import gradio as gr import fasttext from transformers import AutoModelForSequenceClassification from transformers import AutoTokenizer import numpy as np import pandas as pd import torch 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. \ It also provides a version called ""Default"", which determines whether it is Korean or English and predicts it." class LanguageIdentification: def __init__(self): pretrained_lang_model = "./lid.176.ftz" self.model = fasttext.load_model(pretrained_lang_model) def predict_lang(self, text): predictions = self.model.predict(text, k=200) # returns top 200 matching languages return predictions LANGUAGE = LanguageIdentification() def tokenized_data(tokenizer, inputs): return tokenizer.batch_encode_plus( [inputs], return_tensors="pt", padding="max_length", max_length=64, truncation=True) examples = [] df = pd.read_csv('examples.csv', sep='\t', index_col='Unnamed: 0') np.random.seed(100) idx = np.random.choice(50, size=5, replace=False) eng_examples = [ ['Eng', df.iloc[i, 0]] for i in idx ] kor_examples = [ ['Kor', df.iloc[i, 1]] for i in idx ] examples = eng_examples + kor_examples 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) kor_file_name = "{}-{}.pt".format(kor_model_name.replace('/', '_'), kor_step) kor_state_dict = torch.load(kor_file_name) kor_model = AutoModelForSequenceClassification.from_pretrained( kor_model_name, num_labels=2, id2label=id2label, label2id=label2id, state_dict=kor_state_dict ) def builder(Lang, Text): percent_kor, percent_eng = 0, 0 text_list = Text.split(' ') # [ output_1 ] if Lang == 'Default': pred = LANGUAGE.predict_lang(Text) if '__label__en' in pred[0]: Lang = 'Eng' idx = pred[0].index('__label__en') p_eng = pred[1][idx] if '__label__ko' in pred[0]: Lang = 'Kor' idx = pred[0].index('__label__ko') p_kor = pred[1][idx] # Normalize Percentage percent_kor = p_kor / (p_kor+p_eng) percent_eng = p_eng / (p_kor+p_eng) if Lang == 'Eng': model = eng_model tokenizer = eng_tokenizer if percent_eng==0: percent_eng=1 if Lang == 'Kor': model = kor_model tokenizer = kor_tokenizer if percent_kor==0: percent_kor=1 # [ output_2 ] inputs = tokenized_data(tokenizer, Text) model.eval() with torch.no_grad(): logits = model(input_ids=inputs['input_ids'], attention_mask=inputs['attention_mask']).logits m = torch.nn.Softmax(dim=1) output = m(logits) # print(logits, output) # [ output_3 ] output_analysis = [] for word in text_list: tokenized_word = tokenized_data(tokenizer, word) with torch.no_grad(): logit = model(input_ids=tokenized_word['input_ids'], attention_mask=tokenized_word['attention_mask']).logits word_output = m(logit) if word_output[0][1] > 0.99: output_analysis.append( (word, '+++') ) elif word_output[0][1] > 0.9: output_analysis.append( (word, '++') ) elif word_output[0][1] > 0.8: output_analysis.append( (word, '+') ) elif word_output[0][1] < 0.01: output_analysis.append( (word, '---') ) elif word_output[0][1] < 0.1: output_analysis.append( (word, '--') ) elif word_output[0][1] < 0.2: output_analysis.append( (word, '-') ) else: output_analysis.append( (word, None) ) return [ {'Kor': percent_kor, 'Eng': percent_eng}, {id2label[1]: output[0][1].item(), id2label[0]: output[0][0].item()}, output_analysis ] # prediction = torch.argmax(logits, axis=1) return id2label[prediction.item()] # 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) demo = gr.Interface(builder, inputs=[gr.inputs.Dropdown(['Default', 'Eng', 'Kor']), gr.Textbox(placeholder="리뷰를 입력하시오.")], outputs=[ gr.Label(num_top_classes=3, label='Lang'), gr.Label(num_top_classes=2, label='Result'), gr.HighlightedText(label="Analysis", combine_adjacent=False) .style(color_map={"+++": "#CF0000", "++": "#FF3232", "+": "#FFD4D4", "---": "#0004FE", "--": "#4C47FF", "-": "#BEBDFF"}) ], # outputs='label', title=title, description=description, examples=examples) def fn2(): demo1.launch() with gr.Blocks() as demo1: gr.Markdown( """

Movie Review Score Discriminator

""") with gr.Accordion("Open for More!"): gr.Markdown( """ 내용은 아직 바꾸지 않았음 (형식만 참고) 문제점 : 클리어 클릭이 원하는대로 안됨 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. \ It also provides a version called ""Default"", which determines whether it is Korean or English and predicts it. """) with gr.Row(): with gr.Column(): inputs_1 = gr.inputs.Dropdown(['Default', 'Eng', 'Kor'], label='Lang') inputs_2 = gr.Textbox(placeholder="리뷰를 입력하시오.", label='Text') with gr.Row(): btn2 = gr.Button("클리어") btn = gr.Button("제출하기") with gr.Column(): output_1 = gr.Label(num_top_classes=3, label='Lang') output_2 = gr.Label(num_top_classes=2, label='Result') output_3 = gr.HighlightedText(label="Analysis", combine_adjacent=False) \ .style(color_map={"+++": "#CF0000", "++": "#FF3232", "+": "#FFD4D4", "---": "#0004FE", "--": "#4C47FF", "-": "#BEBDFF"}) btn2.click(fn=fn2) btn.click(fn=builder, inputs=[inputs_1, inputs_2], outputs=[output_1, output_2, output_3]) gr.Examples(examples, inputs=[inputs_1, inputs_2]) if __name__ == "__main__": # print(examples) # demo.launch() demo1.launch()