Update app.py

app.py

@@ -7,7 +7,11 @@ MODEL_ID = "edaUsha/Fine_Tuning_Bert_For_Sentiment_Anaysis"
 tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
 model = AutoModelForSequenceClassification.from_pretrained(MODEL_ID)
 
-id2label = {0: "NEGATIVE", 1: "POSITIVE"}  # change if your labels differ
+id2label = {
+    0: "NEGATIVE",   # change to your actual label name
+    1: "NEUTRAL",    # change if needed
+    2: "POSITIVE"    # change if needed
+}
 
 def predict_sentiment(text):
     inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True)
@@ -18,15 +22,4 @@ def predict_sentiment(text):
     pred_id = int(torch.argmax(probs))
     label = id2label[pred_id]
     confidence = float(probs[pred_id])
-    return f"{label} ({confidence:.2f})"
-
-demo = gr.Interface(
-    fn=predict_sentiment,
-    inputs=gr.Textbox(lines=3, label="Input text"),
-    outputs=gr.Textbox(label="Prediction"),
-    title="Fine-tuned BERT Sentiment Analysis",
-    description="Enter a sentence to see its predicted sentiment."
-)
-
-if __name__ == "__main__":
-    demo.launch()
+    return f"{label} ({confidence:.2f})"