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Update app.py
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app.py
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@@ -8,9 +8,9 @@ from sklearn.manifold import TSNE
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# import tensorflow
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from gensim.models import Word2Vec
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import pandas as pd
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import threading
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import matplotlib.pyplot as plt
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import squarify
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import numpy as np
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import re
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import urllib.request
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@@ -24,21 +24,25 @@ st.set_page_config(page_title="OncoDigger", page_icon=":microscope:", layout="wi
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'About': "OncoDigger is a Natural Language Processing (NLP) that harnesses Word2Vec to mine"
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" insight from pubmed abstracts. Created by Jimmie E. Fata, PhD, [email protected]"})
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analytics_code = '''
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<head>
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<!-- Google tag (gtag.js) -->
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<script async src="https://www.googletagmanager.com/gtag/js?id=G-EKFSW65C2P"></script>
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<script>
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</script>
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</head>
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'''
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# Define the HTML and CSS styles
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st.markdown("""
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@@ -98,8 +102,8 @@ st.markdown("---")
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# if authenticate(password):
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opt = st.sidebar.radio("Select a PubMed Corpus", options=(
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'Breast Cancer corpus', 'Skin Cancer corpus', 'Lung Cancer corpus', 'Colorectal Cancer corpus',
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'
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'Kidney Cancer corpus'
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))
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# if opt == "Clotting corpus":
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# model_used = ("pubmed_model_clotting")
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@@ -145,10 +149,10 @@ if opt == "Uterine Cancer corpus":
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model_used = ("uterine_cancer_pubmed_model")
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num_abstracts = 72634
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database_name = "Uterine_cancer"
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if opt == "
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model_used = ("
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num_abstracts =
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database_name = "
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st.header(f":blue[{database_name} Pubmed corpus.]")
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text_input_value = st.text_input(f"Enter one term to search within the {database_name} corpus")
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# import tensorflow
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from gensim.models import Word2Vec
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import pandas as pd
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# import threading
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# import matplotlib.pyplot as plt
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# import squarify
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import numpy as np
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import re
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import urllib.request
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'About': "OncoDigger is a Natural Language Processing (NLP) that harnesses Word2Vec to mine"
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" insight from pubmed abstracts. Created by Jimmie E. Fata, PhD, [email protected]"})
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# analytics_code = '''
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# <head>
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# <!-- Google tag (gtag.js) -->
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# <script async src="https://www.googletagmanager.com/gtag/js?id=G-EKFSW65C2P"></script>
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# <script>
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# window.dataLayer = window.dataLayer || [];
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# function gtag(){dataLayer.push(arguments);}
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# gtag('js', new Date());
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#
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# gtag('config', 'G-EKFSW65C2P');
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# </script>
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# </head>
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# '''
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#
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# html(analytics_code, height=0)
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from google_analytics_component.google_analytics import google_analytics
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google_analytics()
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# Define the HTML and CSS styles
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st.markdown("""
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# if authenticate(password):
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opt = st.sidebar.radio("Select a PubMed Corpus", options=(
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'Breast Cancer corpus', 'Skin Cancer corpus', 'Lung Cancer corpus', 'Colorectal Cancer corpus',
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'Lymphoma Cancer corpus', 'Prostate Cancer corpus', 'Uterine Cancer corpus', 'Urinary Cancer corpus',
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'Kidney Cancer corpus', 'Cervical Cancer corpus'
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))
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# if opt == "Clotting corpus":
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# model_used = ("pubmed_model_clotting")
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model_used = ("uterine_cancer_pubmed_model")
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num_abstracts = 72634
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database_name = "Uterine_cancer"
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if opt == "Cervical Cancer corpus":
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model_used = ("cervical_cancer_pubmed_model")
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num_abstracts = 43327
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database_name = "Cervical_cancer"
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st.header(f":blue[{database_name} Pubmed corpus.]")
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text_input_value = st.text_input(f"Enter one term to search within the {database_name} corpus")
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