app.py

"""
from transformers import pipeline
import gradio as gr
import torch

model = "neuralmind/bert-base-portuguese-cased"
pipe = pipeline('sentiment-analysis', model=model)

def get_sentiment(input_text):
  return pipe(input_text)

iface = gr.Interface(fn=get_sentiment,
                     inputs='text',
                     outputs=['text'],
                     title='Sentiment Analysis',
                     description='Obtenha o sentimento do texto de entrada:'
                    )

iface.launch(inline=False)"""

from transformers import pipeline
import gradio as gr
import torch

model = "neuralmind/bert-base-portuguese-cased"
pipe = pipeline('sentiment-analysis', model=model)

def get_sentiment(input_text):
  return pipe(input_text)

results = pipe(input_text)

# Extract the label and score
label = results[0]['label']
score = results[0]['score']

threshold = 0.5

if label == 'LABEL_1' and score > sentiment_threshold:  # Positive sentiment
    return 'POSITIVO'
else: label == 'LABEL_0' and score <= sentiment_threshold:  # Negative sentiment
    return 'NEGATIVO'



iface = gr.Interface(fn=get_sentiment,
                     inputs='text',
                     outputs='text',
                     title='Sentiment Analysis',
                     description='Obtenha o sentimento do texto de entrada:'
                    )

iface.launch(inline=False)