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FinSentimentAnalysis / app.py

khandelwalkishna15

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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).
	"""
	)