app.py

import gradio as gr
import nltk
from nltk.sentiment import SentimentIntensityAnalyzer
nltk.download('vader_lexicon')
# from transformers import AutoTokenizer, AutoModelForSequenceClassification
# import torch

# pretrained = "rohanphadke/roberta-finetuned-triplebottomline"
# tokenizer = AutoTokenizer.from_pretrained(pretrained)
# model = AutoModelForSequenceClassification.from_pretrained(pretrained)
sia = SentimentIntensityAnalyzer()
# threshold = 0.5

labels = {0: 'people', 1: 'planet', 2:'profit'}
return_labels = {'people': 0.25, 'planet':0.5, 'profit':0.75}
return_sentiment = {'positive': 0.25, 'neutral':0.5, 'negative':0.75}

def greet(name):
    return "Hello " + name + "!!"

def predict_text(text):
    return return_labels, sia.polarity_scores(text)

demo = gr.Interface(fn=predict_text, inputs="text", outputs=["label", "label"])
demo.launch()