Create app.py

app.py

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+import gradio as gr
+from transformers import pipeline
+
+# Load the sentiment analysis pipeline with the specified model
+sentiment_analyzer = pipeline("sentiment-analysis", model="nlptown/bert-base-multilingual-uncased-sentiment")
+
+# Define the sentiment analysis function
+def analyze_sentiment(text):
+    # Perform sentiment analysis
+    result = sentiment_analyzer(text)[0]
+    # Extract label (e.g., "1 star", "2 stars", etc.) and return it
+    return f"Predicted Sentiment: {result['label']}"
+
+# Define input and output components with clear labels
+input_text = gr.Textbox(lines=5, label="Enter Your Text", placeholder="Type a sentence or paragraph here...")
+output_sentiment = gr.Textbox(label="Sentiment Result")
+
+# Define example inputs
+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
+interface = gr.Interface(
+    fn=analyze_sentiment,
+    inputs=input_text,
+    outputs=output_sentiment,
+    title="Sentiment Analyzer",
+    description="Enter text to analyze its sentiment (1 to 5 stars) using a BERT-based model.",
+    examples=examples,
+    theme="default"  # Ensures a clean, responsive design
+)
+
+# Launch the interface
+interface.launch()