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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("tfhub_use", "en")\
.setInputCols(["document"])\
.setOutputCol("sentence_embeddings")
sentimentdl = SentimentDLModel.pretrained(model, "en")\
.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 Sentiment Detection with Spark NLP</div>', unsafe_allow_html=True)
# Sidebar content
model = st.sidebar.selectbox(
"Choose the pretrained model",
["sentimentdl_use_imdb", "sentimentdl_use_twitter"],
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.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
folder_path = f"inputs/{model}"
examples = [
lines[1].strip()
for filename in os.listdir(folder_path)
if filename.endswith('.txt')
for lines in [open(os.path.join(folder_path, filename), 'r', encoding='utf-8').readlines()]
if len(lines) >= 2
]
selected_text = None
result_type = 'tweet'
if 'imdb' in model.lower() or 't5' in model.lower():
selected_text = st.selectbox("Select a sample IMDB review", examples)
result_type = 'review'
else:
selected_text = st.selectbox("Select a sample Tweet", 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.write('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 ['pos', 'positive', 'POSITIVE']:
st.markdown("""<h3>This seems like a <span style="color: green">{}</span> {}. <span style="font-size:35px;">😃</span></h3>""".format('positive', result_type), unsafe_allow_html=True)
elif output in ['neg', 'negative', 'NEGATIVE']:
st.markdown("""<h3>This seems like a <span style="color: red">{}</span> {}. <span style="font-size:35px;">😠</span?</h3>""".format('negative', result_type), unsafe_allow_html=True)
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