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import streamlit as st
import torch as torch
from transformers import AutoTokenizer, AutoModelForSequenceClassification, AutoConfig
# import requests
# from transformers import pipeline
# # Load the model using its ID or name
# model_id_or_name = "peace4ever/roberta-large-finetuned-mongolian_v4" # Update with your model ID or name
# classifier = pipeline("sentiment-analysis", model=model_id_or_name)
# # Example usage
# result = classifier("I loved Star Wars so much!")
# print(result)
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)
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)
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