Spaces:
Sleeping
Sleeping
Palak Deb Patra commited on
Upload 4 files
Browse files
Tagify.py
ADDED
|
@@ -0,0 +1,154 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
"""Topic Modelling and Labelling App"""
|
| 2 |
+
|
| 3 |
+
import base64
|
| 4 |
+
import heapq
|
| 5 |
+
import re
|
| 6 |
+
|
| 7 |
+
# Importing packages
|
| 8 |
+
import gensim
|
| 9 |
+
import streamlit as st
|
| 10 |
+
from gensim import corpora, models
|
| 11 |
+
from Tags import industries
|
| 12 |
+
|
| 13 |
+
|
| 14 |
+
# ...
|
| 15 |
+
# Topic Modelling on a given text
|
| 16 |
+
def preprocess_text(text):
|
| 17 |
+
# Replace this with your own preprocessing code
|
| 18 |
+
# This example simply tokenizes the text and removes stop words
|
| 19 |
+
tokens = gensim.utils.simple_preprocess(text)
|
| 20 |
+
stop_words = gensim.parsing.preprocessing.STOPWORDS
|
| 21 |
+
preprocessed_text = [[token for token in tokens if token not in stop_words]]
|
| 22 |
+
return preprocessed_text
|
| 23 |
+
|
| 24 |
+
|
| 25 |
+
def perform_topic_modeling(transcript_text, num_topics=5, num_words=10):
|
| 26 |
+
# Preprocess the transcript text
|
| 27 |
+
# Replace this with your own preprocessing code
|
| 28 |
+
preprocessed_text = preprocess_text(transcript_text)
|
| 29 |
+
# Create a dictionary of all unique words in the transcripts
|
| 30 |
+
dictionary = corpora.Dictionary(preprocessed_text)
|
| 31 |
+
# Convert the preprocessed transcripts into a bag-of-words representation
|
| 32 |
+
corpus = [dictionary.doc2bow(text) for text in preprocessed_text]
|
| 33 |
+
# Train an LDA model with the specified number of topics
|
| 34 |
+
lda_model = models.LdaModel(
|
| 35 |
+
corpus=corpus, id2word=dictionary, num_topics=num_topics
|
| 36 |
+
)
|
| 37 |
+
# Extract the most probable words for each topic
|
| 38 |
+
Topics = []
|
| 39 |
+
for idx, Topic in lda_model.print_topics(-1, num_words=num_words):
|
| 40 |
+
# Extract the top words for each topic and store in a list
|
| 41 |
+
topic_words = [
|
| 42 |
+
word.split("*")[1].replace('"', "").strip() for word in Topic.split("+")
|
| 43 |
+
]
|
| 44 |
+
Topics.append((f"Topic {idx}", topic_words))
|
| 45 |
+
return Topics
|
| 46 |
+
|
| 47 |
+
|
| 48 |
+
def label_topic(labelling_text):
|
| 49 |
+
"""
|
| 50 |
+
Given a piece of text, this function returns the top five industry labels that best match the topics discussed
|
| 51 |
+
in the text.
|
| 52 |
+
"""
|
| 53 |
+
# Count the number of occurrences of each keyword in the text for each industry
|
| 54 |
+
counts = {}
|
| 55 |
+
for industry, keywords in industries.items():
|
| 56 |
+
count = sum(
|
| 57 |
+
[
|
| 58 |
+
1
|
| 59 |
+
for keyword in keywords
|
| 60 |
+
if re.search(r"\b{}\b".format(keyword), labelling_text, re.IGNORECASE)
|
| 61 |
+
]
|
| 62 |
+
)
|
| 63 |
+
counts[industry] = count
|
| 64 |
+
# Get the top five industries based on their counts
|
| 65 |
+
top_industries = heapq.nlargest(5, counts, key=counts.get)
|
| 66 |
+
|
| 67 |
+
# If only one industry was found, return it
|
| 68 |
+
if len(top_industries) == 1:
|
| 69 |
+
return top_industries[0]
|
| 70 |
+
# If five industries were found, return them both
|
| 71 |
+
else:
|
| 72 |
+
return top_industries
|
| 73 |
+
|
| 74 |
+
|
| 75 |
+
# ...
|
| 76 |
+
|
| 77 |
+
# Streamlit Code
|
| 78 |
+
st.set_page_config(layout="wide")
|
| 79 |
+
|
| 80 |
+
# Font Style
|
| 81 |
+
with open("font.css") as f:
|
| 82 |
+
st.markdown("<style>{}</style>".format(f.read()), unsafe_allow_html=True)
|
| 83 |
+
|
| 84 |
+
|
| 85 |
+
# Display Background
|
| 86 |
+
def add_bg_from_local(image_file):
|
| 87 |
+
with open(image_file, "rb") as image_file:
|
| 88 |
+
encoded_string = base64.b64encode(image_file.read())
|
| 89 |
+
st.markdown(
|
| 90 |
+
f"""
|
| 91 |
+
<style>
|
| 92 |
+
.stApp {{
|
| 93 |
+
background-image: url(data:image/{"png"};base64,{encoded_string.decode()});
|
| 94 |
+
background-size: cover;
|
| 95 |
+
}}
|
| 96 |
+
</style>
|
| 97 |
+
""",
|
| 98 |
+
unsafe_allow_html=True,
|
| 99 |
+
)
|
| 100 |
+
|
| 101 |
+
|
| 102 |
+
add_bg_from_local("Images/background.png")
|
| 103 |
+
# Main content
|
| 104 |
+
st.markdown(
|
| 105 |
+
"""
|
| 106 |
+
<style>
|
| 107 |
+
.tagify-title {
|
| 108 |
+
font-size: 62px;
|
| 109 |
+
text-align: center;
|
| 110 |
+
transition: transform 0.2s ease-in-out;
|
| 111 |
+
}
|
| 112 |
+
.tagify-title span {
|
| 113 |
+
transition: color 0.2s ease-in-out;
|
| 114 |
+
}
|
| 115 |
+
.tagify-title:hover span {
|
| 116 |
+
color: #f5fefd; /* Hover color */
|
| 117 |
+
}
|
| 118 |
+
.tagify-title:hover {
|
| 119 |
+
transform: scale(1.15);
|
| 120 |
+
}
|
| 121 |
+
</style>
|
| 122 |
+
""",
|
| 123 |
+
unsafe_allow_html=True,
|
| 124 |
+
)
|
| 125 |
+
|
| 126 |
+
text = "Tagify" # Text to be styled
|
| 127 |
+
colored_text = ''.join(
|
| 128 |
+
['<span style="color: hsl({}, 70%, 50%);">{}</span>'.format(20 + (i * 30 / len(text)), char) for i, char in
|
| 129 |
+
enumerate(text)])
|
| 130 |
+
colored_text_with_malt = colored_text + ' <span style="color: hsl(40, 70%, 50%);">☲</span>'
|
| 131 |
+
st.markdown(f'<h1 class="tagify-title">{colored_text_with_malt}</h1>', unsafe_allow_html=True)
|
| 132 |
+
|
| 133 |
+
st.markdown(
|
| 134 |
+
'<h2 style="font-size:30px;color: #F5FEFD; text-align: center;">Topic Modelling and Labelling</h2>',
|
| 135 |
+
unsafe_allow_html=True,
|
| 136 |
+
)
|
| 137 |
+
|
| 138 |
+
input_text = st.text_area("Paste your Input Text", height=200)
|
| 139 |
+
if st.button("Analyze Text"):
|
| 140 |
+
col1, col2 = st.columns([2, 2])
|
| 141 |
+
with col1:
|
| 142 |
+
st.info("Text is below")
|
| 143 |
+
st.write(input_text)
|
| 144 |
+
with col2:
|
| 145 |
+
# Perform topic modeling on the transcript text
|
| 146 |
+
topics = perform_topic_modeling(input_text)
|
| 147 |
+
# Display the resulting topics in the app
|
| 148 |
+
st.info("Topics in the Text")
|
| 149 |
+
for topic in topics:
|
| 150 |
+
st.success(f"{topic[0]}: {', '.join(topic[1])}", icon="✅")
|
| 151 |
+
# Label the text with the top five industries
|
| 152 |
+
label = label_topic(input_text)
|
| 153 |
+
st.info("Top Five Industries")
|
| 154 |
+
st.success(f"{', '.join(label)}", icon="✅")
|
Tags.py
ADDED
|
@@ -0,0 +1,680 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
# Topic labeling
|
| 2 |
+
|
| 3 |
+
insurance_keywords = [
|
| 4 |
+
"actuary",
|
| 5 |
+
"claims",
|
| 6 |
+
"coverage",
|
| 7 |
+
"deductible",
|
| 8 |
+
"policyholder",
|
| 9 |
+
"premium",
|
| 10 |
+
"underwriter",
|
| 11 |
+
"risk assessment",
|
| 12 |
+
"insurable interest",
|
| 13 |
+
"loss ratio",
|
| 14 |
+
"reinsurance",
|
| 15 |
+
"actuarial tables",
|
| 16 |
+
"property damage",
|
| 17 |
+
"liability",
|
| 18 |
+
"flood insurance",
|
| 19 |
+
"term life insurance",
|
| 20 |
+
"whole life insurance",
|
| 21 |
+
"health insurance",
|
| 22 |
+
"auto insurance",
|
| 23 |
+
"homeowners insurance",
|
| 24 |
+
"marine insurance",
|
| 25 |
+
"crop insurance",
|
| 26 |
+
"catastrophe insurance",
|
| 27 |
+
"umbrella insurance",
|
| 28 |
+
"pet insurance",
|
| 29 |
+
"travel insurance",
|
| 30 |
+
"professional liability insurance",
|
| 31 |
+
"disability insurance",
|
| 32 |
+
"long-term care insurance",
|
| 33 |
+
"annuity",
|
| 34 |
+
"pension plan",
|
| 35 |
+
"group insurance",
|
| 36 |
+
"insurtech",
|
| 37 |
+
"insured",
|
| 38 |
+
"insurer",
|
| 39 |
+
"subrogation",
|
| 40 |
+
"adjuster",
|
| 41 |
+
"third-party administrator",
|
| 42 |
+
"excess and surplus lines",
|
| 43 |
+
"captives",
|
| 44 |
+
"workers compensation",
|
| 45 |
+
"insurance fraud",
|
| 46 |
+
"health savings account",
|
| 47 |
+
"health maintenance organization",
|
| 48 |
+
"preferred provider organization",
|
| 49 |
+
]
|
| 50 |
+
|
| 51 |
+
finance_keywords = [
|
| 52 |
+
"asset",
|
| 53 |
+
"liability",
|
| 54 |
+
"equity",
|
| 55 |
+
"capital",
|
| 56 |
+
"portfolio",
|
| 57 |
+
"dividend",
|
| 58 |
+
"financial statement",
|
| 59 |
+
"balance sheet",
|
| 60 |
+
"income statement",
|
| 61 |
+
"cash flow statement",
|
| 62 |
+
"statement of retained earnings",
|
| 63 |
+
"financial ratio",
|
| 64 |
+
"valuation",
|
| 65 |
+
"bond",
|
| 66 |
+
"stock",
|
| 67 |
+
"mutual fund",
|
| 68 |
+
"exchange-traded fund",
|
| 69 |
+
"hedge fund",
|
| 70 |
+
"private equity",
|
| 71 |
+
"venture capital",
|
| 72 |
+
"mergers and acquisitions",
|
| 73 |
+
"initial public offering",
|
| 74 |
+
"secondary market",
|
| 75 |
+
"primary market",
|
| 76 |
+
"securities",
|
| 77 |
+
"derivative",
|
| 78 |
+
"option",
|
| 79 |
+
"futures",
|
| 80 |
+
"forward contract",
|
| 81 |
+
"swaps",
|
| 82 |
+
"commodities",
|
| 83 |
+
"credit rating",
|
| 84 |
+
"credit score",
|
| 85 |
+
"credit report",
|
| 86 |
+
"credit bureau",
|
| 87 |
+
"credit history",
|
| 88 |
+
"credit limit",
|
| 89 |
+
"credit utilization",
|
| 90 |
+
"credit counseling",
|
| 91 |
+
"credit card",
|
| 92 |
+
"debit card",
|
| 93 |
+
"ATM",
|
| 94 |
+
"bankruptcy",
|
| 95 |
+
"foreclosure",
|
| 96 |
+
"debt consolidation",
|
| 97 |
+
"taxes",
|
| 98 |
+
"tax return",
|
| 99 |
+
"tax deduction",
|
| 100 |
+
"tax credit",
|
| 101 |
+
"tax bracket",
|
| 102 |
+
"taxable income",
|
| 103 |
+
]
|
| 104 |
+
|
| 105 |
+
banking_capital_markets_keywords = [
|
| 106 |
+
"bank",
|
| 107 |
+
"credit union",
|
| 108 |
+
"savings and loan association",
|
| 109 |
+
"commercial bank",
|
| 110 |
+
"investment bank",
|
| 111 |
+
"retail bank",
|
| 112 |
+
"wholesale bank",
|
| 113 |
+
"online bank",
|
| 114 |
+
"mobile banking",
|
| 115 |
+
"checking account",
|
| 116 |
+
"savings account",
|
| 117 |
+
"money market account",
|
| 118 |
+
"certificate of deposit",
|
| 119 |
+
"loan",
|
| 120 |
+
"mortgage",
|
| 121 |
+
"home equity loan",
|
| 122 |
+
"line of credit",
|
| 123 |
+
"credit card",
|
| 124 |
+
"debit card",
|
| 125 |
+
"ATM",
|
| 126 |
+
"automated clearing house",
|
| 127 |
+
"wire transfer",
|
| 128 |
+
"ACH",
|
| 129 |
+
"SWIFT",
|
| 130 |
+
"international banking",
|
| 131 |
+
"foreign exchange",
|
| 132 |
+
"forex",
|
| 133 |
+
"currency exchange",
|
| 134 |
+
"central bank",
|
| 135 |
+
"Federal Reserve",
|
| 136 |
+
"interest rate",
|
| 137 |
+
"inflation",
|
| 138 |
+
"deflation",
|
| 139 |
+
"monetary policy",
|
| 140 |
+
"fiscal policy",
|
| 141 |
+
"quantitative easing",
|
| 142 |
+
"securities",
|
| 143 |
+
"stock",
|
| 144 |
+
"bond",
|
| 145 |
+
"mutual fund",
|
| 146 |
+
"exchange-traded fund",
|
| 147 |
+
"hedge fund",
|
| 148 |
+
"private equity",
|
| 149 |
+
"venture capital",
|
| 150 |
+
"investment management",
|
| 151 |
+
"portfolio management",
|
| 152 |
+
"wealth management",
|
| 153 |
+
"financial planning",
|
| 154 |
+
]
|
| 155 |
+
|
| 156 |
+
healthcare_life_sciences_keywords = [
|
| 157 |
+
"medical device",
|
| 158 |
+
"pharmaceutical",
|
| 159 |
+
"biotechnology",
|
| 160 |
+
"clinical trial",
|
| 161 |
+
"FDA",
|
| 162 |
+
"healthcare provider",
|
| 163 |
+
"healthcare plan",
|
| 164 |
+
"healthcare insurance",
|
| 165 |
+
"patient",
|
| 166 |
+
"doctor",
|
| 167 |
+
"nurse",
|
| 168 |
+
"pharmacist",
|
| 169 |
+
"hospital",
|
| 170 |
+
"clinic",
|
| 171 |
+
"healthcare system",
|
| 172 |
+
"healthcare policy",
|
| 173 |
+
"public health",
|
| 174 |
+
"healthcare IT",
|
| 175 |
+
"electronic health record",
|
| 176 |
+
"telemedicine",
|
| 177 |
+
"personalized medicine",
|
| 178 |
+
"genomics",
|
| 179 |
+
"proteomics",
|
| 180 |
+
"clinical research",
|
| 181 |
+
"drug development",
|
| 182 |
+
"drug discovery",
|
| 183 |
+
"medicine",
|
| 184 |
+
"health",
|
| 185 |
+
]
|
| 186 |
+
|
| 187 |
+
law_keywords = [
|
| 188 |
+
"law",
|
| 189 |
+
"legal",
|
| 190 |
+
"attorney",
|
| 191 |
+
"lawyer",
|
| 192 |
+
"litigation",
|
| 193 |
+
"arbitration",
|
| 194 |
+
"dispute resolution",
|
| 195 |
+
"contract law",
|
| 196 |
+
"intellectual property",
|
| 197 |
+
"corporate law",
|
| 198 |
+
"labor law",
|
| 199 |
+
"tax law",
|
| 200 |
+
"real estate law",
|
| 201 |
+
"environmental law",
|
| 202 |
+
"criminal law",
|
| 203 |
+
"family law",
|
| 204 |
+
"immigration law",
|
| 205 |
+
"bankruptcy law",
|
| 206 |
+
]
|
| 207 |
+
|
| 208 |
+
sports_keywords = [
|
| 209 |
+
"sports",
|
| 210 |
+
"football",
|
| 211 |
+
"basketball",
|
| 212 |
+
"baseball",
|
| 213 |
+
"hockey",
|
| 214 |
+
"soccer",
|
| 215 |
+
"golf",
|
| 216 |
+
"tennis",
|
| 217 |
+
"olympics",
|
| 218 |
+
"athletics",
|
| 219 |
+
"coaching",
|
| 220 |
+
"sports management",
|
| 221 |
+
"sports medicine",
|
| 222 |
+
"sports psychology",
|
| 223 |
+
"sports broadcasting",
|
| 224 |
+
"sports journalism",
|
| 225 |
+
"esports",
|
| 226 |
+
"fitness",
|
| 227 |
+
]
|
| 228 |
+
|
| 229 |
+
media_keywords = [
|
| 230 |
+
"media",
|
| 231 |
+
"entertainment",
|
| 232 |
+
"film",
|
| 233 |
+
"television",
|
| 234 |
+
"radio",
|
| 235 |
+
"music",
|
| 236 |
+
"news",
|
| 237 |
+
"journalism",
|
| 238 |
+
"publishing",
|
| 239 |
+
"public relations",
|
| 240 |
+
"advertising",
|
| 241 |
+
"marketing",
|
| 242 |
+
"social media",
|
| 243 |
+
"digital media",
|
| 244 |
+
"animation",
|
| 245 |
+
"graphic design",
|
| 246 |
+
"web design",
|
| 247 |
+
"video production",
|
| 248 |
+
]
|
| 249 |
+
|
| 250 |
+
manufacturing_keywords = [
|
| 251 |
+
"manufacturing",
|
| 252 |
+
"production",
|
| 253 |
+
"assembly",
|
| 254 |
+
"logistics",
|
| 255 |
+
"supply chain",
|
| 256 |
+
"quality control",
|
| 257 |
+
"lean manufacturing",
|
| 258 |
+
"six sigma",
|
| 259 |
+
"industrial engineering",
|
| 260 |
+
"process improvement",
|
| 261 |
+
"machinery",
|
| 262 |
+
"automation",
|
| 263 |
+
"aerospace",
|
| 264 |
+
"automotive",
|
| 265 |
+
"chemicals",
|
| 266 |
+
"construction materials",
|
| 267 |
+
"consumer goods",
|
| 268 |
+
"electronics",
|
| 269 |
+
"semiconductors",
|
| 270 |
+
]
|
| 271 |
+
|
| 272 |
+
automobile_keywords = [
|
| 273 |
+
"automotive",
|
| 274 |
+
"cars",
|
| 275 |
+
"trucks",
|
| 276 |
+
"SUVs",
|
| 277 |
+
"electric vehicles",
|
| 278 |
+
"hybrid vehicles",
|
| 279 |
+
"autonomous " "vehicles",
|
| 280 |
+
"car manufacturing",
|
| 281 |
+
"automotive design",
|
| 282 |
+
"car dealerships",
|
| 283 |
+
"auto parts",
|
| 284 |
+
"vehicle maintenance",
|
| 285 |
+
"car rental",
|
| 286 |
+
"fleet management",
|
| 287 |
+
"telematics",
|
| 288 |
+
]
|
| 289 |
+
|
| 290 |
+
telecom_keywords = [
|
| 291 |
+
"telecom",
|
| 292 |
+
"telecommunications",
|
| 293 |
+
"wireless",
|
| 294 |
+
"networks",
|
| 295 |
+
"internet",
|
| 296 |
+
"broadband",
|
| 297 |
+
"fiber optics",
|
| 298 |
+
"5G",
|
| 299 |
+
"telecom infrastructure",
|
| 300 |
+
"telecom equipment",
|
| 301 |
+
"VoIP",
|
| 302 |
+
"satellite communications",
|
| 303 |
+
"mobile devices",
|
| 304 |
+
"smartphones",
|
| 305 |
+
"telecom services",
|
| 306 |
+
"telecom regulation",
|
| 307 |
+
"telecom policy",
|
| 308 |
+
]
|
| 309 |
+
|
| 310 |
+
digital_world_keywords = [
|
| 311 |
+
"Artificial intelligence",
|
| 312 |
+
"Machine learning",
|
| 313 |
+
"Data Science",
|
| 314 |
+
"Big Data",
|
| 315 |
+
"Cloud Computing",
|
| 316 |
+
"Cybersecurity",
|
| 317 |
+
"Information security",
|
| 318 |
+
"Network security",
|
| 319 |
+
"Blockchain",
|
| 320 |
+
"Cryptocurrency",
|
| 321 |
+
"Internet of things",
|
| 322 |
+
"IoT",
|
| 323 |
+
"Web development",
|
| 324 |
+
"Mobile development",
|
| 325 |
+
"Frontend development",
|
| 326 |
+
"Backend development",
|
| 327 |
+
"Software engineering",
|
| 328 |
+
"Software development",
|
| 329 |
+
"Programming",
|
| 330 |
+
"Database",
|
| 331 |
+
"Data analytics",
|
| 332 |
+
"Business intelligence",
|
| 333 |
+
"DevOps",
|
| 334 |
+
"Agile",
|
| 335 |
+
"Scrum",
|
| 336 |
+
"Product management",
|
| 337 |
+
"Project management",
|
| 338 |
+
"IT consulting",
|
| 339 |
+
"IT service management",
|
| 340 |
+
"ERP",
|
| 341 |
+
"CRM",
|
| 342 |
+
"SaaS",
|
| 343 |
+
"PaaS",
|
| 344 |
+
"IaaS",
|
| 345 |
+
"Virtualization",
|
| 346 |
+
"Artificial reality",
|
| 347 |
+
"AR",
|
| 348 |
+
"Virtual reality",
|
| 349 |
+
"VR",
|
| 350 |
+
"Gaming",
|
| 351 |
+
"E-commerce",
|
| 352 |
+
"Digital marketing",
|
| 353 |
+
"SEO",
|
| 354 |
+
"SEM",
|
| 355 |
+
"Content marketing",
|
| 356 |
+
"Social media marketing",
|
| 357 |
+
"User experience",
|
| 358 |
+
"UX design",
|
| 359 |
+
"UI design",
|
| 360 |
+
"Cloud-native",
|
| 361 |
+
"Microservices",
|
| 362 |
+
"Serverless",
|
| 363 |
+
"Containerization",
|
| 364 |
+
]
|
| 365 |
+
technology_keywords = [
|
| 366 |
+
"technology",
|
| 367 |
+
"innovation",
|
| 368 |
+
"research",
|
| 369 |
+
"development",
|
| 370 |
+
"software",
|
| 371 |
+
"hardware",
|
| 372 |
+
"artificial intelligence",
|
| 373 |
+
"machine learning",
|
| 374 |
+
"data science",
|
| 375 |
+
"big data",
|
| 376 |
+
"cloud computing",
|
| 377 |
+
"cybersecurity",
|
| 378 |
+
"blockchain",
|
| 379 |
+
"internet of things",
|
| 380 |
+
"IoT",
|
| 381 |
+
"web development",
|
| 382 |
+
"mobile development",
|
| 383 |
+
"data analytics",
|
| 384 |
+
"business intelligence",
|
| 385 |
+
"virtual reality",
|
| 386 |
+
"VR",
|
| 387 |
+
"augmented reality",
|
| 388 |
+
"AR",
|
| 389 |
+
"gaming",
|
| 390 |
+
"e-commerce",
|
| 391 |
+
"digital marketing",
|
| 392 |
+
]
|
| 393 |
+
|
| 394 |
+
healthcare_keywords = [
|
| 395 |
+
"healthcare",
|
| 396 |
+
"medical",
|
| 397 |
+
"medicine",
|
| 398 |
+
"hospital",
|
| 399 |
+
"clinic",
|
| 400 |
+
"doctor",
|
| 401 |
+
"nurse",
|
| 402 |
+
"pharmacist",
|
| 403 |
+
"patient care",
|
| 404 |
+
"healthcare system",
|
| 405 |
+
"public health",
|
| 406 |
+
"healthcare policy",
|
| 407 |
+
"telemedicine",
|
| 408 |
+
"electronic health records",
|
| 409 |
+
"medical devices",
|
| 410 |
+
"clinical trials",
|
| 411 |
+
"pharmaceuticals",
|
| 412 |
+
]
|
| 413 |
+
|
| 414 |
+
education_keywords = [
|
| 415 |
+
"education",
|
| 416 |
+
"teaching",
|
| 417 |
+
"learning",
|
| 418 |
+
"school",
|
| 419 |
+
"university",
|
| 420 |
+
"college",
|
| 421 |
+
"student",
|
| 422 |
+
"teacher",
|
| 423 |
+
"curriculum",
|
| 424 |
+
"online education",
|
| 425 |
+
"e-learning",
|
| 426 |
+
"distance learning",
|
| 427 |
+
"educational technology",
|
| 428 |
+
"learning management system",
|
| 429 |
+
"educational resources",
|
| 430 |
+
]
|
| 431 |
+
|
| 432 |
+
energy_keywords = [
|
| 433 |
+
"energy",
|
| 434 |
+
"renewable energy",
|
| 435 |
+
"solar energy",
|
| 436 |
+
"wind energy",
|
| 437 |
+
"hydropower",
|
| 438 |
+
"nuclear energy",
|
| 439 |
+
"fossil fuels",
|
| 440 |
+
"oil",
|
| 441 |
+
"natural gas",
|
| 442 |
+
"coal",
|
| 443 |
+
"electricity",
|
| 444 |
+
"energy efficiency",
|
| 445 |
+
"smart grid",
|
| 446 |
+
"sustainability",
|
| 447 |
+
]
|
| 448 |
+
|
| 449 |
+
retail_keywords = [
|
| 450 |
+
"retail",
|
| 451 |
+
"shopping",
|
| 452 |
+
"e-commerce",
|
| 453 |
+
"online shopping",
|
| 454 |
+
"brick and mortar",
|
| 455 |
+
"store",
|
| 456 |
+
"customer",
|
| 457 |
+
"consumer behavior",
|
| 458 |
+
"inventory management",
|
| 459 |
+
"supply chain",
|
| 460 |
+
"logistics",
|
| 461 |
+
"retail analytics",
|
| 462 |
+
]
|
| 463 |
+
|
| 464 |
+
hospitality_keywords = [
|
| 465 |
+
"hospitality",
|
| 466 |
+
"hotel",
|
| 467 |
+
"restaurant",
|
| 468 |
+
"tourism",
|
| 469 |
+
"travel",
|
| 470 |
+
"hospitality management",
|
| 471 |
+
"customer service",
|
| 472 |
+
"guest experience",
|
| 473 |
+
"hospitality industry",
|
| 474 |
+
"event management",
|
| 475 |
+
]
|
| 476 |
+
|
| 477 |
+
real_estate_keywords = [
|
| 478 |
+
"real estate",
|
| 479 |
+
"property",
|
| 480 |
+
"home",
|
| 481 |
+
"house",
|
| 482 |
+
"apartment",
|
| 483 |
+
"commercial property",
|
| 484 |
+
"real estate agent",
|
| 485 |
+
"real estate market",
|
| 486 |
+
"mortgage",
|
| 487 |
+
"real estate investment",
|
| 488 |
+
"property management",
|
| 489 |
+
"housing market",
|
| 490 |
+
"rental properties",
|
| 491 |
+
]
|
| 492 |
+
|
| 493 |
+
agriculture_keywords = [
|
| 494 |
+
"agriculture",
|
| 495 |
+
"farming",
|
| 496 |
+
"crop",
|
| 497 |
+
"livestock",
|
| 498 |
+
"agribusiness",
|
| 499 |
+
"sustainable agriculture",
|
| 500 |
+
"precision agriculture",
|
| 501 |
+
"agricultural technology",
|
| 502 |
+
"food security",
|
| 503 |
+
]
|
| 504 |
+
|
| 505 |
+
environment_keywords = [
|
| 506 |
+
"environment",
|
| 507 |
+
"sustainability",
|
| 508 |
+
"conservation",
|
| 509 |
+
"climate change",
|
| 510 |
+
"renewable resources",
|
| 511 |
+
"ecology",
|
| 512 |
+
"green energy",
|
| 513 |
+
"eco-friendly",
|
| 514 |
+
"environmental policy",
|
| 515 |
+
"carbon footprint",
|
| 516 |
+
]
|
| 517 |
+
|
| 518 |
+
art_culture_keywords = [
|
| 519 |
+
"art",
|
| 520 |
+
"culture",
|
| 521 |
+
"creativity",
|
| 522 |
+
"music",
|
| 523 |
+
"film",
|
| 524 |
+
"literature",
|
| 525 |
+
"painting",
|
| 526 |
+
"sculpture",
|
| 527 |
+
"performing arts",
|
| 528 |
+
"cultural heritage",
|
| 529 |
+
"artistic expression",
|
| 530 |
+
]
|
| 531 |
+
|
| 532 |
+
travel_keywords = [
|
| 533 |
+
"travel",
|
| 534 |
+
"tourism",
|
| 535 |
+
"vacation",
|
| 536 |
+
"holiday",
|
| 537 |
+
"adventure",
|
| 538 |
+
"travel agency",
|
| 539 |
+
"travel planning",
|
| 540 |
+
"travel destination",
|
| 541 |
+
"sightseeing",
|
| 542 |
+
"cruise",
|
| 543 |
+
]
|
| 544 |
+
|
| 545 |
+
fashion_keywords = [
|
| 546 |
+
"fashion",
|
| 547 |
+
"clothing",
|
| 548 |
+
"apparel",
|
| 549 |
+
"style",
|
| 550 |
+
"designer",
|
| 551 |
+
"fashion trends",
|
| 552 |
+
"fashion industry",
|
| 553 |
+
"fashion show",
|
| 554 |
+
"fashion accessories",
|
| 555 |
+
"fashion retail",
|
| 556 |
+
]
|
| 557 |
+
|
| 558 |
+
architecture_keywords = [
|
| 559 |
+
"architecture",
|
| 560 |
+
"building",
|
| 561 |
+
"design",
|
| 562 |
+
"construction",
|
| 563 |
+
"architect",
|
| 564 |
+
"urban planning",
|
| 565 |
+
"architecture styles",
|
| 566 |
+
"sustainable architecture",
|
| 567 |
+
"interior design",
|
| 568 |
+
"landscape architecture",
|
| 569 |
+
]
|
| 570 |
+
|
| 571 |
+
aviation_keywords = [
|
| 572 |
+
"aviation",
|
| 573 |
+
"aircraft",
|
| 574 |
+
"airline",
|
| 575 |
+
"flight",
|
| 576 |
+
"pilot",
|
| 577 |
+
"aviation safety",
|
| 578 |
+
"aerospace",
|
| 579 |
+
"aviation technology",
|
| 580 |
+
"air traffic control",
|
| 581 |
+
"airport",
|
| 582 |
+
]
|
| 583 |
+
|
| 584 |
+
gaming_keywords = [
|
| 585 |
+
"gaming",
|
| 586 |
+
"video games",
|
| 587 |
+
"gamer",
|
| 588 |
+
"gaming industry",
|
| 589 |
+
"game development",
|
| 590 |
+
"esports",
|
| 591 |
+
"gaming community",
|
| 592 |
+
"gaming platform",
|
| 593 |
+
"online gaming",
|
| 594 |
+
"gaming tournaments",
|
| 595 |
+
]
|
| 596 |
+
|
| 597 |
+
food_beverage_keywords = [
|
| 598 |
+
"food",
|
| 599 |
+
"beverage",
|
| 600 |
+
"cuisine",
|
| 601 |
+
"restaurant",
|
| 602 |
+
"chef",
|
| 603 |
+
"culinary arts",
|
| 604 |
+
"food industry",
|
| 605 |
+
"food culture",
|
| 606 |
+
"food technology",
|
| 607 |
+
"food sustainability",
|
| 608 |
+
]
|
| 609 |
+
|
| 610 |
+
fitness_keywords = [
|
| 611 |
+
"fitness",
|
| 612 |
+
"exercise",
|
| 613 |
+
"workout",
|
| 614 |
+
"gym",
|
| 615 |
+
"fitness training",
|
| 616 |
+
"fitness equipment",
|
| 617 |
+
"health and fitness",
|
| 618 |
+
"personal training",
|
| 619 |
+
"fitness classes",
|
| 620 |
+
"wellness",
|
| 621 |
+
]
|
| 622 |
+
|
| 623 |
+
pharmaceuticals_keywords = [
|
| 624 |
+
"pharmaceuticals",
|
| 625 |
+
"drugs",
|
| 626 |
+
"medicine",
|
| 627 |
+
"pharmaceutical industry",
|
| 628 |
+
"drug development",
|
| 629 |
+
"clinical trials",
|
| 630 |
+
"pharmaceutical research",
|
| 631 |
+
"pharmacy",
|
| 632 |
+
"pharmacology",
|
| 633 |
+
"pharmaceutical manufacturing",
|
| 634 |
+
]
|
| 635 |
+
|
| 636 |
+
music_keywords = [
|
| 637 |
+
"music",
|
| 638 |
+
"musical",
|
| 639 |
+
"artist",
|
| 640 |
+
"concert",
|
| 641 |
+
"music production",
|
| 642 |
+
"music industry",
|
| 643 |
+
"music performance",
|
| 644 |
+
"music streaming",
|
| 645 |
+
"music festival",
|
| 646 |
+
"music education",
|
| 647 |
+
]
|
| 648 |
+
|
| 649 |
+
industries = {
|
| 650 |
+
"Insurance": insurance_keywords,
|
| 651 |
+
"Finance": finance_keywords,
|
| 652 |
+
"Banking": banking_capital_markets_keywords,
|
| 653 |
+
"Health": healthcare_life_sciences_keywords,
|
| 654 |
+
"Law": law_keywords,
|
| 655 |
+
"Sports": sports_keywords,
|
| 656 |
+
"Entertainment": media_keywords,
|
| 657 |
+
"Manufacturing": manufacturing_keywords,
|
| 658 |
+
"Automobile": automobile_keywords,
|
| 659 |
+
"Telecom": telecom_keywords,
|
| 660 |
+
"Digital World": digital_world_keywords,
|
| 661 |
+
"Technology": technology_keywords,
|
| 662 |
+
"Healthcare": healthcare_keywords,
|
| 663 |
+
"Education": education_keywords,
|
| 664 |
+
"Energy": energy_keywords,
|
| 665 |
+
"Retail": retail_keywords,
|
| 666 |
+
"Hospitality": hospitality_keywords,
|
| 667 |
+
"Real Estate": real_estate_keywords,
|
| 668 |
+
"Agriculture": agriculture_keywords,
|
| 669 |
+
"Environment": environment_keywords,
|
| 670 |
+
"Art & Culture": art_culture_keywords,
|
| 671 |
+
"Travel": travel_keywords,
|
| 672 |
+
"Fashion": fashion_keywords,
|
| 673 |
+
"Architecture": architecture_keywords,
|
| 674 |
+
"Aviation": aviation_keywords,
|
| 675 |
+
"Gaming": gaming_keywords,
|
| 676 |
+
"Food & Beverage": food_beverage_keywords,
|
| 677 |
+
"Fitness": fitness_keywords,
|
| 678 |
+
"Pharmaceuticals": pharmaceuticals_keywords,
|
| 679 |
+
"Music": music_keywords,
|
| 680 |
+
}
|
font.css
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
@import url('https://fonts.googleapis.com/css2?family=Open+Sans:wght@300&display=swap');
|
| 2 |
+
|
| 3 |
+
*{font-family: 'Open Sans';}
|
requirements.txt
ADDED
|
Binary file (1.62 kB). View file
|
|
|