Update app.py

app.py

@@ -21,26 +21,32 @@ st.markdown("""
     </style>
     """, unsafe_allow_html=True)
 
-st.header("Word2Vec App for Clotting Pubmed Database.")
+opt=st.sidebar.radio("Select a PubMed Database", options=('Clotting', 'Neuroblastoma'))
+if opt == "Clotting":
+    model_used = ("pubmed_model_clotting")
+    database_name = "Clotting"
+if opt == "Neuroblastoma":
+    model_used = ("pubmed_model_clotting")
+    database_name = "Neuroblastoma"
 
-text_input_value = st.text_input("Enter one term to search within the Clotting database", max_chars=50)
+
+st.header(f"Word2Vec App for {database_name} Pubmed Database.")
+st.subheader("Uncovering knowledge through Natural Language Processing (NLP)")
+
+text_input_value = st.text_input(f"Enter one term to search within the {database_name} database", max_chars=50)
 query = text_input_value
 query = query.lower()
 # query = input ("Enter your keyword(s):")
 if query:
-
-    if query.isalpha():
-        bar = st.progress(0)
-        time.sleep(.2)
-        st.caption(":LightSkyBlue[searching 40123 PubMed abstracts]")
-        for i in range(10):
-            bar.progress((i + 1) * 10)
-            time.sleep(.1)
-    else:
-        st.write('Please omit numbers in term')
+    bar = st.progress(0)
+    time.sleep(.2)
+    st.caption(f":LightSkyBlue[searching 40123 {database_name} PubMed abstracts]")
+    for i in range(10):
+        bar.progress((i + 1) * 10)
+        time.sleep(.1)
 
     try:
-        model = Word2Vec.load("pubmed_model_clotting")  # you can continue training with the loaded model!
+        model = Word2Vec.load(model_used)  # you can continue training with the loaded model!
         words = list(model.wv.key_to_index)
         X = model.wv[model.wv.key_to_index]
         model2 = model.wv[query]
@@ -85,7 +91,7 @@ if query:
     plt.clf()
 
     csv = table.head(100).to_csv().encode('utf-8')
-    st.download_button(label="download top 100 words (csv)", data=csv, file_name='clotting_words.csv', mime='text/csv')
+    st.download_button(label="download top 100 words (csv)", data=csv, file_name=f'{database_name}_words.csv', mime='text/csv')
 
     # st.write(short_table)
     #
@@ -126,7 +132,7 @@ 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='clotting_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')
 
 # findRelationships(query, df)