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import streamlit as st
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "mrm8488/distilroberta-finetuned-financial-news-sentiment-analysis"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
# Seting the page title
st.title("Financial Sentiment Analysis")
# Adding a text input for the user to input financial news
text_input = st.text_area("Enter Financial News:", "Tesla stock is soaring after record-breaking earnings.")
# Function to perform sentiment analysis
def predict_sentiment(text):
inputs = tokenizer(text, return_tensors="pt", max_length=1022, 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
# 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(): # Checking if input is not empty or contains only whitespaces
# Showing loading spinner while processing
with st.spinner("Analyzing sentiment..."):
sentiment = predict_sentiment(text_input)
# Extracting confidence scores
inputs = tokenizer(text_input, return_tensors="pt")
outputs = model(**inputs)
confidence_scores = outputs.logits.softmax(dim=1)[0].tolist()
# 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():
inputs = tokenizer(text_input, return_tensors="pt")
outputs = model(**inputs)
raw_scores = outputs.logits[0].tolist()
st.info(f"Raw Sentiment Scores: {raw_scores}")
# footer
st.markdown(
"""
**Built with [Streamlit](https://streamlit.io/) and [Transformers](https://huggingface.co/models).**
"""
)
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