app.py

import ktrain
from gradio import Blocks, Interface, Parallel, Tabs, TabItem, Markdown


"""
examples = [
	["I only get my kids the ones I got....I've turned down many so called 'vaccines'"], 
	["In child protective services, further providing for definitions, for immunity from liability"], 
	["Lol what? Measles is a real thing. Get vaccinated"]]
title = "Vaccine Sentiment Task - VS2"
desc = "Enter vaccine-related tweets to generate sentiment from 3 models (BERT, MentalBERT, PHS-BERT). Label 0='vaccine critical', 1='neutral', 2='vaccine supportive'. The three provided examples have true labels 0,1,2 respectively. For details about VS2, please refer to our paper (linked provided in the corresponding Hugging Face repository)."

predictor_bert = ktrain.load_predictor('bert')
predictor_mental = ktrain.load_predictor('mentalbert')
predictor_phs = ktrain.load_predictor('phsbert')

def BERT(text):
	results = predictor_bert.predict(str(text))
	return str(results)
	
def MentalBERT(text):
	results = predictor_mental.predict(str(text))
	return str(results)
	
def PHSBERT(text):
	results = predictor_phs.predict(str(text))
	return str(results)

bert_io = Interface(fn=BERT, inputs="text", outputs="text")
mental_io = Interface(fn=MentalBERT, inputs="text", outputs="text")
phs_io = Interface(fn=PHSBERT, inputs="text", outputs="text")

vs = Parallel(bert_io, mental_io, phs_io, 
	examples=examples, 
	title=title, 
	description=desc,
	theme="peach")
	
def model(text):
	return "Predictions unavailable - to be completed."

hm = Interface(fn=model, inputs="text", outputs="text")

# interfaces = [vs, hm]
# interface_names = ["Vaccine Sentiment Task", "Health Mention Task"]

# TabbedInterface(interfaces, interface_names).launch()

"""