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import numpy as np
import os
import gradio as gr
os.environ["WANDB_DISABLED"] = "true"
from datasets import load_dataset, load_metric
from transformers import (
AutoConfig,
# AutoModelForSequenceClassification,
AutoTokenizer,
TrainingArguments,
logging,
pipeline
)
# model_name =
# tokenizer = AutoTokenizer.from_pretrained(model_name)
# config = AutoConfig.from_pretrained(model_name)
# pipe = pipeline("text-classification")
# pipe("This restaurant is awesome")
label2id = {
"LABEL_0": "negative",
"LABEL_1": "neutral",
"LABEL_2": "positive"
}
analyzer = pipeline(
"sentiment-analysis", model="thak123/Cro-Frida", tokenizer="EMBEDDIA/crosloengual-bert"
)
def predict_sentiment(x):
return label2id[analyzer(x)[0]["label"]]
interface = gr.Interface(
fn=predict_sentiment,
inputs='text',
outputs=['text'],
title='Croatian Movie reviews Sentiment Analysis',
examples= ["Volim kavu","Ne volim kavu"],
description='Get the positive/neutral/negative sentiment for the given input.'
)
interface.launch(inline = False)
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