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)
+    sentiment = result[0]['label']
+    return sentiment
+
+def update_language(language):
+    if language == "العربية":
+        return (
+            gr.update(label="أدخل النص", placeholder="اكتب جملة أو فقرة هنا ^^..."),
+            gr.update(label="المشاعر المتوقعة (من 1 إلى 5)"),
+            "تحليل المشاعر"
+        )
+    else:  # Default to English
+        return (
+            gr.update(label="Enter Text", placeholder="Type a sentence or paragraph here ^^..."),
+            gr.update(label="Predicted Sentiment (1-5) star"),
+            "Analyze Sentiment"
+        )
+
+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."]
+]
+
+# Gradio
+with gr.Blocks() as demo:
+    gr.Markdown("## Sentiment Analysis - Choose Language (Arabic or English)")
+
+    language_choice = gr.Radio(["العربية", "English"], label="اختر اللغة", value="English")
+
+    with gr.Row():
+        with gr.Column():
+            text_input = gr.Textbox(label="Enter Text", placeholder="Type a sentence or paragraph here ^^...")
+
+        with gr.Column():
+            output = gr.Textbox(label="Predicted Sentiment (1-5)", interactive=False)
+
+    analyze_button = gr.Button("Analyze Sentiment")
+
+    gr.Examples(examples=examples, inputs=text_input)
+
+    analyze_button.click(analyze_sentiment, inputs=[text_input], outputs=output)
+
+    language_choice.change(update_language, inputs=[language_choice], outputs=[text_input, output, analyze_button])
+
+demo.launch()