Create app.py

app.py

@@ -0,0 +1,24 @@
+import gradio as gr
+from transformers import pipeline, AutoModelForSequenceClassification, AutoTokenizer
+
+# Load model and tokenizer
+model_name = "nlptown/bert-base-multilingual-uncased-sentiment"
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+# Define pipeline for sentiment analysis
+classifier = pipeline('text-classification', model=model, tokenizer=tokenizer)
+
+# Define Gradio interface
+def predict_sentiment(text):
+    result = classifier(text)[0]
+    label = result['label']
+    score = result['score']
+    return f"Prediction: {label} with confidence {score}"
+
+iface = gr.Interface(fn=predict_sentiment, inputs="text", outputs="text", 
+                     title="Sentiment Analysis with Hugging Face and Gradio", 
+                     description="Enter text to get sentiment prediction.")
+
+# Launch Gradio interface
+iface.launch()