Create app.py

app.py

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+import gradio as gr
+from transformers import pipeline
+
+# Load the sentiment analysis pipeline
+sentiment_pipeline = pipeline("text-classification", model="nlptown/bert-base-multilingual-uncased-sentiment")
+
+def analyze_sentiment(text):
+    if not text.strip():
+        return "Please enter some text to analyze."
+    
+    result = sentiment_pipeline(text)[0]
+    return f"Predicted Sentiment: {result['label']} stars"
+
+# Define the Gradio interface
+with gr.Blocks() as demo:
+    gr.Markdown("# Sentiment Analysis using BERT Model")
+    gr.Markdown("Enter a sentence or paragraph below and click 'Analyze' to get the predicted sentiment (1 to 5 stars).")
+    
+    text_input = gr.Textbox(label="Input Text", placeholder="Enter your text here...", lines=3)
+    analyze_button = gr.Button("Analyze Sentiment")
+    output_text = gr.Textbox(label="Predicted Sentiment", interactive=False)
+    
+    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."
+    ]
+    
+    gr.Examples(examples=examples, inputs=text_input)
+    analyze_button.click(analyze_sentiment, inputs=text_input, outputs=output_text)
+    
+# Launch the Gradio app
+demo.launch()