import streamlit as st
import tensorflow as tf

from transformers import pipeline

model_ckpt = "Yah216/Sentiment_Analysis_CAMelBERT_msa_sixteenth_HARD"

pipe = pipeline("text-classification", model_ckpt)
#from transformers import AutoTokenizer, TFAutoModelForSequenceClassification
  
#tokenizer = AutoTokenizer.from_pretrained("Yah216/Sentiment_Analysis_CAMelBERT_msa_sixteenth_HARD")

#model = TFAutoModelForSequenceClassification.from_pretrained("Yah216/Sentiment_Analysis_CAMelBERT_msa_sixteenth_HARD")

#labels= model.config.label2id

text = st.text_area("Enter some text in arabic language!") 
if text: 
   #out = tf.math.softmax(model(tokenizer(text, padding=True, truncation=True, return_tensors="np")).logits, axis = -1)
  # res = out.numpy()
   
  # labels['NEGATIVE'] = res[0,0]
   #labels['NEUTRAL'] = res[0,1]
   #labels['POSITIVE'] = res[0,2]
  # st.json(labels)
  st.json(pipe(text))