Added examples to make it easier for the users

app.py

@@ -14,12 +14,20 @@ def classifier(sentence):
     predicted_label = label_mapping[result[0]['label']]
     return predicted_label  # Should print "negative" or "positive"
 
+examples = [
+    ["This movie is amazing!"],
+    ["I dislike this product."],
+    ["The food was bland. The texture was fine but the taste was lacking."],
+    ["The book was enjoyable. The story was good but predictable."],
+    ["The movie was boring. The plot was dull and unoriginal."]
+]
+
 # Define the Gradio Interface
 demo = gr.Interface(
     fn=classifier, 
     inputs=gr.Textbox(
         lines=4, 
-        placeholder="Type a sentence to classify the sentiment...", 
+        placeholder="Enter a sentence to analyze sentiment (e.g., 'I really liked this product.')", 
         label="Input Text"
     ), 
     outputs=gr.Textbox(
@@ -29,6 +37,7 @@ demo = gr.Interface(
     description="Classify the sentiment of the input text as positive or negative.",
     theme="hugging-face",  # Optional, you can experiment with other themes like 'huggingface'
     allow_flagging="never",  # Disable flagging if not needed
+    examples=examples # Add examples to make it easier for users
 )
 
 # Launch the interface