import numpy as np
import gradio as gr

demo = gr.Blocks()

def flip_text(x):
    return x[::-1]

def flip_image(x):
    return np.fliplr(x)

with demo:
    gr.Markdown("Flip text or image files using this demo.")
    with gr.Tabs():
        with gr.TabItem("Flip Text"):
            text_input = gr.Textbox()
            text_output = gr.Textbox()
            text_button = gr.Button("Flip")
        with gr.TabItem("Flip Image"):
            with gr.Row():
                image_input = gr.Image()
                image_output = gr.Image()
            image_button = gr.Button("Flip")

    text_button.click(flip_text, inputs=text_input, outputs=text_output)
    image_button.click(flip_image, inputs=image_input, outputs=image_output)

demo.launch()



# import ktrain
# import gradio as gr
# from gradio import Blocks, Interface, Parallel, Tabs, TabItem, Markdown
# Tabs
# gr.Blocks()
# gr.Tabs()
# gr.TabbedInterface()

"""
from gradio import Blocks, Interface, Parallel, Tabs, TabItem, Markdown, Textbox

with Blocks() as demo:
	Markdown("Text")
	with Tabs():
		with TabItem("Name 1"):
			text_input = Textbox()
			text_output = Textbox()
"""

"""
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()

"""