Update app.py

app.py

@@ -7,6 +7,13 @@ model = joblib.load('tunisian_arabiz_sentiment_analysis_model.pkl')
 vectorizer = joblib.load('tfidf_vectorizer.pkl')
 
 def predict_sentiment(text):
+    if not text.strip():
+        return (
+            "No input provided",
+            "N/A",
+            "Please enter some text to get a sentiment prediction."
+        )
+    
     text_vectorized = vectorizer.transform([text])
     prediction = model.predict(text_vectorized)[0]
     probabilities = model.predict_proba(text_vectorized)[0]
@@ -54,8 +61,7 @@ iface = gr.Interface(
     ],
     examples=formatted_examples,
     title="Tunisian Arabiz Sentiment Analysis",
-    description=
-    """
+    description="""
     <p>This model predicts the sentiment of Tunisian text as either Positive or Negative. It works with both Tunisian Arabiz and standard Arabic script.</p>
         
     <h4>What is Tunisian Arabiz? / ما هي العربيزية التونسية؟</h4>
@@ -74,21 +80,19 @@ iface = gr.Interface(
     <p>This sentiment analysis model was trained on a combined dataset from TuniziDataset and the Tunisian Dialect Corpus. 
     It uses TF-IDF vectorization for feature extraction and Logistic Regression for classification.</p>
     
-    
     <p>The model accepts Tunisian Arabiz written with Latin and Arabic script.</p>
     
     <h3>Limitations</h3>
     <p>Due to dataset limitations, neutral sentiment data was removed to achieve maximum performance. </p>
     <p>The model may not perform well on very colloquial expressions or new slang terms not present in the training data. 
     Sentiment can be nuanced and context-dependent, which may not always be captured accurately by this model.</p>
-    
+    <center>
     <h2>This model is open-source, and contributions of additional datasets are welcome to improve its capabilities.</h2>
     
     <h2>هذا النموذج مفتوح المصدر، ونرحب بمساهمات مجموعات البيانات الإضافية لتحسين قدراته.</h2>
-    
-    
+    </center>
     """
 )
 
 # Launch the interface
-iface.launch()
+iface.launch()