Spaces:
Runtime error
Runtime error
File size: 5,228 Bytes
69abbc0 03bdce4 69abbc0 03bdce4 69abbc0 03bdce4 69abbc0 03bdce4 69abbc0 03bdce4 69abbc0 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 |
import streamlit as st
from pathlib import Path
import json
from support_functions import HealthseaSearch
def visualize_dataset():
# 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
@st.cache(allow_output_mutation=True)
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("""This app presents the analyzed dataset of up to one million reviews. You can search for the best products and substances to any health aspect based on what reviewers wrote in their reviews.""")
st.markdown("""---""")
st.markdown(central_text("🎀 Dataset"), unsafe_allow_html=True)
kpi_products, kpi_reviews, kpi_condition, kpi_benefit = st.columns(4)
kpi_products.markdown(kpi(len(products), "Products"), unsafe_allow_html=True)
kpi_reviews.markdown(kpi(933.240, "Reviews"), unsafe_allow_html=True)
kpi_condition.markdown(kpi(len(conditions), "Conditions"), unsafe_allow_html=True)
kpi_benefit.markdown(kpi(len(benefits), "Benefits"), unsafe_allow_html=True)
st.markdown("""---""")
# Expander
show_conditions, show_benefits = st.columns(2)
with show_conditions.expander("Top 1000 mentioned Conditions"):
st.write(search_engine.get_all_conditions_df())
with show_benefits.expander("Top 1000 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.markdown("""The products are scored based on what reviewers say. Additional variables in the scoring function are product rating, helpful count and whether the review is considered 'fake'. """)
# DataFrame
st.write(search_engine.get_products_df(search, n))
# KPI & Alias
aspect_alias = search_engine.get_aspect(search)["alias"]
if len(aspect_alias) > 0:
kpi_mentions, kpi_product_mentions, kpi_alias = st.columns(3)
kpi_mentions.markdown(
kpi(search_engine.get_aspect_meta(search)["frequency"], "Mentions"),
unsafe_allow_html=True,
)
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,
)
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.markdown("""To improve the search, the table also shows results of other health aspects with a high similarity""")
st.write(search_engine.tsne_plot(vectors))
else:
kpi_mentions, kpi_product_mentions = st.columns(2)
kpi_mentions.markdown(
kpi(search_engine.get_aspect_meta(search)["frequency"], "Mentions"),
unsafe_allow_html=True,
)
kpi_product_mentions.markdown(
kpi(len(search_engine.get_aspect(search)["products"]), "Products"),
unsafe_allow_html=True,
)
st.markdown("""---""")
# Substances
st.markdown(central_text("🍯 Substances"), unsafe_allow_html=True)
st.markdown("""The scores of the substances are based on the products""")
# DataFrame
st.write(search_engine.get_substances_df(search, n))
kpi_tmp, kpi_substances = st.columns(2)
kpi_substances.markdown(
kpi(len(search_engine.get_aspect(search)["substance"]), "Substances"),
unsafe_allow_html=True,
)
|