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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 **
"""
)
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