Update app.py

app.py

@@ -1,23 +1,30 @@
 import streamlit as st
-#import torch
-from transformers import DistilBertTokenizer, DistilBertForSequenceClassification
-from transformers import pipeline
-from transformers import pretrained_model
+import torch
 from transformers import AutoModelForSequenceClassification
+from transformers import AutoTokenizer, AutoModel
+from transformers import pipeline
+
+tokenizer = AutoTokenizer.from_pretrained("Michael54546/ToxicTweet")
+model = AutoModelForSequenceClassification.from_pretrained("Michael54546/ToxicTweet")
 
 #st.title("Enter Phrase: ")
 uInput = st.text_input("Enter Phrase: ")
+data = [uInput]
 
+classifier = pipeline("sentiment-analysis", model=model, tokenizer=tokenizer, return_all_scores=True)
+results = classifier(data)
 
-mymodel=AutoModelForSequenceClassification.from_pretrained("pretrained_model")
-pipe = pipeline(model=mymodel)
-data = [uInput]
-#pipeline(data)
+col1, col2, col3 = st.columns(3)
 
-col1, col2 = st.columns(2)
+for x in results:
+  for p in x:
+    #print(f"{ p['label'] }: { round(p['score'] * 100, 1)}%")
+    if(p['score']>highestscore and p['label']!='toxic'):
+      highestscore=p['score']
+      highest=p['label']
 
-predictions = pipe(data)
+col2.header("Highest Label")
+print(highest)
 
-col2.header("Probabilities")
-for p in predictions:
-    col2.subheader(f"{ p['label'] }: { round(p['score'] * 100, 1)}%")
+col3.header("Probability")
+print(f"{ round([highestscore * 100, 1)}%")