Update app.py

app.py

@@ -9,25 +9,20 @@ tokenizer = AutoTokenizer.from_pretrained("./")
 labels = ["Negative", "Positive"]
 
 def classify_sentiment(text):
-    ahmedlofti= text 
     inputs = tokenizer(text, return_tensors="tf", truncation=True, padding=True)
     outputs = model(**inputs)
     prediction = tf.argmax(outputs.logits, axis=1).numpy()[0]
-    sentiment = labels[prediction].lower()
-
-    # Return both processed message and classification
-    return ahmedlofti, sentiment
+    return labels[prediction].lower()
 
 with gr.Blocks() as demo:
     gr.Markdown("# Fas7ni Feedbacks\nClassify your feedback as positive or negative!")
 
     with gr.Row():
         msg = gr.Textbox(placeholder="Write your comment or feedback here...", label="Enter Feedback")
-        processed = gr.Textbox(label="Processed Message")
-        sentiment = gr.Textbox(label="Sentiment")
+        output = gr.Textbox(label="Sentiment")
 
     submit = gr.Button("Classify")
 
-    submit.click(fn=classify_sentiment, inputs=msg, outputs=[processed, sentiment])
+    submit.click(fn=classify_sentiment, inputs=msg, outputs=output)
 
 demo.launch()