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| import streamlit as st | |
| from transformers import AutoTokenizer, AutoModelForSequenceClassification | |
| from newspaper import Article | |
| # Model and tokenizer | |
| model_name = "mrm8488/distilroberta-finetuned-financial-news-sentiment-analysis" | |
| tokenizer = AutoTokenizer.from_pretrained(model_name) | |
| model = AutoModelForSequenceClassification.from_pretrained(model_name) | |
| # Setting the page title | |
| st.title("Financial News Sentiment Analysis") | |
| # Input option: Text or URL | |
| input_option = st.radio("Choose input type:", ["Text Input", "URL Input"]) | |
| if input_option == "Text Input": | |
| text_input = st.text_area("Enter Financial News:", "DEMO : Tesla stock is soaring after record-breaking earnings.") | |
| else: | |
| url_input = st.text_input("Enter URL to scrape headline:") | |
| if url_input: | |
| try: | |
| # Scrape the headline from the URL | |
| article = Article(url_input) | |
| article.download() | |
| article.parse() | |
| text_input = article.title # Use the article's title as the headline | |
| st.success(f"Scraped Headline: {text_input}") | |
| except Exception as e: | |
| st.error(f"Failed to extract headline: {e}") | |
| text_input = "" | |
| # Function to perform sentiment analysis | |
| def predict_sentiment(text): | |
| inputs = tokenizer(text, return_tensors="pt", max_length=512, truncation=True) | |
| outputs = model(**inputs) | |
| sentiment_class = outputs.logits.argmax(dim=1).item() | |
| sentiment_mapping = {0: 'Negative', 1: 'Neutral', 2: 'Positive'} | |
| predicted_sentiment = sentiment_mapping.get(sentiment_class, 'Unknown') | |
| return predicted_sentiment, outputs.logits.softmax(dim=1)[0].tolist() | |
| # Button to trigger sentiment analysis | |
| if st.button("Analyze Sentiment"): | |
| # Checking if the input text is not empty | |
| if text_input and text_input.strip(): | |
| # Showing loading spinner while processing | |
| with st.spinner("Analyzing sentiment..."): | |
| sentiment, confidence_scores = predict_sentiment(text_input) | |
| # Considering a threshold for sentiment prediction | |
| threshold = 0.5 | |
| # Changing the success message background color based on sentiment and threshold | |
| if sentiment == 'Positive' and confidence_scores[2] > threshold: | |
| st.success(f"Sentiment: {sentiment} (Confidence: {confidence_scores[2]:.3f})") | |
| elif sentiment == 'Negative' and confidence_scores[0] > threshold: | |
| st.error(f"Sentiment: {sentiment} (Confidence: {confidence_scores[0]:.3f})") | |
| elif sentiment == 'Neutral' and confidence_scores[1] > threshold: | |
| st.info(f"Sentiment: {sentiment} (Confidence: {confidence_scores[1]:.3f})") | |
| else: | |
| st.warning("Low confidence, or sentiment not above threshold. Please try again.") | |
| else: | |
| st.warning("Please enter some valid text for sentiment analysis.") | |
| # Optional: Displaying the raw sentiment scores | |
| if st.checkbox("Show Raw Sentiment Scores"): | |
| if text_input and text_input.strip(): | |
| _, raw_scores = predict_sentiment(text_input) | |
| st.info(f"Raw Sentiment Scores: \n Negative : {raw_scores[0]} \n Positive : {raw_scores[2]} \n Neutral : {raw_scores[1]}") | |
| # footer | |
| st.markdown( | |
| """ | |
| ** Built and maintained by Swayam Mohanty ** | |
| """ | |
| ) | |