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import random
import gradio as gr
import networkx as nx
from lib.graph_extract import triplextract, parse_triples
from lib.visualize import create_graph, create_bokeh_plot, create_plotly_plot
from lib.samples import snippets
WORD_LIMIT = 300
def process_text(text, entity_types, predicates, layout_type, visualization_type):
if not text:
return None, None, "Please enter some text."
words = text.split()
if len(words) > WORD_LIMIT:
return None, None, f"Please limit your input to {WORD_LIMIT} words. Current word count: {len(words)}"
entity_types = [et.strip() for et in entity_types.split(",") if et.strip()]
predicates = [p.strip() for p in predicates.split(",") if p.strip()]
if not entity_types:
return None, None, "Please enter at least one entity type."
if not predicates:
return None, None, "Please enter at least one predicate."
try:
prediction = triplextract(text, entity_types, predicates)
if prediction.startswith("Error"):
return None, None, prediction
entities, relationships = parse_triples(prediction)
if not entities and not relationships:
return None, None, "No entities or relationships found. Try different text or check your input."
G = create_graph(entities, relationships)
if visualization_type == 'Bokeh':
fig = create_bokeh_plot(G, layout_type)
else:
fig = create_plotly_plot(G, layout_type)
output_text = f"Entities: {entities}\nRelationships: {relationships}\n\nRaw output:\n{prediction}"
return G, fig, output_text
except Exception as e:
print(f"Error in process_text: {str(e)}")
return None, None, f"An error occurred: {str(e)}"
def update_graph(G, layout_type, visualization_type):
if G is None:
return None, "Please process text first."
try:
if visualization_type == 'Bokeh':
fig = create_bokeh_plot(G, layout_type)
else:
fig = create_plotly_plot(G, layout_type)
return fig, ""
except Exception as e:
print(f"Error in update_graph: {e}")
return None, f"An error occurred while updating the graph: {str(e)}"
def update_inputs(sample_name):
sample = snippets[sample_name]
return sample.text_input, sample.entity_types, sample.predicates
with gr.Blocks(theme=gr.themes.Monochrome()) as demo:
gr.Markdown("# Knowledge Graph Extractor")
default_sample_name = random.choice(list(snippets.keys()))
default_sample = snippets[default_sample_name]
with gr.Row():
with gr.Column(scale=1):
sample_dropdown = gr.Dropdown(choices=list(snippets.keys()), label="Select Sample", value=default_sample_name)
input_text = gr.Textbox(label="Input Text", lines=5, value=default_sample.text_input)
entity_types = gr.Textbox(label="Entity Types", value=default_sample.entity_types)
predicates = gr.Textbox(label="Predicates", value=default_sample.predicates)
layout_type = gr.Dropdown(choices=['spring', 'fruchterman_reingold', 'circular', 'random', 'spectral', 'shell'],
label="Layout Type", value='spring')
visualization_type = gr.Radio(choices=['Bokeh', 'Plotly'], label="Visualization Type", value='Bokeh')
process_btn = gr.Button("Process Text")
with gr.Column(scale=2):
output_graph = gr.Plot(label="Knowledge Graph")
error_message = gr.Textbox(label="Textual Output")
graph_state = gr.State(None)
def process_and_update(text, entity_types, predicates, layout_type, visualization_type):
G, fig, output = process_text(text, entity_types, predicates, layout_type, visualization_type)
return G, fig, output
def update_graph_wrapper(G, layout_type, visualization_type):
if G is not None:
fig, _ = update_graph(G, layout_type, visualization_type)
return fig
sample_dropdown.change(update_inputs, inputs=[sample_dropdown], outputs=[input_text, entity_types, predicates])
process_btn.click(process_and_update,
inputs=[input_text, entity_types, predicates, layout_type, visualization_type],
outputs=[graph_state, output_graph, error_message])
layout_type.change(update_graph_wrapper,
inputs=[graph_state, layout_type, visualization_type],
outputs=[output_graph])
visualization_type.change(update_graph_wrapper,
inputs=[graph_state, layout_type, visualization_type],
outputs=[output_graph])
if __name__ == "__main__":
demo.launch(share=True)import random
import gradio as gr
import networkx as nx
from lib.graph_extract import triplextract, parse_triples
from lib.visualize import create_graph, create_bokeh_plot, create_plotly_plot
from lib.samples import snippets
WORD_LIMIT = 300
def process_text(text, entity_types, predicates, layout_type, visualization_type):
if not text:
return None, None, "Please enter some text."
words = text.split()
if len(words) > WORD_LIMIT:
return None, None, f"Please limit your input to {WORD_LIMIT} words. Current word count: {len(words)}"
entity_types = [et.strip() for et in entity_types.split(",") if et.strip()]
predicates = [p.strip() for p in predicates.split(",") if p.strip()]
if not entity_types:
return None, None, "Please enter at least one entity type."
if not predicates:
return None, None, "Please enter at least one predicate."
try:
prediction = triplextract(text, entity_types, predicates)
if prediction.startswith("Error"):
return None, None, prediction
entities, relationships = parse_triples(prediction)
if not entities and not relationships:
return None, None, "No entities or relationships found. Try different text or check your input."
G = create_graph(entities, relationships)
if visualization_type == 'Bokeh':
fig = create_bokeh_plot(G, layout_type)
else:
fig = create_plotly_plot(G, layout_type)
output_text = f"Entities: {entities}\nRelationships: {relationships}\n\nRaw output:\n{prediction}"
return G, fig, output_text
except Exception as e:
print(f"Error in process_text: {str(e)}")
return None, None, f"An error occurred: {str(e)}"
def update_graph(G, layout_type, visualization_type):
if G is None:
return None, "Please process text first."
try:
if visualization_type == 'Bokeh':
fig = create_bokeh_plot(G, layout_type)
else:
fig = create_plotly_plot(G, layout_type)
return fig, ""
except Exception as e:
print(f"Error in update_graph: {e}")
return None, f"An error occurred while updating the graph: {str(e)}"
def update_inputs(sample_name):
sample = snippets[sample_name]
return sample.text_input, sample.entity_types, sample.predicates
with gr.Blocks(theme=gr.themes.Monochrome()) as demo:
gr.Markdown("# Knowledge Graph Extractor")
default_sample_name = random.choice(list(snippets.keys()))
default_sample = snippets[default_sample_name]
with gr.Row():
with gr.Column(scale=1):
sample_dropdown = gr.Dropdown(choices=list(snippets.keys()), label="Select Sample", value=default_sample_name)
input_text = gr.Textbox(label="Input Text", lines=5, value=default_sample.text_input)
entity_types = gr.Textbox(label="Entity Types", value=default_sample.entity_types)
predicates = gr.Textbox(label="Predicates", value=default_sample.predicates)
layout_type = gr.Dropdown(choices=['spring', 'fruchterman_reingold', 'circular', 'random', 'spectral', 'shell'],
label="Layout Type", value='spring')
visualization_type = gr.Radio(choices=['Bokeh', 'Plotly'], label="Visualization Type", value='Bokeh')
process_btn = gr.Button("Process Text")
with gr.Column(scale=2):
output_graph = gr.Plot(label="Knowledge Graph")
error_message = gr.Textbox(label="Textual Output")
graph_state = gr.State(None)
def process_and_update(text, entity_types, predicates, layout_type, visualization_type):
G, fig, output = process_text(text, entity_types, predicates, layout_type, visualization_type)
return G, fig, output
def update_graph_wrapper(G, layout_type, visualization_type):
if G is not None:
fig, _ = update_graph(G, layout_type, visualization_type)
return fig
sample_dropdown.change(update_inputs, inputs=[sample_dropdown], outputs=[input_text, entity_types, predicates])
process_btn.click(process_and_update,
inputs=[input_text, entity_types, predicates, layout_type, visualization_type],
outputs=[graph_state, output_graph, error_message])
layout_type.change(update_graph_wrapper,
inputs=[graph_state, layout_type, visualization_type],
outputs=[output_graph])
visualization_type.change(update_graph_wrapper,
inputs=[graph_state, layout_type, visualization_type],
outputs=[output_graph])
if __name__ == "__main__":
demo.launch(share=True) |