import torch
from transformers import AutoTokenizer,AutoModelForSequenceClassification,Trainer,TrainingArguments
import torch.nn.functional as F
from datasets import load_dataset
model_name = 'thanhcong2001/Sentiment'
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name,num_labels = 2)

# set up device
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model.to(device)
def pre_sentiment(sentence):
    model.eval()
    inputs = tokenizer(sentence,return_tensors='pt',max_length=64,padding=True,truncation=True)
    inputs = {k:v 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()
    result = 'POSITIVE' if pre == 1 else 'NEGATIVE'
    return f'Result: {result} - Score: {score}'
import gradio as gr
demo = gr.Interface(fn=pre_sentiment,inputs='text',outputs='text',title='Predict sentence')
demo.launch()