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import streamlit as st
from functions_preprocess import LinguisticPreprocessor, download_if_non_existent, CNN, build_vocab
import pickle
import nltk
from datasets import load_dataset
import torch
nltk.download('stopwords')
nltk.download('punkt')
download_if_non_existent('corpora/stopwords', 'stopwords')
download_if_non_existent('taggers/averaged_perceptron_tagger', 'averaged_perceptron_tagger')
download_if_non_existent('corpora/wordnet', 'wordnet')
from torchtext.data.utils import get_tokenizer
#################################################################### Streamlit interface
st.title("Movie Reviews: An NLP Sentiment analysis")
#################################################################### Cache the model loading
@st.cache_data()
def load_model():
model_pkl_file = "sentiment_model.pkl"
with open(model_pkl_file, 'rb') as file:
model = pickle.load(file)
return model
def load_cnn():
model = CNN(16236, 300, 128, [3, 8], 0.5, 2)
model.load_state_dict(torch.load('model_cnn.pkl', map_location=torch.device('cpu')))
model.eval()
return model
def predict_sentiment(text, model, vocab, torch_text = False):
tokenizer = get_tokenizer("basic_english")
if torch_text == True:
processor.transform(text)
tokens = tokenizer(text)
encoded = [vocab[token] for token in tokens]
input_tensor = torch.tensor(encoded).unsqueeze(0).to(device)
with torch.no_grad(): # No gradient needed
model.eval() # Evaluation mode
outputs = model(input_tensor)
probs = torch.softmax(outputs, dim=1)
pred_class = torch.argmax(probs, dim=1).item()
return pred_class # Return the predicted class index
else:
processor.transform(text)
prediction = model.predict([text])
return prediction
model_1 = load_model()
model_2 = load_cnn()
processor = LinguisticPreprocessor()
train_data = load_dataset('rotten_tomatoes', split='train')
vocab, tokenizer = build_vocab(train_data)
############################################################# Text input
with st.expander("Model 1: SGD Classifier"):
st.markdown("Give it a go by writing a positive or negative text, and analyze it!")
# Text input inside the expander
user_input = st.text_area("Enter text here...", key='model1_input')
if st.button('Analyze', key='model1_button'):
# Displaying output
result = predict_sentiment(user_input, model_1)
if result >= 0.5:
st.write('The sentiment is: Positive 😀', key='model1_poswrite')
else:
st.write('The sentiment is: Negative 😞', key='model1_negwrite')
with st.expander("Model 2: CNN Sentiment analysis"):
st.markdown("Give it a go by writing a positive or negative text, and analyze it!")
# Text input inside the expander
user_input = st.text_area("Enter text here...", key='model2_input')
if st.button('Analyze', key='model2_button'):
# Displaying output
result = predict_sentiment(user_input, model_2, vocab, torch_text=True)
if result >= 0.5:
st.write('The sentiment is: Positive 😀', key='model2_poswrite')
else:
st.write('The sentiment is: Negative 😞', key='model2_negwrite')
st.caption("Por @efeperro.")
stop_words = set(stopwords.words('english')) |