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| import streamlit as st | |
| import spacy | |
| from spacy_streamlit import visualize_ner | |
| from support_functions import HealthseaPipe | |
| import operator | |
| # Header | |
| with open("style.css") as f: | |
| st.markdown("<style>" + f.read() + "</style>", unsafe_allow_html=True) | |
| # Intro | |
| st.title("Welcome to Healthsea 🪐") | |
| intro, jellyfish = st.columns(2) | |
| jellyfish.markdown("\n") | |
| intro.subheader("Create easier access to health✨") | |
| jellyfish.image("data/img/Jellymation.gif") | |
| intro.markdown( | |
| """Healthsea is an end-to-end spaCy v3 pipeline for analyzing user reviews to supplementary products and extracting their potential effects on health.""" | |
| ) | |
| intro.markdown( | |
| """The code for Healthsea is provided in this [github repository](https://github.com/explosion/healthsea). Visit our [blog post](https://explosion.ai/blog/healthsea) or more about the Healthsea project. | |
| """ | |
| ) | |
| st.write( | |
| """This app visualizes the individual processing steps of the pipeline in which you can write custom reviews to get insights into the functionality of all the different components. | |
| You can visit the [Healthsea Demo app](https://huggingface.co/spaces/spacy/healthsea-demo) for exploring the Healthsea processing on productive data. | |
| """ | |
| ) | |
| st.markdown("""---""") | |
| # Setup | |
| 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.", | |
| "Product helped my joint pain but it also caused rashes.", | |
| "I'm diagnosed with gastritis. This product helped!", | |
| "This has made my insomnia even worse.", | |
| "It didn't help my joint pain.", | |
| ] | |
| # 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 | |
| load_state = st.info("Loading...") | |
| try: | |
| load_state.info("Loading model...") | |
| if "model" not in st.session_state: | |
| nlp = spacy.load("en_healthsea") | |
| st.session_state["model"] = nlp | |
| load_state.success ("Loading complete!") | |
| # Download model | |
| except LookupError: | |
| import nltk | |
| import benepar | |
| load_state.info ("Downloading model...") | |
| benepar.download('benepar_en3') | |
| if "model" not in st.session_state: | |
| nlp = spacy.load("en_healthsea") | |
| st.session_state["model"] = nlp | |
| load_state.success ("Loading complete!") | |
| except Exception as e: | |
| load_state.success ("Something went wrong!") | |
| st.error(e) | |
| # Pipeline | |
| 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) | |
| nlp = st.session_state["model"] | |
| 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.info("""The NER identifies two labels: 'Condition' and 'Benefit'. 'Condition' entities are generally diseases, symptoms, or general health problems (e.g. joint pain), while 'Benefit' entities are positive desired health aspects (e.g. energy)""") | |
| 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.info("""The text is segmented into clauses and classified by a Text Classification model. We additionally blind found entities to improve generalization and to inform the model about our current target entity. | |
| The Text Classification predicts four exclusive classes that represent the health effect: 'Positive', 'Negative', 'Neutral', 'Anamnesis'.""") | |
| st.info("""The 'Anamnesis' class is defined as the current state of health of a reviewer (e.g. 'I am diagnosed with joint pain'). It is used to link health aspects to health effects that are mentioned later in a review.""") | |
| 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.info("""Multiple classification are aggregated into one final classification.""") | |
| 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) | |