Create app.py

app.py

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+import gradio as gr
+from transformers import pipeline
+
+# Initialize the sentiment analysis pipeline
+# Model: nlptown/bert-base-multilingual-uncased-sentiment
+sentiment_analyzer = pipeline("sentiment-analysis", model="nlptown/bert-base-multilingual-uncased-sentiment")
+
+def analyze_sentiment(text):
+    """
+    Returns the predicted sentiment as a label ranging from 1 to 5 stars.
+    """
+    result = sentiment_analyzer(text)[0]
+    label = result["label"]  # e.g., "1 star", "2 stars", "3 stars", "4 stars", or "5 stars"
+    return f"Predicted sentiment: {label}"
+
+# Predefined examples
+examples = [
+    ["I love this product! It's amazing!"],
+    ["This was the worst experience I've ever had."],
+    ["The movie was okay, not great but not bad either."],
+    ["Absolutely fantastic! I would recommend it to everyone."]
+]
+
+# Create the Gradio interface
+demo = gr.Interface(
+    fn=analyze_sentiment,
+    inputs=gr.Textbox(lines=3, label="Enter Your Text Here"),
+    outputs=gr.Textbox(label="Predicted Sentiment"),
+    title="Multilingual Sentiment Analysis",
+    description=(
+        "This app uses the 'nlptown/bert-base-multilingual-uncased-sentiment' model "
+        "to predict sentiment on a scale of 1 to 5 stars."
+    ),
+    examples=examples,
+)
+
+if __name__ == "__main__":
+    demo.launch()