Create app.py

app.py

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+import gradio as gr
+from transformers import pipeline
+
+# Load the sentiment analysis pipeline
+# We use a model specifically trained on product reviews (Amazon reviews)
+model_name = "LiYuan/amazon-review-sentiment-analysis"
+sentiment_pipeline = pipeline("sentiment-analysis", model=model_name)
+
+def analyze_sentiment(review_text):
+    """
+    Analyzes the sentiment of the input text and returns a formatted result.
+    The model outputs star ratings (1-5 stars).
+    """
+    if not review_text.strip():
+        return "Please enter some text to analyze.", None
+
+    try:
+        # Perform sentiment analysis
+        results = sentiment_pipeline(review_text)
+        
+        # The model returns labels like '1 star', '2 stars', etc.
+        label = results[0]['label']
+        score = results[0]['score']
+        
+        # Map star ratings to sentiment categories
+        star_count = int(label.split()[0])
+        
+        if star_count >= 4:
+            sentiment = "Positive"
+            color = "🟢"
+        elif star_count == 3:
+            sentiment = "Neutral"
+            color = "🟡"
+        else:
+            sentiment = "Negative"
+            color = "🔴"
+            
+        result_text = f"### Sentiment: {sentiment} {color}\n"
+        result_text += f"**Rating:** {label} ({score:.2%} confidence)\n\n"
+        
+        # Add some context for computer system products
+        if "battery" in review_text.lower():
+            result_text += "- *Note: This review mentions battery life.*\n"
+        if "performance" in review_text.lower() or "fast" in review_text.lower() or "slow" in review_text.lower():
+            result_text += "- *Note: This review mentions system performance.*\n"
+        if "screen" in review_text.lower() or "display" in review_text.lower():
+            result_text += "- *Note: This review mentions the display/screen.*\n"
+            
+        return result_text, {label: score}
+        
+    except Exception as e:
+        return f"Error during analysis: {str(e)}", None
+
+# Define the Gradio interface
+with gr.Blocks(theme=gr.themes.Soft()) as demo:
+    gr.Markdown("# 💻 Computer System Sentiment Analyzer")
+    gr.Markdown(
+        "Enter a review for a computer, laptop, or hardware component to analyze its sentiment. "
+        "This tool uses a model trained on millions of product reviews to provide accurate star ratings."
+    )
+    
+    with gr.Row():
+        with gr.Column():
+            input_text = gr.Textbox(
+                label="Product Review",
+                placeholder="e.g., The MacBook Pro has amazing performance and a stunning display, but the price is a bit high...",
+                lines=5
+            )
+            submit_btn = gr.Button("Analyze Sentiment", variant="primary")
+            
+        with gr.Column():
+            output_markdown = gr.Markdown(label="Analysis Result")
+            output_label = gr.Label(label="Confidence Score")
+
+    # Examples for users to try
+    gr.Examples(
+        examples=[
+            ["The laptop is incredibly fast and the battery lasts all day. Highly recommended!"],
+            ["The screen arrived with dead pixels and the customer service was unhelpful. Disappointed."],
+            ["It's a decent computer for the price. Not the fastest, but gets the job done for basic tasks."],
+            ["The cooling system is quite loud under load, but the gaming performance is top-notch."]
+        ],
+        inputs=input_text
+    )
+
+    submit_btn.click(
+        fn=analyze_sentiment,
+        inputs=input_text,
+        outputs=[output_markdown, output_label]
+    )
+
+if __name__ == "__main__":
+    demo.launch(server_name="0.0.0.0",show_error=True)