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app.py

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+import gradio as gr
+from transformers import pipeline
+
+sentiment_pipeline = pipeline("sentiment-analysis")
+
+def predict_sentiment(text):
+    result = sentiment_pipeline(text)[0]
+    return f"{result['label']}"
+
+with gr.Blocks(theme=gr.themes.Soft()) as interface:
+    gr.Markdown(
+        """
+        <h1 style='text-align: center;'>Sentiment Analysis App</h1>
+        <p style='text-align: center;'>Analyze the sentiment of any review or short text. The model will classify it as <strong>Positive</strong> or <strong>Negative</strong>.</p>
+        """,
+    )
+
+    with gr.Row():
+        with gr.Column():
+            input_text = gr.Textbox(
+                label="Enter your text",
+                placeholder="Type your review here...",
+                lines=4
+            )
+            submit_btn = gr.Button("Analyze")
+
+        with gr.Column():
+            output_label = gr.Textbox(
+                label="Prediction",
+                interactive=False
+            )
+
+    submit_btn.click(predict_sentiment, inputs=input_text, outputs=output_label)
+
+interface.launch()

requirements.txt

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+gradio==4.29.0
+transformers==4.41.2
+torch
+numpy