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import gradio as gr
from datasets import load_dataset
from PIL import Image
import io
import base64
import json
from graph_visualization import visualize_graph
# Load the dataset
dataset = load_dataset(
"Zaherrr/OOP_KG_Dataset", split='train'
)
print(f'This is the dataset: {dataset}')
dataset = dataset['_data_files']
print(f'This is the train dataset: {dataset}')
def reshape_json_data_to_fit_visualize_graph(graph_data):
nodes = graph_data["nodes"]
edges = graph_data["edges"]
transformed_nodes = [
{"id": nodes["id"][idx], "label": nodes["label"][idx]}
for idx in range(len(nodes["id"]))
]
transformed_edges = [
{"source": edges["source"][idx], "target": edges["target"][idx], "type": "->"}
for idx in range(len(edges["source"]))
]
# print(f"transformed nodes = {transformed_nodes}")
graph_data = {"nodes": transformed_nodes, "edges": transformed_edges}
return graph_data
def display_example(index):
example = dataset[index]
# print("This is the example: ")
# print(example)
# Get the image
img = example["image"]
# Prepare the graph data
graph_data = {"nodes": example["nodes"], "edges": example["edges"]}
# # Convert graph_data to JSON string
# json_data = json.dumps(graph_data)
transformed_graph_data = reshape_json_data_to_fit_visualize_graph(graph_data)
# print(json_data)
# Generate the graph visualization
graph_html = visualize_graph(transformed_graph_data)
return img, graph_html
def create_interface():
with gr.Blocks() as demo:
gr.Markdown("# Knowledge Graph Visualizer")
with gr.Row():
index_slider = gr.Slider(
minimum=0, maximum=len(dataset) - 1, step=1, label="Example Index"
)
with gr.Row():
image_output = gr.Image(type="pil", label="Image")
graph_output = gr.HTML(label="Knowledge Graph")
index_slider.change(
fn=display_example,
inputs=[index_slider],
outputs=[image_output, graph_output],
)
return demo
# Create and launch the interface
if __name__ == "__main__":
demo = create_interface()
demo.launch(debug=True)
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