| 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("./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() |