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SparkNLP_NER
File size: 4,648 Bytes 
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import streamlit as st
st.set_page_config(
    layout="centered",  # Can be "centered" or "wide". In the future also "dashboard", etc.
    initial_sidebar_state="auto",  # Can be "auto", "expanded", "collapsed"
    page_title='Extractive Summarization',  # String or None. Strings get appended with "• Streamlit".
    page_icon='./favicon.png',  # String, anything supported by st.image, or None.
)
import pandas as pd
import numpy as np
import os
import sys
sys.path.append(os.path.abspath('./'))
import streamlit_apps_config as config
from streamlit_ner_output import show_html2, jsl_display_annotations, get_color

import sparknlp
from sparknlp.base import *
from sparknlp.annotator import *
from pyspark.sql import functions as F
from sparknlp_display import NerVisualizer
from pyspark.ml import Pipeline
from pyspark.sql.types import StringType
spark= sparknlp.start()

## Marking down NER Style
st.markdown(config.STYLE_CONFIG, unsafe_allow_html=True)

root_path = config.project_path

########## To Remove the Main Menu Hamburger ########

hide_menu_style = """
        <style>
        #MainMenu {visibility: hidden;}
        </style>
        """
st.markdown(hide_menu_style, unsafe_allow_html=True)

########## Side Bar ########

## loading logo(newer version with href)
import base64
@st.cache(allow_output_mutation=True)
def get_base64_of_bin_file(bin_file):
    with open(bin_file, 'rb') as f:
        data = f.read()
    return base64.b64encode(data).decode()

@st.cache(allow_output_mutation=True)
def get_img_with_href(local_img_path, target_url):
    img_format = os.path.splitext(local_img_path)[-1].replace('.', '')
    bin_str = get_base64_of_bin_file(local_img_path)
    html_code = f'''
        <a href="{target_url}">
            <img height="90%" width="90%" src="data:image/{img_format};base64,{bin_str}" />
        </a>'''
    return html_code

logo_html = get_img_with_href('./jsl-logo.png', 'https://www.johnsnowlabs.com/')
st.sidebar.markdown(logo_html, unsafe_allow_html=True)


#sidebar info
model_name= ["nerdl_fewnerd_100d"]
st.sidebar.title("Pretrained model to test")
selected_model = st.sidebar.selectbox("", model_name)

######## Main Page #########
app_title= "Detect up to 8 entity types in general domain texts"
app_description= "Named Entity Recognition model aimed to detect up to 8 entity types from general domain texts. This model was trained on the Few-NERD/inter public dataset using Spark NLP, and is available in Spark NLP Models hub (https://nlp.johnsnowlabs.com/models)"
st.title(app_title)
st.markdown("<h2>"+app_description+"</h2>" , unsafe_allow_html=True)
if selected_model == "nerdl_fewnerd_100d":
    st.markdown("**`PERSON`**   **,** **`ORGANIZATION`**    **,**  **`LOCATION`** **,**  **`ART`** **,**  **`BUILDING`** **,**  **`PRODUCT`** **,**  **`EVENT`** **,**  **`OTHER`**", unsafe_allow_html=True)

st.subheader("")


#### Running model and creating pipeline
st.cache(allow_output_mutation=True)
def get_pipeline(text):
    documentAssembler = DocumentAssembler()\
        .setInputCol("text")\
        .setOutputCol("document")

    sentenceDetector= SentenceDetector()\
        .setInputCols(["document"])\
        .setOutputCol("sentence")

    tokenizer = Tokenizer()\
        .setInputCols(["sentence"])\
        .setOutputCol("token")

    embeddings= WordEmbeddingsModel.pretrained("glove_100d")\
        .setInputCols(["sentence", "token"])\
        .setOutputCol("embeddings")


    ner= NerDLModel.pretrained("nerdl_fewnerd_100d")\
        .setInputCols(["document", "token", "embeddings"])\
        .setOutputCol("ner")


    ner_converter= NerConverter()\
        .setInputCols(["sentence", "token", "ner"])\
        .setOutputCol("ner_chunk")


    pipeline = Pipeline(
        stages = [
        documentAssembler,
        sentenceDetector,
        tokenizer,
        embeddings,
        ner,
        ner_converter
        ])

    empty_df = spark.createDataFrame([[""]]).toDF("text")
    pipeline_model = pipeline.fit(empty_df)

    text_df= spark.createDataFrame(pd.DataFrame({"text": [text]}))
    result= pipeline_model.transform(text_df).toPandas()

    return result


    
text= st.text_input("Type here your text and press enter to run:")

result= get_pipeline(text)

#Displaying Ner Visualization
df= pd.DataFrame({"ner_chunk": result["ner_chunk"].iloc[0]})

labels_set = set()
for i in df['ner_chunk'].values:
    labels_set.add(i[4]['entity'])
labels_set = list(labels_set)

labels = st.sidebar.multiselect(
        "NER Labels", options=labels_set, default=list(labels_set)
    )

show_html2(text, df, labels, "Text annotated with identified Named Entities")