Upload app(1).py

app(1).py

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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 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 **
+    """
+)