Update app.py

app.py

@@ -1,33 +1,7 @@
 import gradio as gr
-from transformers import BertTokenizer, BertForSequenceClassification
 from transformers import pipeline
-import torch
-
-# Load model and tokenizer
-model_name = "nlptown/bert-base-multilingual-uncased-sentiment"
-model = BertForSequenceClassification.from_pretrained(model_name)
-tokenizer = BertTokenizer.from_pretrained(model_name)
-
-# Define pipeline
-classifier = pipeline("sentiment-analysis", model=model, tokenizer=tokenizer)
-
-# Prediction function
-def predict_sentiment(text):
-    if not text.strip():
-        return "Please enter some text."
+classifier = pipeline("sentiment-analysis", model="bert-base-uncased")
+def classify_sentiment(text):
     result = classifier(text)[0]
-    label = result['label']
-    score = round(result['score'], 4)
-    return f"Sentiment: {label} (Confidence: {score})"
-
-# Gradio UI
-interface = gr.Interface(
-    fn=predict_sentiment,
-    inputs=gr.Textbox(lines=3, placeholder="Enter movie review..."),
-    outputs="text",
-    title="IMDB Movie Review Sentiment Analysis with BERT",
-    description="This demo uses BERT to predict sentiment on IMDB-like reviews. Model: nlptown/bert-base-multilingual-uncased-sentiment"
-)
-
-if __name__ == "__main__":
-    interface.launch()
+    return f"{result['label']} ({round(result['score'] * 100, 2)}%)"
+gr.Interface(fn=classify_sentiment, inputs="text", outputs="text").launch()