app.py

import gradio as gr
from models import *
from huggingface_hub import hf_hub_download
import os
from config import *

ENTITY_REPO_ID = 'vaivTA/absa_v2_entity'
ENTITY_FILENAME = "entity_model.pt"

SENTIMENT_REPO_ID = 'vaivTA/absa_v2_sentiment'
SENTIMENT_FILENAME = "sentiment_model.pt"

print("downloading model...")
sen_model_file = hf_hub_download(repo_id=SENTIMENT_REPO_ID, filename=SENTIMENT_FILENAME)
entity_model_file = hf_hub_download(repo_id=ENTITY_REPO_ID, filename=ENTITY_FILENAME)

base_model = base_model

tokenizer = AutoTokenizer.from_pretrained(base_model)

sen_model = Classifier(base_model, num_labels=2, device='cpu', tokenizer=tokenizer)
sen_model.load_state_dict(torch.load(sen_model_file, map_location=torch.device('cpu')))

entity_model = Classifier(base_model, num_labels=2, device='cpu', tokenizer=tokenizer)
entity_model.load_state_dict(torch.load(entity_model_file, map_location=torch.device('cpu')))


def infer(test_sentence):
    # entity_model.to(device)
    # entity_model.eval()
    # sen_model.to(device)
    # sen_model.eval()
    
    form = test_sentence
    annotation = []
    
    if len(form) > 500:
        return "Too long sentence!"
    
    
    for pair in entity_property_pair:  
        
        form_ = form + "[SEP]"   
        pair_ = entity2str[pair] + "[SEP]"
        
        tokenized_data = tokenizer(form_, pair_, padding='max_length', max_length=512, truncation=True)
        
        input_ids = torch.tensor([tokenized_data['input_ids']])
        attention_mask = torch.tensor([tokenized_data['attention_mask']])
        
        first_sep = tokenized_data['input_ids'].index(2)
        last_sep = tokenized_data['input_ids'][first_sep+2:].index(2) + (first_sep + 2)        
        mask = [0] * len(tokenized_data['input_ids'])        
        for i in range(first_sep + 2, last_sep):
            mask[i] = 1     
        mask = torch.tensor([mask])
                
        with torch.no_grad():
            outputs = entity_model(input_ids, attention_mask, mask)
        ce_logits = outputs
        ce_predictions = torch.argmax(ce_logits, dim = -1)

        ce_result = tf_id_to_name[ce_predictions[0]]

        if ce_result == 'True':
            with torch.no_grad():
                outputs = sen_model(input_ids, attention_mask, mask)
            pc_logits = outputs
            pc_predictions = torch.argmax(pc_logits, dim=-1)
            pc_result = polarity_id_to_name[pc_predictions[0]]

            annotation.append(f"{pair} - {pc_result}")
            
    result = '\n'.join(annotation)
    return result

    
demo = gr.Interface(fn=infer,
             inputs=gr.Textbox(type="text", label="Input Sentence"),
             outputs=gr.Textbox(type="text", label="Result Sentence"),
             article="**리뷰 예시** : 아파트는 오래되었지만 동네가 조용하고 쾌적하여 살기에는 아주 좋습니다. 큰 마트가 주변에 없는 단점이 잇지만 이촌역이 매우 가깝고 생활권 내에 맛잇는 식당과 커피숖이 즐비합니다 ㅎㅎ"
             )

demo.launch(share=True)