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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
from annotated_text import annotated_text
# Page configuration
st.set_page_config(
layout="wide",
initial_sidebar_state="auto"
)
# CSS for styling
st.markdown("""
<style>
.main-title {
font-size: 36px;
color: #4A90E2;
font-weight: bold;
text-align: center;
}
.section {
background-color: #f9f9f9;
padding: 10px;
border-radius: 10px;
margin-top: 10px;
}
.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):
document_assembler = DocumentAssembler() \
.setInputCol('text') \
.setOutputCol('document')
sentence_detector = SentenceDetector() \
.setInputCols(['document']) \
.setOutputCol('sentence')
tokenizer = Tokenizer() \
.setInputCols(['sentence']) \
.setOutputCol('token')
tokenClassifier_loaded = BertForTokenClassification.pretrained("bert_token_classifier_hi_en_ner", "hi") \
.setInputCols(["sentence", 'token']) \
.setOutputCol("ner")
ner_converter = NerConverter() \
.setInputCols(["sentence", "token", "ner"]) \
.setOutputCol("ner_chunk")
# Create the NLP pipeline
pipeline = Pipeline(stages=[
document_assembler,
sentence_detector,
tokenizer,
tokenClassifier_loaded,
ner_converter
])
return pipeline
def fit_data(pipeline, data):
empty_df = spark.createDataFrame([['']]).toDF('text')
pipeline_model = pipeline.fit(empty_df)
model = LightPipeline(pipeline_model)
result = model.fullAnnotate(data)
return result
def annotate(data):
document, chunks, labels = data["Document"], data["NER Chunk"], data["NER Label"]
annotated_words = []
for chunk, label in zip(chunks, labels):
parts = document.split(chunk, 1)
if parts[0]:
annotated_words.append(parts[0])
annotated_words.append((chunk, label))
document = parts[1]
if document:
annotated_words.append(document)
annotated_text(*annotated_words)
# Sidebar content
model = st.sidebar.selectbox(
"Choose the pretrained model",
["bert_token_classifier_hi_en_ner"],
help="For more info about the models visit: https://sparknlp.org/models"
)
# Set up the page layout
title, sub_title = ('Named Entity Recogniation for Hindi+English text', 'This model was imported from Hugging Face to carry out Name Entity Recognition with mixed Hindi-English texts, provided by the LinCE repository.')
st.markdown(f'<div class="main-title">{title}</div>', unsafe_allow_html=True)
st.markdown(f'<div class="section"><p>{sub_title}</p></div>', unsafe_allow_html=True)
# Reference notebook link in sidebar
link = """
<a href="https://colab.research.google.com/github/JohnSnowLabs/spark-nlp-workshop/blob/master/tutorials/streamlit_notebooks/NER_HINDI_ENGLISH.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 = st.selectbox("Select an example", examples)
custom_input = st.text_input("Try it with your own Sentence!")
text_to_analyze = custom_input if custom_input else selected_text
st.subheader('Full example text')
HTML_WRAPPER = """<div class="scroll entities" style="overflow-x: auto; border: 1px solid #e6e9ef; border-radius: 0.25rem; padding: 1rem; margin-bottom: 2.5rem; white-space:pre-wrap">{}</div>"""
st.markdown(HTML_WRAPPER.format(text_to_analyze), unsafe_allow_html=True)
# Initialize Spark and create pipeline
spark = init_spark()
pipeline = create_pipeline(model)
output = fit_data(pipeline, text_to_analyze)
# Display matched sentence
st.subheader("Processed output:")
results = {
'Document': output[0]['document'][0].result,
'NER Chunk': [n.result for n in output[0]['ner_chunk']],
"NER Label": [n.metadata['entity'] for n in output[0]['ner_chunk']]
}
annotate(results)
with st.expander("View DataFrame"):
df = pd.DataFrame({'NER Chunk': results['NER Chunk'], 'NER Label': results['NER Label']})
df.index += 1
st.dataframe(df)
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