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import gradio as gr
import torch
import torch.nn as nn
from transformers import BertModel, BertTokenizer
# define the labels for the mutli-classification model
class_names = ['Negative', 'Neutral', 'Positive']
# Build the Sentiment Classifier class
class SentimentClassifier(nn.Module):
# Constructor class
def __init__(self, n_classes):
super(SentimentClassifier, self).__init__()
self.bert = BertModel.from_pretrained('lothritz/LuxemBERT')
self.drop = nn.Dropout(p=0.3)
self.out = nn.Linear(self.bert.config.hidden_size, n_classes)
# Forward propagaion class
def forward(self, input_ids, attention_mask):
_, pooled_output = self.bert(
input_ids=input_ids,
attention_mask=attention_mask,
return_dict=False
)
# Add a dropout layer
output = self.drop(pooled_output)
return self.out(output)
# load the CNN binary classification model
model = SentimentClassifier(len(class_names))
model.load_state_dict(torch.load('./pytorch_model.bin', map_location=torch.device('cpu')))
tokenizer = BertTokenizer.from_pretrained('./')
def encode(text):
encoded_text = tokenizer.encode_plus(
text,
max_length=50,
add_special_tokens=True,
return_token_type_ids=False,
pad_to_max_length=True,
return_attention_mask=True,
return_tensors='pt',
)
return encoded_text
def classify(text):
encoded_comment = encode(text)
input_ids = encoded_comment['input_ids']
attention_mask = encoded_comment['attention_mask']
output = model(input_ids, attention_mask)
_, prediction = torch.max(output, dim=1)
return class_names[prediction]
demo = gr.Interface(fn=classify, inputs="text", outputs="text", title="Sentiment Analyser", description="Text classifer for Luxembourgish")
demo.launch() |