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app.py
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import os
import random
import json
import streamlit as st
import streamlit.components.v1 as components
from streamlit_extras.mention import mention
import spacy
import numpy as np
import pandas as pd
import altair as alt
import plotly.express as px
import plotly.graph_objects as go
import matplotlib.pyplot as plt
from wordcloud import WordCloud, ImageColorGenerator, STOPWORDS
from PIL import Image
import pydeck as pdk
# ----------------------
# Loading Function
# ----------------------
@st.cache_data
def load_kjv_clean():
df = pd.read_csv("data/kjv_clean.csv")
df = df.astype({col: 'string' for col in df.select_dtypes(include='object').columns})
return df
@st.cache_data
def load_kjv_locs_all():
df = pd.read_csv("data/kjv_locs_all.csv")
return df
@st.cache_data
def load_sentiment_by_book():
df = pd.read_csv("data/sentiment_by_book.csv")
return df
@st.cache_data
def load_spacy():
model_path = os.path.join("data", "en_core_web_sm", "en_core_web_sm-3.8.0")
if os.path.isdir(model_path):
return spacy.load(model_path)
else:
raise FileNotFoundError(f"SpaCy model not found at {model_path}. Please ensure it is correctly placed.")
@st.cache_data
def load_kjv_books():
old_testament = [
"Genesis", "Exodus", "Leviticus", "Numbers", "Deuteronomy",
"Joshua", "Judges", "Ruth", "1 Samuel", "2 Samuel",
"1 Kings", "2 Kings", "1 Chronicles", "2 Chronicles", "Ezra",
"Nehemiah", "Esther", "Job", "Psalms", "Proverbs",
"Ecclesiastes", "Song of Solomon", "Isaiah", "Jeremiah", "Lamentations",
"Ezekiel", "Daniel", "Hosea", "Joel", "Amos",
"Obadiah", "Jonah", "Micah", "Nahum", "Habakkuk",
"Zephaniah", "Haggai", "Zechariah", "Malachi"
]
new_testament = [
"Matthew", "Mark", "Luke", "John", "Acts",
"Romans", "1 Corinthians", "2 Corinthians", "Galatians", "Ephesians",
"Philippians", "Colossians", "1 Thessalonians", "2 Thessalonians", "1 Timothy",
"2 Timothy", "Titus", "Philemon", "Hebrews", "James",
"1 Peter", "2 Peter", "1 John", "2 John", "3 John",
"Jude", "Revelation"
]
kjv_books = old_testament + new_testament
return kjv_books
@st.cache_data
def load_kjv_books_abv():
kjv_books_abv = {
"Genesis": "GEN",
"Exodus": "EXO",
"Leviticus": "LEV",
"Numbers": "NUM",
"Deuteronomy": "DEU",
"Joshua": "JOS",
"Judges": "JDG",
"Ruth": "RUT",
"1 Samuel": "1SA",
"2 Samuel": "2SA",
"1 Kings": "1KI",
"2 Kings": "2KI",
"1 Chronicles": "1CH",
"2 Chronicles": "2CH",
"Ezra": "EZR",
"Nehemiah": "NEH",
"Esther": "EST",
"Job": "JOB",
"Psalms": "PSA",
"Proverbs": "PRO",
"Ecclesiastes": "ECC",
"Song of Solomon": "SNG",
"Isaiah": "ISA",
"Jeremiah": "JER",
"Lamentations": "LAM",
"Ezekiel": "EZK",
"Daniel": "DAN",
"Hosea": "HOS",
"Joel": "JOL",
"Amos": "AMO",
"Obadiah": "OBA",
"Jonah": "JON",
"Micah": "MIC",
"Nahum": "NAM",
"Habakkuk": "HAB",
"Zephaniah": "ZEP",
"Haggai": "HAG",
"Zechariah": "ZEC",
"Malachi": "MAL",
"Matthew": "MAT",
"Mark": "MRK",
"Luke": "LUK",
"John": "JHN",
"Acts": "ACT",
"Romans": "ROM",
"1 Corinthians": "1CO",
"2 Corinthians": "2CO",
"Galatians": "GAL",
"Ephesians": "EPH",
"Philippians": "PHP",
"Colossians": "COL",
"1 Thessalonians": "1TH",
"2 Thessalonians": "2TH",
"1 Timothy": "1TI",
"2 Timothy": "2TI",
"Titus": "TIT",
"Philemon": "PHM",
"Hebrews": "HEB",
"James": "JAS",
"1 Peter": "1PE",
"2 Peter": "2PE",
"1 John": "1JN",
"2 John": "2JN",
"3 John": "3JN",
"Jude": "JUD",
"Revelation": "REV"
}
return kjv_books_abv
@st.cache_data
def load_kjv_countries():
kjv_countries = [
'Israel', 'State of Palestine', 'Egypt', 'Jordan', 'Lebanon', 'Syria',
'Iraq', 'Iran',
# 'Saudi Arabia', 'Kuwait', 'United Arab Emirates', 'Oman', 'Yemen',
# 'Libya', 'Tunisia',
# 'Sudan', 'Eritrea', 'Djibouti', 'Somalia', 'Uganda', 'Mozambique',
'Türkiye', 'Cyprus', 'Greece', 'Italy'
# 'North Macedonia', 'Malta', 'Spain', 'Croatia',
# 'Armenia', 'Azerbaijan', 'Georgia',
# 'Pakistan', 'India', 'Sri Lanka', 'Bangladesh', 'Indonesia'
]
return kjv_countries
@st.cache_data
def load_pos_map():
pos_map = {
"All": None,
"Adjective": "ADJ",
"Adposition": "ADP",
"Adverb": "ADV",
"Auxiliary": "AUX",
"Coordinating Conjunction": "CCONJ",
"Determiner": "DET",
"Interjection": "INTJ",
"Noun": "NOUN",
"Numeral": "NUM",
"Particle": "PART",
"Pronoun": "PRON",
"Proper Noun": "PROPN",
"Punctuation": "PUNCT",
"Subordinating Conjunction": "SCONJ",
"Symbol": "SYM",
"Verb": "VERB"
}
return pos_map
# @st.cache_data
# def load_fig_cr_cd():
# with open("models/figure_cr_cd.json", 'r') as f:
# figure_cr_cd_json = f.read()
# fig = go.Figure(json.loads(figure_cr_cd_json))
# return fig
# @st.cache_data
# def load_fig_cr_sd():
# with open("models/figure_cr_sd.json", 'r') as f:
# figure_cr_sd_json = f.read()
# fig = go.Figure(json.loads(figure_cr_sd_json))
# return fig
# @st.cache_data
# def load_fig_cr_hm():
# with open("models/figure_cr_hm.json", 'r') as f:
# figure_cr_hm_json = f.read()
# fig = go.Figure(json.loads(figure_cr_hm_json))
# return fig
# @st.cache_data
# def load_fig_cr_ng():
# with open("models/figure_cr_ng.json", 'r') as f:
# figure_cr_ng_json = f.read()
# fig = go.Figure(json.loads(figure_cr_ng_json))
# return fig
# @st.cache_data
# def load_fig_timeline_bc():
# with open("models/figure_timeline_ad.json", 'r') as f:
# figure_timeline_bc_json = f.read()
# fig = go.Figure(json.loads(figure_timeline_bc_json))
# return fig
# @st.cache_data
# def load_fig_timeline_ad():
# with open("models/figure_timeline_ce.json", 'r') as f:
# figure_timeline_ad_json = f.read()
# fig = go.Figure(json.loads(figure_timeline_ad_json))
# return fig
@st.cache_data
def load_fig_cr_pv():
html = open("models/network_cr_pv.html", "r", encoding="utf-8").read()
return html
# ----------------------
# Page Function (Charts)
# ----------------------
def bible_overview():
st.header("Bible Overview")
# labels = [
# "Bible",
# "Old Testament", "New Testament",
# "Torah", "Former Prophets", "Novel", "Poetry", "Latter Prophets",
# "Major Prophets", "Minor Prophets",
# "Early Church",
# "Gospels",
# "Acts",
# "Epistles",
# "Pauline Letters",
# "General Letters",
# "Prophecy",
# # Old Testament
# "Genesis", "Exodus", "Leviticus", "Numbers", "Deuteronomy",
# "Joshua", "Judges", "Ruth", "1 Samuel", "2 Samuel", "1 Kings", "2 Kings",
# "1 Chronicles", "2 Chronicles", "Ezra", "Nehemiah",
# "Tobit", "Judith", "Esther", "1 Maccabees", "2 Maccabees",
# "Job", "Psalms", "Proverbs", "Ecclesiastes", "Song of Songs", "Wisdom", "Sirach",
# "Isaiah", "Jeremiah", "Lamentations", "Baruch", "Ezekiel", "Daniel",
# "Hosea", "Joel", "Amos", "Obadiah", "Jonah", "Micah", "Nahum", "Habakkuk",
# "Zephaniah", "Haggai", "Zechariah", "Malachi",
# # New Testament
# "Matthew", "Mark", "Luke", "John",
# "Acts of The Apostles",
# "Romans", "1 Corinthians", "2 Corinthians", "Galatians", "Ephesians",
# "Philippians", "Colossians", "1 Thessalonians", "2 Thessalonians",
# "1 Timothy", "2 Timothy", "Titus", "Philemon",
# "Hebrews", "James", "1 Peter", "2 Peter", "1 John", "2 John", "3 John", "Jude",
# "Revelation",
# ""
# ]
# parents = [
# "",
# "Bible", "Bible",
# "Old Testament", "Old Testament", "Old Testament", "Old Testament", "Old Testament",
# "Latter Prophets", "Latter Prophets",
# "New Testament", "Early Church", "Early Church", "New Testament",
# "Epistles", "Epistles", "New Testament",
# # Old Testament
# "Torah", "Torah", "Torah",
# "Torah", "Torah",
# "Former Prophets", "Former Prophets", "Former Prophets",
# "Former Prophets", "Former Prophets", "Former Prophets",
# "Former Prophets", "Former Prophets", "Former Prophets",
# "Former Prophets",
# "Novel", "Novel", "Novel", "Novel", "Novel",
# "Poetry", "Poetry", "Poetry", "Poetry", "Poetry", "Poetry", "Poetry",
# "Major Prophets", "Major Prophets", "Major Prophets", "Major Prophets", "Major Prophets", "Major Prophets",
# "Minor Prophets", "Minor Prophets", "Minor Prophets", "Minor Prophets",
# "Minor Prophets", "Minor Prophets", "Minor Prophets", "Minor Prophets",
# "Minor Prophets", "Minor Prophets", "Minor Prophets", "Minor Prophets",
# # New Testament
# "Gospels", "Gospels", "Gospels",
# "Gospels",
# "Acts",
# "Pauline Letters", "Pauline Letters", "Pauline Letters", "Pauline Letters", "Pauline Letters",
# "Pauline Letters", "Pauline Letters", "Pauline Letters", "Pauline Letters",
# "Pauline Letters", "Pauline Letters", "Pauline Letters", "Pauline Letters",
# "General Letters", "General Letters",
# "General Letters", "General Letters",
# "General Letters", "General Letters",
# "General Letters", "General Letters",
# "Prophecy"
# ]
labels = [
# 1 = 1
"Bible",
# 2 = 2
"Old Testament", "New Testament",
# 3 = 5
"Torah", "Former Prophets", "Novel", "Poetry", "Latter Prophets",
# 4 = 2
"Major Prophets", "Minor Prophets",
# 5 = 3
"Early Church", "Epistles", "Prophecy",
# 6 = 2
"Gospels", "Acts",
"Synoptic Gospels",
# 7 = 2
"Pauline Letters", "General Letters",
"Pastoral Epistles",
# 8 = 5
"Genesis", "Exodus", "Leviticus", "Numbers", "Deuteronomy",
# 9 = 11
"Joshua", "Judges", "Ruth", "1 Samuel",
"2 Samuel", "1 Kings", "2 Kings", "1 Chronicles",
"2 Chronicles", "Ezra", "Nehemiah",
# 10 = 5
"Tobit", "Judith", "Esther", "1 Maccabees", "2 Maccabees",
# 11 = 7
"Job", "Psalms", "Proverbs", "Ecclesiastes",
"Song of Songs", "Wisdom", "Sirach",
# 12 = 6
"Isaiah", "Jeremiah", "Lamentations",
"Baruch", "Ezekiel", "Daniel",
# 13 = 12
"Hosea", "Joel", "Amos", "Obadiah", "Jonah",
"Micah", "Nahum", "Habakkuk", "Zephaniah", "Haggai",
"Zechariah", "Malachi",
# 14 = 4
"Matthew", "Mark", "Luke", "John",
# 15 = 1
"Acts of The Apostles",
# 16 = 13
"Romans", "1 Corinthians", "2 Corinthians", "Galatians",
"Ephesians", "Philippians", "Colossians", "1 Thessalonians",
"2 Thessalonians", "1 Timothy", "2 Timothy", "Titus", "Philemon",
# 17 = 8
"Hebrews", "James", "1 Peter", "2 Peter",
"1 John", "2 John", "3 John", "Jude",
# 18 = 1
"Revelation"
]
parents = [
# 1 =1
"",
# 2 = 2
"Bible", "Bible",
# 3 = 5
"Old Testament", "Old Testament", "Old Testament", "Old Testament", "Old Testament",
# 4 = 2
"Latter Prophets", "Latter Prophets",
# 5 = 3
"New Testament", "New Testament", "New Testament",
# 6 = 2
"Early Church", "Early Church",
"Gospels",
# 7 = 2
"Epistles", "Epistles",
"Pauline Letters",
# 8 = 5
"Torah", "Torah", "Torah", "Torah", "Torah",
# 9 = 11
"Former Prophets", "Former Prophets", "Former Prophets", "Former Prophets",
"Former Prophets", "Former Prophets", "Former Prophets", "Former Prophets",
"Former Prophets", "Former Prophets", "Former Prophets",
# 10 = 5
"Novel", "Novel", "Novel", "Novel", "Novel",
# 11 = 7
"Poetry", "Poetry", "Poetry", "Poetry",
"Poetry", "Poetry", "Poetry",
# 12 = 6
"Major Prophets", "Major Prophets", "Major Prophets",
"Major Prophets", "Major Prophets", "Major Prophets",
# 13 = 12
"Minor Prophets", "Minor Prophets", "Minor Prophets", "Minor Prophets", "Minor Prophets",
"Minor Prophets", "Minor Prophets", "Minor Prophets", "Minor Prophets", "Minor Prophets",
"Minor Prophets", "Minor Prophets",
# 14 = 4
"Synoptic Gospels", "Synoptic Gospels", "Synoptic Gospels", "Gospels",
# 15 = 1
"Acts",
# 16 = 13
"Pauline Letters", "Pauline Letters", "Pauline Letters", "Pauline Letters",
"Pauline Letters", "Pauline Letters", "Pauline Letters", "Pauline Letters",
"Pauline Letters", "Pastoral Epistles", "Pastoral Epistles", "Pastoral Epistles", "Pauline Letters",
# 17 = 8
"General Letters", "General Letters", "General Letters", "General Letters",
"General Letters", "General Letters", "General Letters", "General Letters",
# 18 = 1
"Prophecy"
]
# st.write(len(labels))
# st.write(len(parents))
# st.dataframe(
# pd.DataFrame(
# {
# 'labels': labels,
# 'parents': parents
# }
# )
# )
values = [
# 1 = 1
100,
# 2 = 2
85, 75,
# 3 = 5
20, 40, 20, 30, 55,
# 4 = 2
25, 45,
# 5 = 3
20, 60, 10,
# 6 = 2
17.5, 10,
15,
# 7 = 2
45, 25,
15,
5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5
]
fig = go.Figure(go.Sunburst(
labels=labels,
parents=parents,
marker=dict(
colors=values,
colorscale='Reds'
),
branchvalues="total",
))
fig.update_traces(insidetextorientation="radial")
fig.update_layout(
width=1200,
height=1200,
margin=dict(t=0, l=0, r=0, b=0),
)
# with st.container(border=True):
st.plotly_chart(fig, use_container_width=True)
def verses_book():
st.header("# of Verses per Book")
df = load_kjv_clean().copy()
kjv_books = load_kjv_books()
verse_count_per_book = df.groupby("book_name").size().reset_index(name="verse_count")
verse_count_per_book.rename(columns={"book_name": "Book", "verse_count": "# of Verses"}, inplace=True)
# chart_verse_count = (
# alt.Chart(verse_count_per_book)
# .mark_bar()
# .encode(
# x=alt.X("# of Verses:Q", title="# of Verses"),
# y=alt.Y("Book:N", sort=kjv_books, title="Book"),
# tooltip=["Book", "# of Verses"],
# )
# .properties(
# # title="# of Verses per Book",
# width='container',
# height=1500)
# .interactive()
# )
chart_verse_count = go.Figure(
data=[
go.Bar(
x=verse_count_per_book["# of Verses"],
y=verse_count_per_book["Book"],
orientation='h',
text=verse_count_per_book["# of Verses"],
hovertemplate="Book: %{y}<br># of Verses: %{x}<extra></extra>",
marker=dict(
color=verse_count_per_book["# of Verses"],
colorscale="Reds",
cmin=0,
cmax=1500
)
)
]
)
chart_verse_count.update_layout(
xaxis_title="# of Verses",
yaxis_title="Book",
yaxis=dict(categoryorder="array", categoryarray=list(reversed(kjv_books))),
height=1500
)
# st.write("")
# st.write("")
# with st.container(border=True):
# st.altair_chart(chart_verse_count, use_container_width=True, theme="streamlit")
st.plotly_chart(chart_verse_count, use_container_width=True, selection_mode="points")
# st.feedback("thumbs")
# mention(
# label="GitHub Repo: verneylmavt/st-kjv-vis",
# icon="github",
# url="https://github.com/verneylmavt/st-kjv-vis"
# )
# mention(
# label="Other ML Tasks",
# icon="streamlit",
# url="https://verneylogyt.streamlit.app/"
# )
def chapters_book():
st.header("# of Chapters per Book")
df = load_kjv_clean().copy()
kjv_books = load_kjv_books()
chapter_count_per_book = df.groupby("book_name")["chapter_number"].max().reset_index()
chapter_count_per_book.rename(columns={"book_name": "Book", "chapter_number": "# of Chapters"}, inplace=True)
# chart_chapter_count = (
# alt.Chart(chapter_count_per_book)
# .mark_bar()
# .encode(
# x=alt.X("# of Chapters:Q", title="# of Chapters"),
# y=alt.Y("Book:N", sort=kjv_books, title="Book"),
# tooltip=["Book", "# of Chapters"],
# )
# .properties(
# # title="# of Chapters per Book",
# width='container',
# height=1500)
# .interactive()
# )
chart_chapter_count = go.Figure(
data=[
go.Bar(
x=chapter_count_per_book["# of Chapters"],
y=chapter_count_per_book["Book"],
orientation='h',
text=chapter_count_per_book["# of Chapters"],
hovertemplate="Book: %{y}<br># of Chapters: %{x}<extra></extra>",
marker=dict(
color=chapter_count_per_book["# of Chapters"],
colorscale="Reds",
cmin=0,
cmax=75
)
)
]
)
chart_chapter_count.update_layout(
xaxis_title="# of Chapters",
yaxis_title="Book",
yaxis=dict(categoryorder="array", categoryarray=list(reversed(kjv_books))),
height=1500
)
# st.write("")
# st.write("")
# with st.container(border=True):
# st.altair_chart(chart_chapter_count, use_container_width=True, theme="streamlit")
st.plotly_chart(chart_chapter_count, use_container_width=True, selection_mode="points")
# st.feedback("thumbs")
# mention(
# label="GitHub Repo: verneylmavt/st-kjv-vis",
# icon="github",
# url="https://github.com/verneylmavt/st-kjv-vis"
# )
# mention(
# label="Other ML Tasks",
# icon="streamlit",
# url="https://verneylogyt.streamlit.app/"
# )
def verses_chapter():
st.header("# of Verses per Chapter")
df = load_kjv_clean().copy()
kjv_books = load_kjv_books()
chapter_verse_counts = df.groupby(["book_name", "chapter_number"]).size().reset_index(name="verse_count")
chapter_verse_counts.rename(columns={"book_name": "Book", "chapter_number": "Chapter", "verse_count": "# of Verses"}, inplace=True)
# chart_verse_heatmap = (
# alt.Chart(chapter_verse_counts)
# .mark_rect()
# .encode(
# x=alt.X("Chapter:O", title="Chapter"),
# y=alt.Y("Book:N", sort=kjv_books, title="Book"),
# color=alt.Color("# of Verses:Q", title="# of Verses", scale=alt.Scale(scheme="blues")),
# tooltip=["Book", "Chapter", "# of Verses"],
# )
# .properties(
# # title="# of Verses per Chapter",
# width='container',
# height=1500)
# .interactive()
# )
chart_verse_heatmap = go.Figure(
data=[
go.Heatmap(
x=chapter_verse_counts["Chapter"],
y=chapter_verse_counts["Book"],
z=chapter_verse_counts["# of Verses"],
colorscale="Reds",
colorbar=dict(title="# of Verses"),
zmin=0,
zmax=100,
hovertemplate="Book: %{y}<br>Chapter: %{x}<br># of Verses: %{z}<extra></extra>"
)
]
)
chart_verse_heatmap.update_layout(
xaxis_title="Chapter",
yaxis_title="Book",
yaxis=dict(categoryorder="array", categoryarray=list(reversed(kjv_books))),
height=1500
)
# st.write("")
# st.write("")
# with st.container(border=True):
# st.altair_chart(chart_verse_heatmap, use_container_width=True, theme="streamlit")
st.plotly_chart(chart_verse_heatmap, use_container_width=True, selection_mode="points")
# st.feedback("thumbs")
# mention(
# label="GitHub Repo: verneylmavt/st-kjv-vis",
# icon="github",
# url="https://github.com/verneylmavt/st-kjv-vis"
# )
# mention(
# label="Other ML Tasks",
# icon="streamlit",
# url="https://verneylogyt.streamlit.app/"
# )
def lex_rich_book():
st.header("Lexical Richness (Unique-to-Total Word Ratio) per Book")
df = load_kjv_clean().copy()
kjv_books = load_kjv_books()
def calculate_lexical_richness(df):
lexical_data = []
for book, verses in df.groupby("book_name"):
all_text = " ".join(verses["verse_text"])
words = all_text.split()
total_words = len(words)
unique_words = len(set(words))
lexical_richness = unique_words / total_words if total_words > 0 else 0
lexical_data.append({"book_name": book, "lexical_richness": lexical_richness})
return pd.DataFrame(lexical_data)
lexical_richness_df = calculate_lexical_richness(df)
lexical_richness_df.rename(columns={"book_name": "Book", "lexical_richness": "Lexical Richness"}, inplace=True)
# chart_lexical_richness = (
# alt.Chart(lexical_richness_df)
# .mark_bar()
# .encode(
# x=alt.X("Lexical Richness:Q", title="Lexical Richness (Unique-to-Total Word Ratio)"),
# y=alt.Y("Book:N", title="Book", sort=kjv_books),
# tooltip=["Book", "Lexical Richness"]
# )
# .properties(
# # title="Lexical Richness by Book",
# width='container',
# height=1500)
# .interactive()
# )
chart_lexical_richness = go.Figure(
data=[
go.Bar(
x=lexical_richness_df["Lexical Richness"],
y=lexical_richness_df["Book"],
orientation='h',
text=lexical_richness_df["Lexical Richness"],
hovertemplate="Book: %{y}<br>Lexical Richness: %{x}<extra></extra>",
marker=dict(
color=lexical_richness_df["Lexical Richness"],
colorscale="Reds",
cmin=0,
cmax=0.6
)
)
]
)
chart_lexical_richness.update_layout(
xaxis_title="Lexical Richness (Unique-to-Total Word Ratio)",
yaxis_title="Book",
yaxis=dict(categoryorder="array", categoryarray=list(reversed(kjv_books))),
height=1500
)
# st.write("")
# st.write("")
# with st.container(border=True):
# st.altair_chart(chart_lexical_richness, use_container_width=True, theme="streamlit")
st.plotly_chart(chart_lexical_richness, use_container_width=True, selection_mode="points")
def snt_book():
st.header("Sentiment Analysis per Book")
sentiment_by_book = load_sentiment_by_book().copy()
kjv_books = load_kjv_books()
chart_sentiment_by_book = go.Figure(
data=[
go.Bar(
x=sentiment_by_book["Average Sentiment"],
y=sentiment_by_book["Book"],
orientation='h',
text=sentiment_by_book["Average Sentiment"],
hovertemplate="Book: %{y}<br>Average Sentiment: %{x}<extra></extra>",
marker=dict(
color=sentiment_by_book["Average Sentiment"],
colorscale="RdBu",
cmin=-0.2,
cmax=0.5
)
)
]
)
chart_sentiment_by_book.update_layout(
xaxis_title="Average Sentiment",
yaxis_title="Book",
yaxis=dict(categoryorder="array", categoryarray=list(reversed(kjv_books))), # Ensure Genesis appears first
height=1500
)
# st.write("")
# st.write("")
# with st.container(border=True):
# st.altair_chart(chart_sentiment_by_book, use_container_width=True, theme="streamlit")
st.plotly_chart(chart_sentiment_by_book, use_container_width=True, selection_mode="points")
# st.divider()
def word_cloud():
st.header("Word Cloud")
nlp = load_spacy()
df = load_kjv_clean().copy()
kjv_books = load_kjv_books()
pos_map = load_pos_map()
def generate_word_cloud(book_name, df, pos_tag=None, bg="White"):
book_verses = df[df["book_name"] == book_name]["verse_text"]
if book_verses.empty:
print(f"No Verses in Book: {book_name}")
return
text = " ".join(book_verses)
if pos_tag is None:
texts = text
else:
doc = nlp(text)
tokens = [token.text for token in doc if token.pos_ == pos_tag]
texts = " ".join(tokens)
number = random.randint(1, 7)
coloring = np.array(Image.open(f"assets/wordcloud/{number}.png"))
stopwords = set(STOPWORDS)
wordcloud = WordCloud(
background_color="white",
mask = coloring,
stopwords=stopwords,
repeat = False
)
wordcloud.generate(texts)
image_colors = ImageColorGenerator(coloring)
wordcloud.recolor(color_func=image_colors)
wordcloud_image = wordcloud.to_image()
def remove_bg(wordcloud_image):
img = wordcloud_image.convert("RGBA")
datas = img.getdata()
new_data = []
for item in datas:
if item[0] == 255 and item[1] == 255 and item[2] == 255:
new_data.append((255, 255, 255, 0))
else:
new_data.append(item)
img.putdata(new_data)
return img
if bg == "White":
img = wordcloud_image
elif bg == "Transparent":
img = remove_bg(wordcloud_image)
return img
with st.form(key='word_sims_form'):
book_name = st.selectbox(
"Book",
kjv_books,
)
pos_tag = st.selectbox(
"Part of Speech",
list(pos_map.keys()),
)
bg = st.selectbox(
"Background",
["White", "Transparent"],
)
submit_button = st.form_submit_button(label='Generate')
if submit_button:
if book_name and pos_tag:
wordcloud_image = generate_word_cloud(book_name, df, pos_tag=pos_map[pos_tag], bg=bg)
st.image(wordcloud_image, use_container_width=True)
st.feedback("thumbs")
mention(
label="GitHub Repo: verneylmavt/st-kjv-vis",
icon="github",
url="https://github.com/verneylmavt/st-kjv-vis"
)
mention(
label="Other ML Tasks",
icon="streamlit",
url="https://verneylogyt.streamlit.app/"
)
def bib_sites():
st.header("Bible Sites")
df = load_kjv_locs_all().copy()
df['country'] = df['country'].replace('-', 'State of Palestine')
# st.dataframe(df.head())
kjv_books = load_kjv_books()
kjv_books_abv = load_kjv_books_abv()
kjv_countries = load_kjv_countries()
with st.container(border=True):
selected_books = st.multiselect("Books", kjv_books, default=kjv_books)
selected_countries = st.multiselect("Countries", kjv_countries, default=kjv_countries)
filtered_df = df[(df['book_name'].isin(selected_books)) & (df['country'].isin(selected_countries))].copy()
filtered_df["count"] = filtered_df.groupby(["latitude", "longitude"])["latitude"].transform("count")
min_count, max_count = filtered_df["count"].min(), filtered_df["count"].max()
filtered_df["color_intensity"] = np.interp(filtered_df["count"], [min_count, max_count], [50, 255])
# st.dataframe(filtered_df)
if filtered_df.empty:
pass
else:
layer = pdk.Layer(
"ScatterplotLayer",
data=filtered_df,
get_position=["longitude", "latitude"],
get_fill_color="[255, 0, 0, color_intensity]",
get_radius=500,
pickable=True,
)
mid_lat = filtered_df["latitude"].mean()
mid_long = filtered_df["longitude"].mean()
view_state = pdk.ViewState(
longitude=mid_long,
latitude=mid_lat,
zoom=5,
pitch=0,
)
tooltip = {
"html": (
"<b>Verse:</b> {book_name} {chapter_number}:{verse_number}<br/>"
"<b>Scripture:</b> \"{verse_text}\"<br/>"
"<b>Version:</b> KJV (King James Version)<br/>"
"<b>Ancient Place:</b> {name_id_ancient}<br/>"
"<b>Modern Place:</b> {name_id_modern}<br/>"
"<b>----------</b><br/>"
# "<b>Address:</b> {formatted_address}<br/>"
# "<b>Latitude:</b> {latitude}<br/>"
# "<b>Longitude:</b> {longitude}<br/>"
"<b>District / County:</b> {administrative_area_level_2}<br/>"
"<b>State / Province:</b> {administrative_area_level_1}<br/>"
"<b>Country:</b> {country}<br/>"
"<b>----------</b><br/>"
"<b>Google Maps:</b> <a href='https://www.google.com/maps/place/{latitude},{longitude}' target='_blank'>{name_id_modern}</a> <br/>"
"<b>Bible.com:</b> <a href='https://www.bible.com/bible/1/{kjv_books_abv[book_name]}.{chapter_number}.{verse_number}' target='_blank'>{book_name} {chapter_number}:{verse_number}</a>"
),
"style": {
"backgroundColor": "steelblue",
"color": "white"
}
}
deck = pdk.Deck(
layers=[layer],
map_provider="carto",
initial_view_state=view_state,
height = 1000,
tooltip=tooltip,
)
st.pydeck_chart(deck)
st.feedback("thumbs")
mention(
label="GitHub Repo: verneylmavt/st-kjv-vis",
icon="github",
url="https://github.com/verneylmavt/st-kjv-vis"
)
mention(
label="Other ML Tasks",
icon="streamlit",
url="https://verneylogyt.streamlit.app/"
)
st.divider()
container = st.container(border=True)
container.markdown("""
__Source__: OpenBible.info - Bible Geocoding
__URL__: https://www.openbible.info/geo/
""")