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| """ | |
| Module for creating and configuring the Gradio interface. | |
| Handles the UI layout and component setup. | |
| """ | |
| import gradio as gr | |
| def create_interface(classifier, category_examples, custom_theme): | |
| # Wrapper function to handle None inputs and provide loading state | |
| def classify_with_loading(image): | |
| if image is None: | |
| return None | |
| return classifier.classify_image(image) | |
| # Create main interface container with custom theme | |
| with gr.Blocks(theme=custom_theme) as iface: | |
| # Header section with title and description | |
| gr.Markdown("# 🏛️ Landmark Image Classification") | |
| # About section | |
| gr.Markdown(""" | |
| This Gradio-based application allows users to classify famous landmarks using a Vision Transformer (ViT) model. Users can upload an image or select from provided examples to identify landmarks. | |
| """) | |
| # Create two-column layout for input and output | |
| with gr.Row(): | |
| with gr.Column(scale=1): | |
| # Left column: Image input and submit button | |
| input_image = gr.Image(type="pil", label="Input Image") | |
| submit_btn = gr.Button("Classify Landmark", variant="primary") | |
| with gr.Column(scale=1): | |
| # Right column: Classification results | |
| output_label = gr.Label(num_top_classes=5, label="Predictions") | |
| # Examples section with collapsible categories | |
| gr.Markdown("## Example Categories") | |
| for category, examples in category_examples.items(): | |
| # Create collapsible section for each category | |
| with gr.Accordion(f"{category}", open=False): | |
| # Add description of all landmarks in this category | |
| supported_landmarks = [example[1]['label'] for example in examples] if examples else [] | |
| landmarks_text = ", ".join(supported_landmarks) if supported_landmarks else "No landmarks available" | |
| gr.Markdown(f"**Supported landmarks in this category:** {landmarks_text}") | |
| if examples: | |
| gr.Examples( | |
| examples=examples, | |
| inputs=input_image, | |
| outputs=output_label, | |
| fn=classify_with_loading, | |
| cache_examples=False, | |
| label=None, | |
| examples_per_page=1000 | |
| ) | |
| else: | |
| gr.Markdown(f"No example images available for {category}") | |
| # Connect the submit button to the classification function | |
| submit_btn.click( | |
| fn=classify_with_loading, | |
| inputs=input_image, | |
| outputs=output_label, | |
| api_name="classify" | |
| ) | |
| return iface | |