Demo.py

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_name = 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
]

st.subheader("Detect the general sentiment expressed in a movie review or tweet by using our pretrained Spark NLP DL classifier.")

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.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 ['pos', 'positive', 'POSITIVE']:
  st.markdown("""<h3>This seems like a <span style="color: green">{}</span> {}. <span style="font-size:35px;">&#128515;</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;">&#128544;</span?</h3>""".format('negative', result_type), unsafe_allow_html=True)