Create app.py

app.py

@@ -0,0 +1,76 @@
+import gradio as gr
+from transformers import pipeline
+import torch
+
+# Initialize sentiment analysis pipeline
+try:
+    sentiment_pipeline = pipeline(
+        "sentiment-analysis",
+        model="nlptown/bert-base-multilingual-uncased-sentiment",
+        device=0 if torch.cuda.is_available() else -1
+    )
+except Exception as e:
+    raise Exception(f"Failed to load model: {str(e)}")
+
+def analyze_sentiment(text, language):
+    """Analyze sentiment of input text and return sentiment label and confidence score."""
+    if not text or not text.strip():
+        return "Error: Please enter some text", 0
+    
+    try:
+        result = sentiment_pipeline(text)
+        sentiment = result[0]['label']  # e.g., "1 star", "2 stars", etc.
+        score = result[0]['score']      # Confidence score between 0 and 1
+        return sentiment, round(score, 2)
+    except Exception as e:
+        return "Error occurred", 0
+
+# Custom CSS for bilingual readability
+custom_css = """
+    body, .gr-button, .gr-input, .gr-output, .gr-textbox {
+        font-family: 'Tajawal', 'Arial', sans-serif !important;
+    }
+    .gr-button {margin: 5px;}
+    .output-text {font-size: 16px;}
+"""
+
+# Gradio interface for Part 1
+with gr.Blocks(css=custom_css, theme=gr.themes.Soft()) as demo:
+    gr.Markdown("# Sentiment Analysis Platform")
+    gr.Markdown("Enter text in Arabic or English to analyze its sentiment.")
+    
+    with gr.Row():
+        with gr.Column(scale=2):
+            text_input = gr.Textbox(
+                label="Your Comment",
+                placeholder="Type your comment here...",
+                lines=3
+            )
+            language_input = gr.Radio(
+                ["Arabic", "English"],
+                label="Language",
+                value="English"
+            )
+            submit_btn = gr.Button("Analyze", variant="primary")
+        
+        with gr.Column(scale=3):
+            sentiment_output = gr.Textbox(label="Sentiment")
+            score_output = gr.Slider(0, 1, label="Confidence Score", interactive=False)
+    
+    examples = gr.Examples(
+        examples=[
+            ["The product is amazing!", "English"],
+            ["الخدمة سيئة جداً", "Arabic"],
+            ["منتج جيد نوعاً ما", "Arabic"],
+            ["It's okay, nothing special", "English"]
+        ],
+        inputs=[text_input, language_input]
+    )
+    
+    submit_btn.click(
+        fn=analyze_sentiment,
+        inputs=[text_input, language_input],
+        outputs=[sentiment_output, score_output]
+    )
+
+demo.launch()