import gradio as gr
from datasets import load_dataset
import random


README = """
    # Movie Review Score Discriminator
    It is a program that classifies whether it is positive or negative by entering movie reviews.
    You can choose between the Korean version and the English version.
    ## Usage

"""


model_name = "roberta-base"
learning_rate = 5e-5
batch_size_train = 64
step = 1900


id2label = {0: "NEGATIVE", 1: "POSITIVE"}
label2id = {"NEGATIVE": 0, "POSITIVE": 1}


title = "Movie Review Score Discriminator"
description = "It is a program that classifies whether it is positive or negative by entering movie reviews. You can choose between the Korean version and the English version."


imdb_dataset = load_dataset('imdb')
examples = []
# examples = ["the greatest musicians ", "cold movie "]
for i in range(3):
    idx = random.randrange(len(imdb_dataset['train']))
    examples.append(imdb_dataset['train'][idx]['text'])



def fn(text):
    return "hello, " + text


demo1 = gr.Interface.load("models/cardiffnlp/twitter-roberta-base-sentiment", inputs="text", outputs="text", 
                         title=title, theme="peach",
                         allow_flagging="auto",
                         description=description, examples=examples)
# demo = gr.Interface(fn=greet, inputs="text", outputs="text")

# demo2 = gr.Interface(fn=greet, inputs="text", outputs="text", 
#                          title=title, theme="peach",
#                          allow_flagging="auto",
#                          description=description, examples=examples)

here = gr.Interface(fn,
                     inputs= gr.inputs.Textbox( lines=1, placeholder=None, default="", label=None),
                     outputs='text',
                     title="Sentiment analysis of movie reviews",
                     description=description,
                     theme="peach",
                     allow_flagging="auto",
                     flagging_dir='flagging records')


demo3 = gr.Interface.load("models/mdj1412/movie_review_score_discriminator_eng", inputs="text", outputs="text", 
                         title=title, theme="peach",
                         allow_flagging="auto",
                         description=description, examples=examples)
    
if __name__ == "__main__":
    # here.launch()
    demo3.launch()