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| import streamlit as st | |
| from pathlib import Path | |
| import json | |
| from support_functions import HealthseaSearch | |
| # 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 results of Healthsea on a dataset of up to 1 million reviews to 10.000 products. You can use the app to search for any health aspect, whether it's a disease (e.g. joint pain) or a positive state of health (e.g. energy), the app returns a list of products and substances. | |
| You can visit the [Healthsea Pipeline app](https://huggingface.co/spaces/spacy/healthsea-pipeline) for exploring the pipeline itself. | |
| """ | |
| ) | |
| st.warning("""Healthsea is an experimental project and the results should not be used as a foundation for solving health problems. Nor do we want to give the impression that supplements are the answer to anyone's health issues.""") | |
| # Configuration | |
| health_aspect_path = Path("data/health_aspects.json") | |
| product_path = Path("data/products.json") | |
| condition_path = Path("data/condition_vectors.json") | |
| benefit_path = Path("data/benefit_vectors.json") | |
| # Load data | |
| def load_data( | |
| _health_aspect_path: Path, | |
| _product_path: Path, | |
| _condition_path: Path, | |
| _benefit_path: Path, | |
| ): | |
| with open(_health_aspect_path) as reader: | |
| health_aspects = json.load(reader) | |
| with open(_product_path) as reader: | |
| products = json.load(reader) | |
| with open(_condition_path) as reader: | |
| conditions = json.load(reader) | |
| with open(_benefit_path) as reader: | |
| benefits = json.load(reader) | |
| return health_aspects, products, conditions, benefits | |
| # Functions | |
| def kpi(n, text): | |
| html = f""" | |
| <div class='kpi'> | |
| <h1 class='kpi_header'>{n}</h1> | |
| <span>{text}</span> | |
| </div> | |
| """ | |
| return html | |
| def central_text(text): | |
| html = f"""<h2 class='central_text'>{text}</h2>""" | |
| return html | |
| # Loading data | |
| health_aspects, products, conditions, benefits = load_data( | |
| health_aspect_path, product_path, condition_path, benefit_path | |
| ) | |
| search_engine = HealthseaSearch(health_aspects, products, conditions, benefits) | |
| # KPI | |
| st.markdown("""---""") | |
| st.markdown(central_text("🎀 Dataset"), unsafe_allow_html=True) | |
| kpi_products, kpi_reviews, kpi_condition, kpi_benefit = st.columns(4) | |
| def round_to_k(value): | |
| return str(round(value/1000,1))+"k" | |
| kpi_products.markdown(kpi(round_to_k(len(products)), "Products"), unsafe_allow_html=True) | |
| kpi_reviews.markdown(kpi(round_to_k(int(933240)), "Reviews"), unsafe_allow_html=True) | |
| kpi_condition.markdown(kpi(round_to_k(len(conditions)), "Conditions"), unsafe_allow_html=True) | |
| kpi_benefit.markdown(kpi(round_to_k(len(benefits)), "Benefits"), unsafe_allow_html=True) | |
| st.markdown("""---""") | |
| # Expander | |
| show_conditions, show_benefits = st.columns(2) | |
| with show_conditions.expander("Top mentioned Conditions"): | |
| st.write(search_engine.get_all_conditions_df()) | |
| with show_benefits.expander("Top mentioned Benefits"): | |
| st.write(search_engine.get_all_benefits_df()) | |
| st.markdown("""---""") | |
| # Search | |
| search = st.text_input(label="Search for an health aspect", value="joint pain") | |
| n = st.slider("Show top n results", min_value=10, max_value=1000, value=25) | |
| st.markdown("""---""") | |
| st.markdown(central_text("🧃 Products"), unsafe_allow_html=True) | |
| st.info("""The product score is based on the results of Healthsea. Variables used for the score are: health effect prediction, product rating, helpful count and whether the review is considered a 'fake review'. """) | |
| # DataFrame | |
| st.write(search_engine.get_products_df(search, n)) | |
| # KPI & Alias | |
| aspect_alias = search_engine.get_aspect(search)["alias"] | |
| kpi_product_mentions, kpi_alias = st.columns(2) | |
| kpi_product_mentions.markdown(kpi(len(search_engine.get_aspect(search)["products"]), "Products"), unsafe_allow_html=True) | |
| kpi_alias.markdown( | |
| kpi(len(aspect_alias), "Similar health aspects"), | |
| unsafe_allow_html=True, | |
| ) | |
| depth = st.slider("Depth", min_value=0, max_value=5, value=2) | |
| recursive_alias, recursive_edges = search_engine.get_recursive_alias(search,0,{},[],depth) | |
| vectors = [] | |
| main_aspect = search_engine.get_aspect_meta(search) | |
| vectors.append((main_aspect["name"], main_aspect["vector"])) | |
| for aspect in aspect_alias: | |
| current_aspect = search_engine.get_aspect_meta(aspect) | |
| vectors.append((current_aspect["name"], current_aspect["vector"])) | |
| st.markdown("\n") | |
| st.info("""Health aspects with a high similarity (>=90%) are clustered together.""") | |
| #search_engine.pyvis(vectors) | |
| search_engine.pyvis2(recursive_alias,recursive_edges) | |
| st.markdown("""---""") | |
| # Substances | |
| st.markdown(central_text("🍯 Substances"), unsafe_allow_html=True) | |
| st.info("""Substance scores are based on product scores""") | |
| # DataFrame | |
| st.write(search_engine.get_substances_df(search, n)) | |
| kpi_substances, empty = st.columns(2) | |
| kpi_substances.markdown( | |
| kpi(len(search_engine.get_aspect(search)["substance"]), "Substances"), | |
| unsafe_allow_html=True, | |
| ) | |