app.py

import streamlit as st
from PIL import Image
import os
import pandas as pd


@st.cache
def load_data():
    df_ind = pd.read_csv("data/df_individuals_score.csv", index_col=[0])
    df_ind = df_ind.drop("region_code", axis=1)
    df_ind["productive_year"] = df_ind["productive_year"].astype(int)
    df_ind["individual_wikidata_id"] = "https://www.wikidata.org/wiki/" + df_ind[
        "individual_wikidata_id"
    ].astype(str)

    df_ind = df_ind[df_ind["productive_year"] <= 1800]

    return df_ind


df_ind = load_data()

st.title("Our History in Data")

# Set the global index path
global_index_path = "data/immaterial_index/figures_trends_R/results"
global_index_path_per_capita = (
    "data/immaterial_index/figures_trends_R/results_per_capita"
)
unseen_index_path = (
    "data/immaterial_index/figures_trends_R/figures_unseen/results_unseen"
)
unseen_capita_index_path = (
    "data/immaterial_index/figures_trends_R/figures_unseen/results_unseen/per_capita"
)


population_path = "data/population"
maps_path = "data/map_figures"


from region_list import region_list

index_paths = {}

for region_key in region_list:
    # Create the index paths for the current region
    index_paths[region_key] = {
        "map": f"{maps_path}/map_{region_key}.png",
        "global_index": f"{global_index_path}/{region_key}.png",
        "global_index_per_capita": f"{global_index_path_per_capita}/{region_key}.png",
        "unseen_index": f"{unseen_index_path}/{region_key}.png",
        "unseen_index_capita": f"{unseen_capita_index_path}/{region_key}.png",
        "population_index": f"{population_path}/{region_key}.png",
    }

# Get the region names (keys) from the index_paths dictionary
regions = list(index_paths.keys())

# Allow the user to select a region
selected_region = st.sidebar.selectbox("Region:", regions, index=regions.index("Japan"))

# Display the selected region's images vertically
if selected_region in index_paths:

    df = df_ind[df_ind["region_name"] == selected_region]
    df["productive_year"] = round(df["productive_year"], 0)
    df = df.drop(["region_name", "decade"], axis=1)
    df = df[
        [
            "individual_name",
            "productive_year",
            "score",
            "individual_wikidata_id" "",
        ]
    ]
    df = df.sort_values("score", ascending=False)
    df = df.rename(columns={"score": "Number of Catalogs"})
    df = df.reset_index(drop=True)
    st.dataframe(df)

    st.write(f"Number of Cultural producers active before 1800: {len(df)}")

    for key, path in index_paths[selected_region].items():
        if os.path.exists(path):

            if key == "global_index":
                st.subheader("Global Index")
                st.image(
                    Image.open(path), caption=key.capitalize(), use_column_width=True
                )
            elif key == "global_index_per_capita":
                st.subheader("Index per capita")
                st.image(
                    Image.open(path), caption=key.capitalize(), use_column_width=True
                )
            elif key == "unseen_index":
                st.subheader("Unsee-Species Index")
                st.image(
                    Image.open(path), caption=key.capitalize(), use_column_width=True
                )
            elif key == "unseen_index_capita":
                st.subheader("Unsee-Species per capita Index")
                st.image(
                    Image.open(path), caption=key.capitalize(), use_column_width=True
                )
            elif key == "population_index":
                st.subheader("Population Index")
                st.image(
                    Image.open(path), caption=key.capitalize(), use_column_width=True
                )
            elif key == "map":
                st.subheader("Maps")
                st.sidebar.image(
                    Image.open(path), caption=key.capitalize(), use_column_width=True
                )
        else:
            st.write(f"File for {key.capitalize()} does not exist.")