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#Developed by ruslanmv.com
import gradio as gr
import matplotlib.pyplot as plt
import networkx as nx
from owlready2 import *
import difflib
import tempfile
import os
def plot_ontology(search_term, owl_file):
"""
Loads an ontology OWL file, builds a graph of its classes and subclass relationships,
performs fuzzy matching to find nodes similar to the search term, and returns a plot
highlighting the matching nodes (and their immediate neighbors) in pink.
Args:
search_term (str): The search term used for fuzzy matching on node names.
owl_file (file): An uploaded ontology file in OWL format.
Returns:
A tuple with:
- The file path of the saved plot image.
- A text message with search result details.
"""
if owl_file is None:
return None, "Please upload an ontology OWL file."
# Load the ontology from the provided file.
try:
# Append temporary directory to the ontology search path.
onto_path.append(tempfile.gettempdir())
# Load the ontology using its file URI.
onto = get_ontology("file://"+owl_file.name).load()
except Exception as e:
return None, f"Error loading ontology: {e}"
# Build a directed graph: nodes represent ontology classes and edges represent subclass relationships.
G = nx.DiGraph()
for cls in onto.classes():
G.add_node(cls.name, iri=cls.iri)
for parent in cls.is_a:
if isinstance(parent, ThingClass):
G.add_edge(parent.name, cls.name)
# Define a helper function for fuzzy searching nodes.
def fuzzy_search(query, cutoff=0.6):
query = query.lower()
lower_to_orig = {node.lower(): node for node in G.nodes}
lower_nodes = list(lower_to_orig.keys())
matches = difflib.get_close_matches(query, lower_nodes, cutoff=cutoff)
return [lower_to_orig[m] for m in matches]
search_results = fuzzy_search(search_term)
if not search_results:
result_message = f"No matches found for '{search_term}'."
else:
result_message = f"Found nodes: {', '.join(search_results)}"
# Determine which nodes to highlight: matched nodes plus their immediate neighbors (parents and children).
highlight_nodes = set()
for node in search_results:
highlight_nodes.add(node)
highlight_nodes.update(G.predecessors(node))
highlight_nodes.update(G.successors(node))
# Create the graph layout and plot.
pos = nx.spring_layout(G, k=0.5, iterations=50)
plt.figure(figsize=(12, 8))
node_colors = ['pink' if node in highlight_nodes else 'lightblue' for node in G.nodes]
nx.draw(G, pos, with_labels=True, node_size=1500, node_color=node_colors, font_size=10, arrows=True)
plt.title("Ontology Graph with Highlighted Related Nodes\n" + result_message)
# Save the plot to a temporary file and close the plot.
tmpfile = tempfile.NamedTemporaryFile(suffix='.png', delete=False)
plt.savefig(tmpfile.name, format="png")
plt.close()
return tmpfile.name, result_message
# Load default file and search term
default_owl_path = "genai.owl"
default_search_term = "LoRa"
with open(default_owl_path, "rb") as f:
default_owl_bytes = f.read()
# Create a temporary file to store the default OWL content
temp_owl_file = tempfile.NamedTemporaryFile(delete=False, suffix=".owl")
temp_owl_file.write(default_owl_bytes)
temp_owl_file.close()
# Define the Gradio interface.
iface = gr.Interface(
fn=plot_ontology,
inputs=[
gr.Textbox(lines=1, label="Search Term", placeholder="Enter a search term (e.g., LoRa)", value=default_search_term),
gr.File(label="Upload Ontology OWL File", value=temp_owl_file.name)
],
outputs=[
gr.Image(type="filepath", label="Ontology Graph"),
gr.Textbox(label="Search Result Details")
],
title="Ontology Explorer",
description="Upload an ontology OWL file and enter a search term to visualize the ontology graph. "
"Nodes matching the search term and their immediate neighbors (parents and children) are highlighted in pink."
"Reference of the Ontology Loaded: [GENAI](https://bioportal.bioontology.org/ontologies/GENAI)",
examples=[
["LoRa", "genai.owl"],
["LLM", "genai.owl"],
["RLHF", "genai.owl"]
],
cache_examples=True
)
if __name__ == "__main__":
iface.launch(share=True) |