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import gradio as gr
from transformers import pipeline
from model import BERTBaseUncased
from tokenizer import tokenizer
import torch
from utils import label_full_decoder
import sys
import config
import dataset
import engine
from model import BERTBaseUncased
MODEL = None
T = tokenizer.TweetTokenizer(preserve_handles=True, preserve_hashes=True, preserve_case=False, preserve_url=False)
device = config.device
model = BERTBaseUncased()
model.load_state_dict(torch.load(config.MODEL_PATH, map_location=torch.device(device)))
model.to(device)
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)
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=3
)
outputs, [] = engine.predict_fn(test_data_loader, model, device)
print(outputs)
return label_full_decoder(outputs[0])
interface = gr.Interface(
fn=sentence_prediction,
inputs='text',
outputs=['text'],
title='Sentiment Analysis',
description='Get the positive/neutral/negative sentiment for the given input.'
)
interface.launch(inline = False) |