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| import streamlit as st | |
| import pickle | |
| import re | |
| from sklearn.feature_extraction.text import TfidfVectorizer | |
| from nltk.corpus import stopwords | |
| import nltk | |
| from ntscraper import Nitter | |
| # Download stopwords once, using Streamlit's caching | |
| def load_stopwords(): | |
| nltk.download('stopwords') | |
| return stopwords.words('english') | |
| # Load model and vectorizer once | |
| def load_model_and_vectorizer(): | |
| with open('model.pkl', 'rb') as model_file: | |
| model = pickle.load(model_file) | |
| with open('vectorizer.pkl', 'rb') as vectorizer_file: | |
| vectorizer = pickle.load(vectorizer_file) | |
| return model, vectorizer | |
| # Define sentiment prediction function | |
| def predict_sentiment(text, model, vectorizer, stop_words): | |
| # Preprocess text | |
| text = re.sub('[^a-zA-Z]', ' ', text) | |
| text = text.lower() | |
| text = text.split() | |
| text = [word for word in text if word not in stop_words] | |
| text = ' '.join(text) | |
| text = [text] | |
| text = vectorizer.transform(text) | |
| # Predict sentiment | |
| sentiment = model.predict(text) | |
| return "Negative" if sentiment == 0 else "Positive" | |
| # Initialize Nitter scraper | |
| def initialize_scraper(): | |
| return Nitter(log_level=1) | |
| # Function to create a colored card | |
| def create_card(tweet_text, sentiment): | |
| color = "green" if sentiment == "Positive" else "red" | |
| card_html = f""" | |
| <div style="background-color: {color}; padding: 10px; border-radius: 5px; margin: 10px 0;"> | |
| <h5 style="color: white;">{sentiment} Sentiment</h5> | |
| <p style="color: white;">{tweet_text}</p> | |
| </div> | |
| """ | |
| return card_html | |
| # Main app logic | |
| def main(): | |
| st.title("Twitter Sentiment Analysis") | |
| # Load stopwords, model, vectorizer, and scraper only once | |
| stop_words = load_stopwords() | |
| model, vectorizer = load_model_and_vectorizer() | |
| scraper = initialize_scraper() | |
| # User input: either text input or Twitter username | |
| option = st.selectbox("Choose an option", ["Input text", "Get tweets from user"]) | |
| if option == "Input text": | |
| text_input = st.text_area("Enter text to analyze sentiment") | |
| if st.button("Analyze"): | |
| sentiment = predict_sentiment(text_input, model, vectorizer, stop_words) | |
| st.write(f"Sentiment: {sentiment}") | |
| elif option == "Get tweets from user": | |
| username = st.text_input("Enter Twitter username") | |
| if st.button("Fetch Tweets"): | |
| tweets_data = scraper.get_tweets(username, mode='user', number=5) | |
| if 'tweets' in tweets_data: # Check if the 'tweets' key exists | |
| for tweet in tweets_data['tweets']: | |
| tweet_text = tweet['text'] # Access the text of the tweet | |
| sentiment = predict_sentiment(tweet_text, model, vectorizer, stop_words) # Predict sentiment of the tweet text | |
| # Create and display the colored card for the tweet | |
| card_html = create_card(tweet_text, sentiment) | |
| st.markdown(card_html, unsafe_allow_html=True) | |
| else: | |
| st.write("No tweets found or an error occurred.") | |
| if __name__ == "__main__": | |
| main() | |