Create app.py

app.py

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+import gradio as gr
+from transformers import pipeline
+
+sentiment_analyzer = pipeline("sentiment-analysis", model="nlptown/bert-base-multilingual-uncased-sentiment")
+
+def analyze_sentiment(text):
+    result = sentiment_analyzer(text)[0]
+    sentiment_score = result['label']
+    
+    if sentiment_score == '1 star':
+        return 1
+    elif sentiment_score == '2 stars':
+        return 2
+    elif sentiment_score == '3 stars':
+        return 3
+    elif sentiment_score == '4 stars':
+        return 4
+    else:
+        return 5
+
+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."
+]
+
+iface = gr.Interface(
+    fn=analyze_sentiment,  # Function to call for sentiment analysis
+    inputs=[
+        gr.Textbox(label="Enter Text", placeholder="Type or paste a sentence or paragraph here...", lines=5),
+        gr.Button("Analyze Sentiment")  # Button to trigger analysis
+    ],
+    outputs=gr.Textbox(label="Sentiment Rating (1 to 5 stars)"),  # Display sentiment rating
+    live=False,  # Disable live preview while typing
+    examples=examples,  # Predefined examples
+    description="Sentiment analysis using BERT-based model for multilingual sentiment prediction."
+)
+
+iface.launch()