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DistilBERT-Sentiment-Analysis / app.py

perman2011

Update app.py

bab1cdc 5 months ago

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	import streamlit as st
	from transformers import DistilBertTokenizer, DistilBertModel
	import logging
	logging.basicConfig(level=logging.ERROR)
	import torch

	MAX_LEN = 100
	device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
	tokenizer = DistilBertTokenizer.from_pretrained('distilbert-base-uncased', truncation=True, do_lower_case=True)

	class DistilBERTClass(torch.nn.Module):
	def __init__(self):
	super(DistilBERTClass, self).__init__()
	self.l1 = DistilBertModel.from_pretrained("distilbert-base-uncased")
	self.pre_classifier = torch.nn.Linear(768, 768)
	self.dropout = torch.nn.Dropout(0.1)
	self.classifier = torch.nn.Linear(768, 1)

	def forward(self, input_ids, attention_mask, token_type_ids):
	output_1 = self.l1(input_ids=input_ids, attention_mask=attention_mask)
	hidden_state = output_1[0]
	pooler = hidden_state[:, 0]
	pooler = self.pre_classifier(pooler)
	pooler = torch.nn.ReLU()(pooler)
	pooler = self.dropout(pooler)
	output = self.classifier(pooler)
	return output


	model_DB = DistilBERTClass()
	loaded_model_path = './model_DB_1.pt'
	model_DB.load_state_dict(torch.load(loaded_model_path, map_location=torch.device('cpu')))
	model_DB.to(device)


	def sentiment_analysis_DB(input):
	inputs = tokenizer.encode_plus(
	input,
	None,
	add_special_tokens=True,
	max_length=100,
	pad_to_max_length=True,
	return_token_type_ids=True
	)
	ids = torch.tensor([inputs['input_ids']]) # Convert to PyTorch tensor
	mask = torch.tensor([inputs['attention_mask']]) # Convert to PyTorch tensor
	token_type_ids = torch.tensor([inputs["token_type_ids"]]) # Convert to PyTorch tensor

	# Assuming model_DB is a PyTorch model
	output = model_DB(ids, mask, token_type_ids)
	print('Raw output is ', output)

	sigmoid_output = torch.sigmoid(output)
	print('Sigmoid output is ', sigmoid_output)

	# Assuming you want to use a threshold of 0.5
	result = 1 if sigmoid_output.item() > 0.5 else 0

	return result

	# Streamlit app
	st.title("Sentiment Analysis App")

	# User input
	user_input = st.text_area("Enter some text:")

	# Button to trigger sentiment analysis
	if st.button("Analyze Sentiment"):
	# Perform sentiment analysis
	result = sentiment_analysis_DB(user_input)

	# Display result
	if result == 1:
	st.success("Positive sentiment detected!")
	else:
	st.error("Negative sentiment detected.")