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import streamlit as st
from functions_preprocess import LinguisticPreprocessor, download_if_non_existent
import pickle
import nltk
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')
#################################################################### Streamlit interface
st.title("Movie Reviews: An NLP Sentiment analysis")
#################################################################### Cache the model loading
@st.cache_data()
def load_model(model_path):
model_pkl_file = "sentiment_model.pkl"
with open(model_pkl_file, 'rb') as file:
model = pickle.load(file)
return model
def load_cnn():
model.load_state_dict(torch.load('model_cnn.pkl'))
model.eval()
return model
def predict_sentiment(text, model):
processor.transform(text)
prediction = model.predict([text])
return prediction
model_1 = load_model()
model_2 = load_cnn()
processor = LinguisticPreprocessor()
############################################################# 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...")
if st.button('Analyze'):
# Displaying output
result = predict_sentiment(user_input, model_1)
if result >= 0.5:
st.write('The sentiment is: Positive 😀')
else:
st.write('The sentiment is: Negative 😞')
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...")
if st.button('Analyze'):
# Displaying output
result = predict_sentiment(user_input, model_2)
if result >= 0.5:
st.write('The sentiment is: Positive 😀')
else:
st.write('The sentiment is: Negative 😞')
st.caption("Por @efeperro.") |