Update app.py

app.py

@@ -6,10 +6,13 @@ pipe = pipeline("text-classification", model="finiteautomata/bertweet-base-senti
 
 # Define a function to perform sentiment analysis
 def analyze_sentiment(text):
-    results = pipe(text)
-    sentiment = results[0]['label']
-    confidence = results[0]['score']
-    return sentiment, confidence
+    try:
+        results = pipe(text)
+        sentiment = results[0]['label']
+        confidence = results[0]['score']
+        return sentiment, confidence
+    except Exception as e:
+        return "ERROR", 0.0  # Handle errors gracefully
 
 # Create a Streamlit app
 st.title("Sentiment Analysis App")
@@ -17,13 +20,20 @@ st.title("Sentiment Analysis App")
 # Get the user input
 text = st.text_input("Enter text for sentiment analysis")
 
-# Perform sentiment analysis
-sentiment, confidence = analyze_sentiment(text)
+if text:
+    # Perform sentiment analysis
+    sentiment, confidence = analyze_sentiment(text)
 
-# Display the output
-if sentiment == "POSITIVE":
-    st.success(f"Sentiment: Positive, Confidence: {confidence:.2f}")
-elif sentiment == "NEGATIVE":
-    st.error(f"Sentiment: Negative, Confidence: {confidence:.2f}")
-else:
-    st.info(f"Sentiment: Neutral, Confidence: {confidence:.2f}")
+    # Set a confidence threshold
+    confidence_threshold = 0.5
+
+    # Display the output based on the sentiment and confidence
+    if confidence >= confidence_threshold:
+        if sentiment == "POSITIVE":
+            st.success(f"Sentiment: Positive, Confidence: {confidence:.2f}")
+        elif sentiment == "NEGATIVE":
+            st.error(f"Sentiment: Negative, Confidence: {confidence:.2f}")
+        else:
+            st.info(f"Sentiment: Neutral, Confidence: {confidence:.2f}")
+    else:
+        st.warning("Low confidence. Sentiment result may not be reliable.")