_allow_html=True) | |
elif output == 'sadness': | |
st.markdown("""<h3>This seems like a <span style="color: #8F7F6C">{}</span> tweet. <span style="font-size:35px;">😟</span></h3>""".format('sad'), unsafe_allow_html=True) | |
elif output == 'fear': | |
st.markdown("""<h3>This seems like a <span style="color: #B64434">{}</span> tweet. <span style="font-size:35px;">😱</span></h3>""".format('fearful'), unsafe_allow_html=True) | |