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| """ | |
| Gradio interface for Book Cover Analyzer - optimized for Hugging Face Spaces | |
| """ | |
| import gradio as gr | |
| from analyzer.book_analyzer import BookCoverAnalyzer | |
| from PIL import Image | |
| import tempfile | |
| import os | |
| # Initialize analyzer once (loads models) | |
| print("Loading models... This may take 30-60 seconds on first run.") | |
| analyzer = BookCoverAnalyzer(verbose=True) | |
| print("Models loaded successfully!") | |
| def analyze_book_cover(image): | |
| """Analyze uploaded book cover image""" | |
| if image is None: | |
| return "Please upload an image.", "", "", "", "" | |
| tmp_path = None | |
| try: | |
| # Save PIL Image to temporary file (analyzer expects file path) | |
| with tempfile.NamedTemporaryFile(delete=False, suffix='.jpg') as tmp: | |
| image.save(tmp.name, format='JPEG') | |
| tmp_path = tmp.name | |
| # Analyze | |
| result = analyzer.analyze(tmp_path) | |
| # Check if analysis succeeded | |
| if not result.get('success', False): | |
| error = result.get('error', 'Unknown error') | |
| return f"Analysis failed: {error}", "", "", "", "" | |
| # Extract interpretation | |
| interp = result.get('interpretation', {}) | |
| # Format outputs | |
| title = interp.get('inferred_title') or "Unable to determine" | |
| authors = ", ".join(interp.get('inferred_authors', [])) or "Unable to determine" | |
| publisher = interp.get('inferred_publisher') or "Unable to determine" | |
| # Format detected elements | |
| image_regions = result.get('image_regions', []) | |
| if image_regions: | |
| elements_text = f"**Detected {len(image_regions)} visual elements:**\n" | |
| for elem in image_regions[:5]: # Top 5 | |
| elements_text += f"- {elem['location']}: {elem['pytorch_class']} ({elem['confidence']:.1%})\n" | |
| else: | |
| elements_text = "No visual elements detected (possibly plain text cover)" | |
| # Format detected text | |
| text_regions = result.get('text_regions', []) | |
| if text_regions: | |
| text_output = f"**Detected {len(text_regions)} text regions:**\n" | |
| for region in text_regions[:10]: # Top 10 | |
| text_output += f"- {region['text']}\n" | |
| # Format other text (reviews/quotes) | |
| other = interp.get('other_text', []) | |
| if other: | |
| text_output += f"\n**Reviews/Quotes:**\n" | |
| for text in other[:3]: | |
| text_output += f"- {text}\n" | |
| else: | |
| text_output = "No text detected on cover" | |
| return title, authors, publisher, elements_text, text_output | |
| except Exception as e: | |
| error_msg = f"Error analyzing image: {str(e)}" | |
| return error_msg, "", "", "", "" | |
| finally: | |
| # Always clean up temp file, even if there's an error | |
| if tmp_path and os.path.exists(tmp_path): | |
| try: | |
| os.unlink(tmp_path) | |
| except: | |
| pass # Ignore cleanup errors | |
| # Create Gradio interface | |
| demo = gr.Interface( | |
| fn=analyze_book_cover, | |
| inputs=gr.Image(type="pil", label="Upload Book Cover"), | |
| outputs=[ | |
| gr.Textbox(label="📚 Book Title", lines=2), | |
| gr.Textbox(label="✍️ Author(s)", lines=2), | |
| gr.Textbox(label="🏢 Publisher", lines=1), | |
| gr.Textbox(label="🖼️ Visual Elements (ImageNet Classification)", lines=8), | |
| gr.Textbox(label="📝 Detected Text (EasyOCR)", lines=15) | |
| ], | |
| title="📖 Book Cover Analyzer", | |
| description=""" | |
| Upload a book cover image to automatically detect: | |
| - **Book title, author, publisher** (inferred from text) | |
| - **Visual elements** (classified using ResNet18) | |
| - **All text regions** (extracted using EasyOCR) | |
| Powered by OpenCV, PyTorch, and EasyOCR. | |
| """, | |
| examples=[ | |
| # You can add example images here if you want | |
| ], | |
| theme="soft" | |
| ) | |
| if __name__ == "__main__": | |
| demo.launch(server_name="0.0.0.0", server_port=7860) | |