File size: 5,293 Bytes
f996927 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 |
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 = SentenceDetectorDLModel.pretrained("sentence_detector_dl", "en") \
.setInputCols(["document"]) \
.setOutputCol("sentence")
tokenizer = Tokenizer() \
.setInputCols(["sentence"]) \
.setOutputCol("token")
embeddings = WordEmbeddingsModel.pretrained("glove_100d", "en") \
.setInputCols("sentence", "token") \
.setOutputCol("embeddings")
ner = NerDLModel.pretrained("nerdl_snips_100d") \
.setInputCols(["sentence", "token", "embeddings"]) \
.setOutputCol("ner")
ner_converter = NerConverter() \
.setInputCols(["document", "token", "ner"]) \
.setOutputCol("ner_chunk")
pipeline = Pipeline(stages=[
document_assembler,
sentence_detector,
tokenizer,
embeddings,
ner,
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)
# Set up the page layout
st.markdown('<div class="main-title">Extract intents in general commands related to music, restaurants, movies.</div>', unsafe_allow_html=True)
st.markdown("""
<div class="section">
<p>The <strong>nerdl_snips_100d</strong> model excels at identifying and classifying key entities in user commands related to music, restaurants, and movies. This model provides a structured representation of user intents by detecting and categorizing various entities such as genres, restaurant types, and movie titles.</p>
</div>
""", unsafe_allow_html=True)
# Sidebar content
model = st.sidebar.selectbox(
"Choose the pretrained model",
["nerdl_snips_100d"],
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/NER.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)
|