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| import gradio as gr | |
| from transformers import pipeline | |
| # Load the pre-trained sentiment analysis model | |
| sentiment_model = pipeline("sentiment-analysis", model="nlptown/bert-base-multilingual-uncased-sentiment") | |
| # Function to analyze sentiment | |
| def analyze_sentiment(text): | |
| # Get sentiment predictions | |
| result = sentiment_model(text) | |
| label = result[0]['label'] | |
| score = result[0]['score'] | |
| # Format the sentiment into human-friendly text | |
| label_mapping = { | |
| '1 star': 'Very Negative', | |
| '2 stars': 'Negative', | |
| '3 stars': 'Neutral', | |
| '4 stars': 'Positive', | |
| '5 stars': 'Very Positive' | |
| } | |
| sentiment_result = f"**Sentiment**: {label_mapping[label]}\n**Confidence**: {score:.2f}" | |
| return sentiment_result | |
| # Custom CSS for appealing colors on dark and light themes | |
| css = """ | |
| body {background-color: #f4f4f4; font-family: 'Arial'; color: #333;} | |
| input, textarea {border-radius: 10px; border: 2px solid #999; padding: 10px; background-color: #fff; color: #333;} | |
| button {background-color: #3498db; color: white; border: none; padding: 10px 20px; border-radius: 10px; cursor: pointer;} | |
| button:hover {background-color: #2980b9;} | |
| .output-text {font-size: 18px; color: #333;} | |
| footer {display: none !important;} | |
| /* Dark theme */ | |
| @media (prefers-color-scheme: dark) { | |
| body {background-color: #2c3e50; color: #ecf0f1;} | |
| input, textarea {background-color: #34495e; color: #ecf0f1; border: 2px solid #999;} | |
| button {background-color: #2980b9;} | |
| button:hover {background-color: #1abc9c;} | |
| .output-text {color: #ecf0f1;} | |
| } | |
| """ | |
| # Create Gradio interface with enhanced UI and updated description | |
| interface = gr.Interface( | |
| fn=analyze_sentiment, | |
| inputs=gr.Textbox( | |
| lines=5, | |
| placeholder="Enter a marketplace review or sentence here...", | |
| label="Input Review", | |
| ), | |
| outputs=gr.Markdown(), | |
| title="Sentiment Reveal", | |
| description=( | |
| "Analyze the sentiment of product reviews in English, Dutch, German, French, Italian, and Spanish. Focused Sentiment Analysis for eCommerce." | |
| ), | |
| examples=[["This product is amazing! I highly recommend it."], | |
| ["I'm very disappointed with this purchase."], | |
| ["The product was okay, not great but not terrible."]], | |
| allow_flagging="never", | |
| css=css, | |
| ) | |
| # Launch the app with sharing enabled | |
| interface.launch(share=True) | |