Update app.py

app.py

@@ -7,6 +7,16 @@ import matplotlib.pyplot as plt
 import squarify
 import numpy as np
 
+st.set_page_config(
+    page_title="FATA4 Science",
+                page_icon=":microscope:",
+                layout="wide",
+                initial_sidebar_state="expanded",
+                menu_items={
+                    'About': "FATA4 Science is a Natural Language Processing (NLP) that ...."
+                }
+                )
+
 # Define the HTML and CSS styles
 st.markdown("""
     <style>
@@ -31,11 +41,11 @@ if opt == "Neuroblastoma corpus":
     num_abstracts = 29032
     database_name = "Neuroblastoma"
 
-
-st.header(f"Word2Vec App for {database_name} Pubmed corpus.")
+st.title(":red[Fast Acting Text Analysis (FATA) 4 Science]")
+st.markdown("---")
 st.subheader("Uncovering knowledge through Natural Language Processing (NLP)")
-st.subheader("Open sidebar to choose corpus")
 
+st.header(f"{database_name} Pubmed corpus.")
 text_input_value = st.text_input(f"Enter one term to search within the {database_name} corpus", max_chars=50)
 query = text_input_value
 query = query.lower()
@@ -43,7 +53,7 @@ query = query.lower()
 if query:
     bar = st.progress(0)
     time.sleep(.2)
-    st.caption(f":LightSkyBlue[searching {num_abstracts} {database_name} PubMed abstracts]")
+    st.caption(f":LightSkyBlue[searching {num_abstracts} {database_name} PubMed abstracts] covering 1990-2022")
     for i in range(10):
         bar.progress((i + 1) * 10)
         time.sleep(.1)
@@ -135,9 +145,23 @@ if query:
     st.pyplot(fig2)
 
     csv = df1.head(100).to_csv().encode('utf-8')
-    st.download_button(label="download top 100 genes (csv)", data=csv, file_name=f'{database_name}_genes.csv', mime='text/csv')
+    st.download_button(label="download top 100 genes (csv)", data=csv, file_name=f'{database_name}_genes.csv',
+                       mime='text/csv')
+
+DEFAULT_WIDTH = 80
+VIDEO_DATA = f"https://www.youtube.com/@NCIgov/search?query=cancer"
+
+width = st.sidebar.slider(
+    label="Width", min_value=0, max_value=100, value=DEFAULT_WIDTH, format="%d%%"
+)
+
+width = max(width, 0.01)
+side = max((100 - width) / 2, 0.01)
+
+_, container, _ = st.columns([side, width, side])
+container.video(data=VIDEO_DATA)
+
 
-# findRelationships(query, df)
 
 
 # model = gensim.models.KeyedVectors.load_word2vec_format('pubmed_model_clotting', binary=True)