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
@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("""---""")

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,
)