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import gradio as gr
import transformers
import torch
from transformers import BertModel, BertTokenizer, AdamW, get_linear_schedule_with_warmup
from torch import nn, optim
from torch.utils.data import Dataset, DataLoader
import pickle
class_names = ['left', 'neutral', 'right']
PRE_TRAINED_MODEL_NAME = 'bert-base-uncased'
tokenizer = BertTokenizer.from_pretrained(PRE_TRAINED_MODEL_NAME)
MAX_LEN = 256
bert_model = BertModel.from_pretrained(PRE_TRAINED_MODEL_NAME)
class SentimentClassifier(nn.Module):
def __init__(self, n_classes):
super(SentimentClassifier, self).__init__()
self.bert = BertModel.from_pretrained(PRE_TRAINED_MODEL_NAME)
self.drop = nn.Dropout(p=0.4)
self.out = nn.Linear(self.bert.config.hidden_size, n_classes)
def forward(self, input_ids, attention_mask):
_, pooled_output = self.bert(
input_ids=input_ids,
attention_mask=attention_mask,
return_dict=False
)
output = self.drop(pooled_output)
return self.out(output)
model = SentimentClassifier(len(class_names))
model2 = torch.load("model_BERT_2", map_location=torch.device('cpu'))
def result_final(new_article):
encoded_review = tokenizer.encode_plus(
review_text,
max_length=MAX_LEN,
add_special_tokens=True,
return_token_type_ids=False,
padding="max_length",
truncation=True,
return_attention_mask=True,
return_tensors='pt',
)
input_ids = encoded_review['input_ids'].to(device)
attention_mask = encoded_review['attention_mask'].to(device)
output = model2(input_ids, attention_mask)
_, prediction = torch.max(output, dim=1)
return class_names[prediction]
iface = gr.Interface(fn = result_final, inputs = "text", outputs = ["text"], title = "News Bias Classifer")
iface.launch()
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