Update app.py

app.py

@@ -8,11 +8,11 @@ import postt
 from postt import postcor
 from transformers import  pipeline, TokenClassificationPipeline, BertForTokenClassification , AutoTokenizer , TextClassificationPipeline , AutoModelForSequenceClassification
 
+st.set_page_config(layout="wide")
 
 
 
-
-st.header("Knowledge extraction on Endocrine disruptors")
+st.title("Knowledge extraction: EDCs")
 st.write("This tool lets you extract relation triples concerning interactions between: endocrine disrupting chemicals, hormones, receptors and cancers.")
 st.write("It is the result of an end of studies project within ESI school and dedicated to biomedical researchers looking to extract precise information about the subject without digging into long publications.")
 
@@ -33,6 +33,7 @@ if submit and len(x) != 0:
   
   tokenizer = AutoTokenizer.from_pretrained("dmis-lab/biobert-large-cased-v1.1", truncation = True, padding=True, model_max_length=512,)
   model_checkpoint = BertForTokenClassification.from_pretrained("dexay/Ner2HgF", )
+  st.caption("Downloading models")
   model_re = AutoModelForSequenceClassification.from_pretrained("dexay/reDs3others", )
   
   token_classifier = pipeline("token-classification", tokenizer = tokenizer,model=model_checkpoint,  )
@@ -173,9 +174,8 @@ if submit and len(x) != 0:
   
   #lstSentEnc,lstSentEnt,lstSentbilbl
   
-  st.text("Entities detected.")
-  st.text("")
-  st.text("Next: Relation detection ...")
+  st.caption("Entities detected.")
+  st.caption("Next: Relation detection ...")
   
   
   # Relation extraction part
@@ -311,16 +311,16 @@ if submit and len(x) != 0:
   st.table(edccandf)
   csv = edccandf.to_csv(index=False).encode('utf-8')
   with st.sidebar:
-    st.write("You can only choose one download!!")
-    st.write("we recommed ZIP file.")
-    st.write("Download table only:")
+    st.subheader("You can only choose one download !!")
+    st.caption("we recommed ZIP file.")
+    st.write("Download table only :")
     st.download_button(
          label="Download CSV",
          data=csv,
          file_name='Relation_triples_table.csv',
          mime='text/csv',
     )
-    st.write("Download table plus separate csvs for each family of pairs:")
+    st.write("Download table plus separate csvs for each family of pairs :")
     with open("allcsvs.zip", "rb") as fp:
       btn = st.download_button(
           label="Download ZIP",