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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) | |