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| import gradio as gr | |
| from datasets import load_dataset | |
| from transformers import pipeline | |
| # Load the WikiArt dataset in streaming mode | |
| dataset = load_dataset("huggan/wikiart", streaming=True) | |
| # Function to safely get a field value or return a default message | |
| def get_field(record, field, default="Unknown"): | |
| return record[field] if field in record and record[field] is not None else default | |
| # Function to display artwork details | |
| def display_artwork(index): | |
| try: | |
| for i, record in enumerate(dataset["train"]): # Stream through the dataset | |
| if i == index: | |
| return ( | |
| get_field(record, "image"), | |
| f"Title: {get_field(record, 'title')}\n" | |
| f"Artist: {get_field(record, 'artist')}\n" | |
| f"Style: {get_field(record, 'style')}\n" | |
| f"Genre: {get_field(record, 'genre')}" | |
| ) | |
| return None, "Error: Index out of range or invalid." | |
| except Exception as e: | |
| return None, f"Error: {str(e)}" | |
| # Function to filter artworks based on metadata | |
| def filter_artworks(artist=None, genre=None, style=None): | |
| results = [] | |
| try: | |
| for record in dataset["train"]: | |
| if (artist is None or get_field(record, "artist") == artist) and \ | |
| (genre is None or get_field(record, "genre") == genre) and \ | |
| (style is None or get_field(record, "style") == style): | |
| results.append(record) | |
| except Exception as e: | |
| return [] | |
| return results | |
| # Function to display filtered artworks | |
| def display_filtered_artworks(artist, genre, style): | |
| filtered_results = filter_artworks(artist, genre, style) | |
| if len(filtered_results) == 0: | |
| return None, "No artworks found with the specified filters." | |
| return [(get_field(r, "image"), | |
| f"Title: {get_field(r, 'title')}\n" | |
| f"Artist: {get_field(r, 'artist')}\n" | |
| f"Style: {get_field(r, 'style')}\n" | |
| f"Genre: {get_field(r, 'genre')}") | |
| for r in filtered_results] | |
| # Chatbot functionality for museum guide persona using a publicly available Hugging Face model | |
| chatbot = pipeline("text-generation", model="gpt2") # Replace with a valid Hugging Face model | |
| def museum_guide_query(prompt): | |
| try: | |
| response = chatbot(prompt, max_length=100, num_return_sequences=1) | |
| return response[0]["generated_text"] | |
| except Exception as e: | |
| return f"Error: {str(e)}" | |
| # Gradio interfaces | |
| artwork_interface = gr.Interface( | |
| fn=display_artwork, | |
| inputs=gr.Number(label="Artwork Index"), | |
| outputs=[gr.Image(label="Artwork"), gr.Text(label="Details")], | |
| title="Exhibit AI - Virtual Art Gallery" | |
| ) | |
| filter_interface = gr.Interface( | |
| fn=display_filtered_artworks, | |
| inputs=[gr.Text(label="Artist"), gr.Text(label="Genre"), gr.Text(label="Style")], | |
| outputs=gr.Gallery(label="Filtered Artworks"), # Removed the 'caption' argument | |
| title="Filter Artworks" | |
| ) | |
| chatbot_interface = gr.Interface( | |
| fn=museum_guide_query, | |
| inputs=gr.Textbox(label="Ask the Museum Guide"), | |
| outputs=gr.Text(label="Guide Response"), | |
| title="Museum Guide Chatbot" | |
| ) | |
| # Launch Gradio Blocks to combine all interfaces | |
| def launch_app(): | |
| with gr.Blocks() as demo: | |
| gr.Markdown("# Exhibit AI - Virtual Art Gallery") | |
| gr.TabbedInterface( | |
| [artwork_interface, filter_interface, chatbot_interface], | |
| ["View Artwork", "Filter Artworks", "Ask the Museum Guide"] | |
| ) | |
| demo.launch() | |
| if __name__ == "__main__": | |
| launch_app() |