import torch
from transformers import AutoTokenizer,AutoModelForSequenceClassification,Trainer,TrainingArguments
import torch.nn.functional as F
from datasets import load_dataset
labels = {
    'Negative':0,
    'Positive':1,
    'Neutral':2,
    'Irrelevant':3,
}
model_name = 'distilbert-base-uncased'
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name,num_labels=4)
# Set up device
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model.to(device)
# create function to predict sentence
def pre_sentiment(sentence):
    model.eval()
    inputs = tokenizer(sentence,return_tensors='pt',truncation=True,padding=True)
    inputs = {k:v.to(device) for k,v in inputs.items()}
    with torch.no_grad():
        outputs = model(**inputs)
        probs = F.softmax(outputs.logits,dim=-1)
    pre = torch.argmax(probs,dim=-1).item()
    score = torch.max(probs).item()
    inv_label = {v:k for k,v in labels.items()}
    result = inv_label.get(pre,'Unknown')
    return result
# create UI/UX
import gradio as gr

demo = gr.Interface(fn=pre_sentiment,inputs='text',outputs='text',title='Predict Sentiment')
demo.launch()