Update app.py

app.py

@@ -1,12 +1,22 @@
-from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import gradio as gr
 from transformers import pipeline
 
-# Load a multi-class sentiment model
-model_name = "cardiffnlp/twitter-roberta-base-sentiment"
-tokenizer = AutoTokenizer.from_pretrained(model_name)
-model = AutoModelForSequenceClassification.from_pretrained(model_name)
-sentiment_model = pipeline("sentiment-analysis", model=model, tokenizer=tokenizer)
+# Load pre-trained sentiment analysis model
+sentiment_model = pipeline("sentiment-analysis")
 
+# Define function to use the model
 def analyze_sentiment(text):
     result = sentiment_model(text)[0]
     return f"Label: {result['label']} | Confidence: {result['score']:.2f}"
+
+# Create Gradio interface
+demo = gr.Interface(
+    fn=analyze_sentiment,
+    inputs=gr.Textbox(lines=3, placeholder="Type a sentence here..."),
+    outputs="text",
+    title="Simple Sentiment Analyzer",
+    description="Find out if your text is Positive or Negative using a BERT model."
+)
+
+# Launch the app
+demo.launch()