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import gradio as gr
from transformers import AutoModelForSequenceClassification
from transformers import AutoTokenizer
import random
import torch
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
"""
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."
def tokenized_data(tokenizer, inputs):
return tokenizer.batch_encode_plus(
[inputs],
return_tensors="pt",
padding="max_length",
max_length=64,
truncation=True)
examples_eng = ["the greatest musicians ", "cold movie "]
examples_kor = ["긍정", "부정"]
examples = []
df = pd.read_csv('examples.csv', sep='\t', index_col='Unnamed: 0')
for i in range(2):
idx = random.randint(0, 50)
examples.append(['Eng', df.iloc[idx, 0]])
examples.append(['Kor', df.iloc[idx, 1]])
eng_model_name = "roberta-base"
eng_step = 1900
eng_tokenizer = AutoTokenizer.from_pretrained(eng_model_name)
eng_file_name = "{}-{}.pt".format(eng_model_name, eng_step)
eng_state_dict = torch.load(eng_file_name)
eng_model = AutoModelForSequenceClassification.from_pretrained(
eng_model_name, num_labels=2, id2label=id2label, label2id=label2id,
state_dict=eng_state_dict
)
kor_model_name = "klue_roberta-small"
kor_step = 2400
kor_tokenizer = AutoTokenizer.from_pretrained(kor_model_name.replace('_', '/'))
kor_file_name = "{}-{}.pt".format(kor_model_name, kor_step)
kor_state_dict = torch.load(kor_file_name)
kor_model = AutoModelForSequenceClassification.from_pretrained(
kor_model_name.replace('_', '/'), num_labels=2, id2label=id2label, label2id=label2id,
state_dict=kor_state_dict
)
def builder(lang, text):
if lang == 'Eng':
model = eng_model
tokenizer = eng_tokenizer
else:
model = kor_model
tokenizer = kor_tokenizer
inputs = tokenized_data(tokenizer, text)
model.eval()
with torch.no_grad():
logits = model(input_ids=inputs['input_ids'],
attention_mask=inputs['attention_mask']).logits
prediction = torch.argmax(logits, axis=1)
return id2label[prediction.item()]
def builder2(inputs):
return eng_model(inputs)
demo = gr.Interface(builder, inputs=[gr.inputs.Dropdown(['Eng', 'Kor']), "text"], outputs="text",
title=title, description=description, examples=examples)
# demo2 = gr.Interface(builder2, inputs="text", outputs="text",
# title=title, theme="peach",
# allow_flagging="auto",
# description=description, examples=examples)
# 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__":
# print(examples)
demo.launch()
# demo3.launch() |