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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)
# Set the page title
st.title("Financial Sentiment Analysis App")
# Add 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")
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"):
# Check if the input text is not empty
if text_input:
# Show loading spinner while processing
with st.spinner("Analyzing sentiment..."):
sentiment = predict_sentiment(text_input)
# Change the view based on the predicted sentiment
st.success(f"Sentiment: {sentiment}")
if sentiment == 'Positive':
st.balloons() # Celebratory animation for positive sentiment
# Add additional views for other sentiments as needed
else:
st.warning("Please enter some text for sentiment analysis.")
# Optional: Display the raw sentiment scores
if st.checkbox("Show Raw Sentiment Scores"):
if text_input:
inputs = tokenizer(text_input, return_tensors="pt")
outputs = model(**inputs)
raw_scores = outputs.logits[0].tolist()
st.info(f"Raw Sentiment Scores: {raw_scores}")
# Optional: Display additional information or analysis
# Add more components as needed for your specific use case
# Add a footer
st.text("Built with Streamlit and Transformers")
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