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"""Summary tab rendering functionality"""
import pandas as pd
import streamlit as st
import spacy
from spacy import displacy
###
### INTERNAL FUNCTIONS
###
def __analyse(text, analysis_type, nlp):
"""Analyse and return the named-entity for the given text"""
doc = nlp(text)
### analyse based on type
if analysis_type == "NER":
heading = "Named Entity Recognition (NER)"
result = displacy.render(docs=doc, style="ent", jupyter=False)
label, desc = [], []
for ent in doc.ents:
label.append(ent.label_)
desc.append(spacy.explain(ent.label_))
df = pd.DataFrame(data={"Codes": label, "Description": desc})
df = df.drop_duplicates().reset_index()
elif analysis_type == "POS":
result = ""
word, tag, pos, desc = [], [], [], []
for token in doc:
if token.is_stop or token.is_punct:
continue
word.append(str(token))
tag.append(str(token.tag_))
pos.append(token.pos_)
desc.append(spacy.explain(token.tag_))
df = pd.DataFrame(data=dict(Token=word, Tag=tag, Pos=pos, Description=desc))
heading = "Parts of speech tagging (POS)"
return result, df, heading
def __section(header, nlp):
"""Build page UI elements"""
st.header(header)
st.write(
"Choose the analysis-type (NER/POS) to be performed, "
+ "enter the text in the text area and then click Analyse"
)
analysis_type = st.radio(label="Type:", options=["NER", "POS"])
text = st.text_area("Enter text:", height=300)
### analyse the entered text and show the results
if st.button("Analyse"):
result, df, heading = __analyse(text, analysis_type, nlp)
st.subheader(heading)
st.divider()
st.write(result, unsafe_allow_html=True)
st.write(" ")
st.dataframe(df, use_container_width=True)
st.divider()
###
### MAIN FLOW, entry point
###
def render(nlp):
"""NER tab page"""
__section("Named Entity & Parts Of Speech Recognition", nlp)
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