Update app.py

app.py

@@ -1,35 +1,30 @@
-# import streamlit as st
-
-# import torch as torch
-# from transformers import AutoTokenizer, AutoModelForSequenceClassification, AutoConfig
-
-
 import streamlit as st
+import torch as torch
+from transformers import AutoTokenizer, AutoModelForSequenceClassification, AutoConfig
 from transformers import pipeline
 from PIL import Image
 
-pipeline = pipeline(task="image-classification", model="julien-c/hotdog-not-hotdog")
+model_name = "peace4ever/roberta-large-finetuned-mongolian_v3"  
+pipeline = pipeline(task="sentiment-analysis", model=model_name)
 
-st.title("Hot Dog? Or Not?")
+st.title("Эерэг? Сөрөг эсвэл аль нь ч биш?")
+text = st.text_area("Өгүүлбэр оруулна уу?")
 
-file_name = st.file_uploader("Upload a hot dog candidate image")
-
-if file_name is not None:
+if text is not None:
+    
     col1, col2 = st.columns(2)
 
-    image = Image.open(file_name)
-    col1.image(image, use_column_width=True)
-    predictions = pipeline(image)
+    predictions = pipeline(text)
 
     col2.header("Probabilities")
     for p in predictions:
         col2.subheader(f"{ p['label'] }: { round(p['score'] * 100, 1)}%")
 
-# model_name = "peace4ever/roberta-large-finetuned-mongolian_v3"  
+
 # tokenizer = AutoTokenizer.from_pretrained(model_name)
 # model = AutoModelForSequenceClassification.from_pretrained(model_name)
 
-# text = st.text_area("Өгүүлбэр оруулна уу?")
+# 
 # encoded_input = tokenizer(text, return_tensors="pt")
 # output = model(**encoded_input)