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import streamlit as st
import pickle
import re
from sklearn.feature_extraction.text import CountVectorizer
with open('count_vectorizer.pkl','rb')as vectorizer_file:
count_vectorizer = pickle.load(vectorizer_file)
with open('nb_classifier.pkl','rb')as classifier_file:
nb_classifier = pickle.load(classifier_file)
def process_text(text):
text = text.lower()
text = re.sub(r'http\S+', '', text)
text = re.sub(r'@[a-zA-Z0-9_]+', '', text)
text = re.sub(r'#', '', text)
text = re.sub(r'[^a-zA-Z\s]', '', text)
return text
sentiment_mapping = {
"Negative" : "Negative 😔",
"Positive" : "Positive 😊",
"Neutral" : "Neutral 🙄",
"Irrelevant" : "Irrelevant 🤷♂️"
}
def main():
col1 , col2 , col3 ,col4 = st.columns([1,1,3,1])
with col3:
st.image("./pngwing.com (1).png" , width=100)
st.title("Twitter Sentiment Classifier")
st.write("Enter twitter tweet below :")
input_text = st.text_area("Input Text :","")
if st.button("Predict"):
cleaned_text = process_text(input_text)
vectorizer_text = count_vectorizer.transform([cleaned_text])
sentiment_prediction = nb_classifier.predict(vectorizer_text)[0]
predicted_sentiment = sentiment_mapping.get(sentiment_prediction , "Unknown Sentiment")
st.write("Predicted Sentimen :")
st.title(predicted_sentiment)
if __name__ == "__main__":
main() |