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from os import path
import streamlit as st
# import pickle
# from tensorflow import keras
import tensorflow as tf
import torch
from torch import nn
from transformers import BertModel, BertTokenizer
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
MODEL_NAME = "bert-base-cased"
MODEL_PATH = path.join(path.dirname(__file__), "bert_model.h5")
# 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(MODEL_NAME)
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)
@st.cache_resource
def load_model_and_tokenizer():
model = SentimentClassifier(3)
model.load_state_dict(torch.load(MODEL_PATH, map_location=torch.device("cpu")))
model.eval()
return model, BertTokenizer.from_pretrained("bert-base-cased")
def predict(content):
model, tokenizer = load_model_and_tokenizer()
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
encoded_review = tokenizer.encode_plus(
content,
max_length=160,
add_special_tokens=True,
return_token_type_ids=False,
pad_to_max_length=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 = model(input_ids, attention_mask)
_, prediction = torch.max(output, dim=1)
class_names = ["negative", "neutral", "positive"]
return class_names[prediction]
def main():
st.set_page_config(page_title="Sentiment Analysis", page_icon="📝")
# giving a title to our page
st.title("Sentiment analysis")
contents = st.text_area(
"Please enter reviews/sentiment/setences/contents:",
placeholder="Enter your text here",
height=200,
)
prediction = ""
# Create a prediction button
if st.button("Analyze Sentiment"):
stripped = contents.strip()
if not stripped:
st.error("Please enter some text.")
return
prediction = predict(contents)
if prediction == "positive":
st.success("This is positive 😄")
elif prediction == "negative":
st.error("This is negative 😟")
else:
st.warning("This is neutral 🙂")
upload_file = st.file_uploader("Or upload a file", type=["txt"])
if upload_file is not None:
contents = upload_file.read().decode("utf-8")
for line in contents.splitlines():
line = line.strip()
if not line:
continue
prediction = predict(line)
if prediction == "positive":
st.success(line + "\n\nThis is positive 😄")
elif prediction == "negative":
st.error(line + "\n\nThis is negative 😟")
else:
st.warning(line + "\n\nThis is neutral 🙂")
if __name__ == "__main__":
main()
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