app.py

import gradio as gr

from transformers import pipeline

sentiment = pipeline("sentiment-analysis")

def get_sentiment(input_text):
  return sentiment(input_text)

iface = gr.Interface(fn = get_sentiment, 
                     inputs = "text", 
                     outputs = ['text'],
                     title = 'Sentiment Analysis', 
                     description="Get Sentiment Negative/Positive for the given input")
                     
iface.launch(inline = False)
"""
import gradio as gr
from sentence_transformers import SentenceTransformer

model_Q = SentenceTransformer('flax-sentence-embeddings/multi-QA_v1-mpnet-asymmetric-Q')

def getVectors(sentences):
            vectors = []
            splitSentences = sentences.split('/*/')
            for sentence in sentences:
                        vectors.append(model_Q.encode(sentence))
            return vectors
            
interface = gr.Interface(fn = getVectors,
inputs = "text",
outputs = ['text'],
title = 'get vectors',
description = 'get vectors for search')

interface.launch(inline = False)
"""