|
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 = [] |
|
|
|
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) |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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__": |
|
|
|
demo3.launch() |
