Spaces:
Sleeping
Sleeping
File size: 1,889 Bytes
e710478 a5b267d 4b096e3 e710478 a5b267d e710478 a5b267d e710478 a5b267d e710478 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 |
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
DEVICE = config.device
def get_sentiment(input_text):
result = sentiment(input_text)
return f"result: {result[0]['label']}", f"score: {result[0]['score']}"
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=3
)
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)
print(outputs)
return outputs[0]
interface = gr.Interface(
fn=sentence_prediction,
inputs='text',
outputs=['text', 'text'],
title='Sentiment Analysis',
description='Get the positive/negative sentiment for the given input.'
)
interface.launch(inline = False) |