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| from imports import * | |
| from data_preprocessing import * | |
| from web_scrapper import * | |
| from model import* | |
| # Download necessary NLTK data | |
| nltk.download('wordnet') | |
| nltk.download('omw-1.4') | |
| st.set_page_config(page_title="News Sentiment Analysis") | |
| st.title("News Sentiment Analysis") | |
| use_logistic = False | |
| model_used = st.radio("Select model for analysis",["Bert(Preferred)","Logistic"]) | |
| if model_used=="Logistic": | |
| use_logistic=True | |
| else: | |
| use_logistic = False | |
| if use_logistic: | |
| logistic_model() | |
| #Load the trained model and preprocessing objects | |
| logreg = joblib.load('logreg_model.pkl') | |
| with open('vectorizer.pkl', 'rb') as handle: | |
| vectorizer = pickle.load(handle) | |
| with open('label_encoder.pkl', 'rb') as handle: | |
| label_encoder = pickle.load(handle) | |
| else: | |
| model_name = "nlptown/bert-base-multilingual-uncased-sentiment" | |
| tokenizer = AutoTokenizer.from_pretrained(model_name) | |
| model = AutoModelForSequenceClassification.from_pretrained(model_name) | |
| domain = st.radio("Select Domain of news for analysis",["Sports","Tech","Politics","Entertainment","Business"],index=None) | |
| if domain!=None: | |
| if domain=="Sports": | |
| titles, links = scrape_sports(url="https://www.indiatoday.in/search/sports", driver_path='chromedriver.exe') | |
| elif domain=="Tech": | |
| titles, links = scrape_sports(url="https://indianexpress.com/section/technology/", driver_path='chromedriver.exe') | |
| elif domain=="Politics": | |
| titles, links = scrape_sports(url="https://indianexpress.com/section/political-pulse/", driver_path='chromedriver.exe') | |
| elif domain=="Entertainment": | |
| titles, links = scrape_sports(url="https://indianexpress.com/section/entertainment/", driver_path='chromedriver.exe') | |
| elif domain=="Business": | |
| titles, links = scrape_sports(url="https://indianexpress.com/section/business/", driver_path='chromedriver.exe') | |
| if use_logistic: | |
| # Clean and preprocess titles | |
| cleaned_titles = [clean(title) for title in titles] | |
| lemmatized_titles = [lemmatize_text(title) for title in cleaned_titles] | |
| transformed_titles = vectorizer.transform(lemmatized_titles) | |
| # Make predictions | |
| predictions = logreg.predict(transformed_titles) | |
| predicted_labels = label_encoder.inverse_transform(predictions) | |
| for title, link,sentiment in zip(titles, links,predicted_labels): | |
| st.write(f"Title: {title}") | |
| st.write(f"Link: {link}") | |
| st.write(f"Predicted Sentiment: {sentiment}") | |
| st.write("-----") | |
| else: | |
| # Display results | |
| for title, link in zip(titles, links): | |
| sentiment = analyze_sentiment(title,tokenizer,model) | |
| st.write(f"Title: {title}") | |
| st.write(f"Link: {link}") | |
| st.write(f"Predicted Sentiment: {sentiment}") | |
| st.write("-----") | |