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import streamlit as st
import spacy
from spacy_streamlit import visualize_ner
from support_functions import HealthseaPipe
import operator
def visualize_pipeline():
healthsea_pipe = HealthseaPipe()
color_code = {
"POSITIVE": ("#3C9E58", "#1B7735"),
"NEGATIVE": ("#FF166A", "#C0094B"),
"NEUTRAL": ("#7E7E7E", "#4E4747"),
"ANAMNESIS": ("#E49A55", "#AD6B2D"),
}
example_reviews = [
"This is great for joint pain.",
"This help joint pain but causes rashes",
"I'm diagnosed with gastritis. This product helped!",
"Made my insomnia worse",
"Didn't help my energy levels",
]
# Functions
def kpi(n, text):
html = f"""
<div class='kpi'>
<h1>{n}</h1>
<span>{text}</span>
</div>
"""
return html
def central_text(text):
html = f"""<h2 class='central_text'>{text}</h2>"""
return html
def format_clause(text, meta, pred):
html = f"""
<div>
<div class="clause" style="background-color:{color_code[pred][0]} ; box-shadow: 0px 5px {color_code[pred][1]}; border-color:{color_code[pred][1]};">
<div class="clause_text">{text}</div>
</div>
<div class="clause_meta">
<div>{meta}</div>
</div>
</div>"""
return html
def format_effect(text, pred):
html = f"""
<div>
<div class="clause" style="background-color:{color_code[pred][0]} ; box-shadow: 0px 5px {color_code[pred][1]}; border-color:{color_code[pred][1]};">
<div class="clause_text">{text}</div>
</div>
</div>"""
return html
# Load model
nlp = spacy.load("en_healthsea")
# Pipeline
st.markdown("""---""")
st.markdown(central_text("⚙️ Pipeline"), unsafe_allow_html=True)
check = st.checkbox("Use predefined examples")
if not check:
text = st.text_input(label="Write a review", value="This is great for joint pain!")
else:
text = st.selectbox("Predefined example reviews", example_reviews)
doc = nlp(text)
# NER
visualize_ner(
doc,
labels=nlp.get_pipe("ner").labels,
show_table=False,
title="✨ Named Entity Recognition",
colors={"CONDITION": "#FF4B76", "BENEFIT": "#629B68"},
)
st.markdown("""---""")
# Segmentation, Blinding, Classification
st.markdown("## 🔮 Segmentation, Blinding, Classification")
clauses = healthsea_pipe.get_clauses(doc)
for doc_clause, clause in zip(clauses, doc._.clauses):
classification = max(clause["cats"].items(), key=operator.itemgetter(1))[0]
percentage = round(float(clause["cats"][classification]) * 100, 2)
meta = f"{clause['ent_name']} ({classification} {percentage}%)"
st.markdown(
format_clause(doc_clause.text, meta, classification), unsafe_allow_html=True
)
st.markdown("\n")
st.markdown("""---""")
# Aggregation
st.markdown("## 🔗 Aggregation")
for effect in doc._.health_effects:
st.markdown(
format_effect(
f"{doc._.health_effects[effect]['effect']} effect on {effect}",
doc._.health_effects[effect]["effect"],
),
unsafe_allow_html=True,
)
st.markdown("\n")
st.markdown("""---""")
# Indepth
st.markdown("## 🔧 Pipeline attributes")
clauses_col, effect_col = st.columns(2)
clauses_col.markdown("### doc._.clauses")
for clause in doc._.clauses:
clauses_col.json(clause)
effect_col.markdown("### doc._.health_effects")
effect_col.json(doc._.health_effects)
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