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