Spaces:
Running
Running
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() |