Update app.py

app.py

@@ -1,6 +1,5 @@
 import streamlit as st
-import torch
-# from transformers import AutoTokenizer, AutoModelForSequenceClassification, AutoConfig
+
 from transformers import pipeline
 
 model_name = "peace4ever/roberta-large-finetuned-mongolian_v3"
@@ -30,30 +29,3 @@ if text is not None:
     # probability = predictions[0]["score"]
     # col1.write(label)
     # col2.write(f"{probability:.2f}")  
-
-
-
-# tokenizer = AutoTokenizer.from_pretrained(model_name)
-# model = AutoModelForSequenceClassification.from_pretrained(model_name)
-
-# 
-# encoded_input = tokenizer(text, return_tensors="pt")
-# output = model(**encoded_input)
-
-# label_map = {"positive": 0, "negative": 1, "neutral": 2}
-
-# # Update the model configuration with custom labels
-# config = AutoConfig.from_pretrained(model_name)
-# config.label2id = {"positive": 0, "negative": 1, "neutral": 2}
-# config.id2label = {0: "positive", 1: "negative", 2: "neutral"}
-# config.save_pretrained(model_name)
-
-# predicted_label_id = torch.argmax(output.logits, dim=1).item()
-# id2label = model.config.id2label
-# predicted_label = id2label[predicted_label_id]
-
-# print("Predicted Class:", predicted_label)
-
-# st.json(predicted_label)
-
-