app.py

# import torch
from utils import label_full_decoder
import sys
import dataset
import engine
from model import BERTBaseUncased
# from tokenizer import tokenizer
import config
from transformers import pipeline, AutoTokenizer, AutoModel
import gradio as gr


# T = tokenizer.TweetTokenizer(
#     preserve_handles=True, preserve_hashes=True, preserve_case=False, preserve_url=False)

# def preprocess(text):
#     tokens = T.tokenize(text)
#     print(tokens, file=sys.stderr)
#     ptokens = []
#     for index, token in enumerate(tokens):
#         if "@" in token:
#             if index > 0:
#                 # check if previous token was mention
#                 if "@" in tokens[index-1]:
#                     pass
#                 else:
#                     ptokens.append("mention_0")
#             else:
#                 ptokens.append("mention_0")
#         else:
#             ptokens.append(token)

#     print(ptokens, file=sys.stderr)
#     return " ".join(ptokens)


def sentence_prediction(sentence):
    # sentence = preprocess(sentence)
    
    model_path = config.MODEL_PATH

    test_dataset = dataset.BERTDataset(
        review=[sentence],
        target=[0]
    )

    test_data_loader = torch.utils.data.DataLoader(
        test_dataset,
        batch_size=config.VALID_BATCH_SIZE,
        num_workers=-1
    )

    device = config.device

    model = BERTBaseUncased()
    model.load_state_dict(torch.load(
        model_path, map_location=torch.device(device)))
    model.to(device)

    outputs, [] = engine.predict_fn(test_data_loader, model, device)

    outputs =  classifier(sentence)
    
    print(outputs)
    return outputs #{"label":outputs[0]}




demo = gr.Interface(
  fn=sentence_prediction, 
  inputs='text',
  outputs='label',
)

demo.launch()