Update app.py

app.py

@@ -4,12 +4,11 @@ import transformers
 from transformers import  pipeline, TokenClassificationPipeline, BertForTokenClassification , AutoTokenizer , TextClassificationPipeline , AutoModelForSequenceClassification
 
 st.header("Knowledge extraction on Endocrine disruptors")
-st.text("This tool lets you extract relation triples concerning interactions between: endocrine disrupting chemicals, hormones, receptors and cancers.")
-st.text("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.")
+st.write("This tool lets you extract relation triples concerning interactions between: endocrine disrupting chemicals, hormones, receptors and cancers.")
+st.twrite("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.")
 x = st.text_area('Entre you text on EDCs:')
 
-if x[-1] not in ".?:":
-  x += "."
+
 #model.to("cpu")
 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", )
@@ -20,6 +19,9 @@ token_classifier = pipeline("token-classification", tokenizer = tokenizer,model=
 
 st.text("Knowledge extraction is in progress ...")
 
+if x and x[-1] not in ".?:":
+  x += "."
+
 biotext = x
 
 #split document or text into sentences