File size: 3,348 Bytes
695363c f3dbd83 64c5ee5 695363c 2e7c89d f3dbd83 695363c 2d000f8 2e7c89d 695363c 2e7c89d 9d8c9ea 695363c 2e7c89d 7b61abf d1cb3da 695363c 2e7c89d 7b61abf 695363c 64c5ee5 695363c 7b61abf 695363c f274747 7b61abf 64c5ee5 8a2ba4f 64c5ee5 f274747 64c5ee5 7b61abf a12b796 695363c 2e7c89d 695363c f3dbd83 695363c d1cb3da 9d8c9ea 695363c d1cb3da 695363c |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 |
import gradio as gr
import rdflib
import requests
import matplotlib.pyplot as plt
import networkx as nx
from io import BytesIO
import base64
# Função para carregar e extrair os nomes do arquivo JSON-LD a partir de uma URL
def load_names_from_url(jsonld_url):
response = requests.get(jsonld_url)
data = response.json()
names = []
for item in data:
if 'name' in item:
names.append(item['name'])
return names
# Carregar nomes do arquivo JSON-LD
jsonld_url = 'https://huggingface.co/spaces/histlearn/ShowGraph/raw/main/datafile.jsonld'
names = load_names_from_url(jsonld_url)
def run_query_and_visualize(qtext, jsonld_url):
print("Executando consulta SPARQL...")
print(f"Consulta SPARQL: {qtext}")
# Carrega o arquivo JSON-LD
g = rdflib.Graph()
g.parse(jsonld_url, format="json-ld")
print("Consulta SPARQL carregada...")
# Executa a consulta SPARQL
qres = g.query(qtext)
# Cria o gráfico de rede
G = nx.DiGraph()
print("Processando resultados da consulta...")
# Processa os resultados da consulta
for row in qres:
s, p, o = row
G.add_node(str(s), label=str(s).split('/')[-1]) # Usar o último segmento da URI como rótulo
G.add_node(str(o), label=str(o).split('/')[-1])
G.add_edge(str(s), str(o), label=str(p).split('/')[-1])
# Desenha o gráfico usando NetworkX e Matplotlib
pos = {
"Adem": (0, 0.6),
"Adem-geo": (-0.4, -0.3),
"Adem-obra": (0.4, -0.1)
}
plt.figure(figsize=(10, 8))
nx.draw_networkx_nodes(G, pos, node_size=3000, node_color="skyblue", alpha=0.9)
nx.draw_networkx_edges(G, pos, width=2, alpha=0.5, edge_color='gray')
nx.draw_networkx_labels(G, pos, labels=nx.get_node_attributes(G, 'label'), font_size=9, font_color="black")
nx.draw_networkx_edge_labels(G, pos, edge_labels=nx.get_edge_attributes(G, 'label'), font_size=9, font_color="red")
plt.xlim(-1, 1)
plt.ylim(-1, 1)
plt.title("Resultado da Consulta SPARQL", size=15)
plt.axis('off')
# Salva o gráfico em um arquivo
buf = BytesIO()
plt.savefig(buf, format='png')
buf.seek(0)
img_str = base64.b64encode(buf.read()).decode()
graph_html = f'<img src="data:image/png;base64,{img_str}"/>'
plt.close()
print("Gráfico gerado com sucesso.")
return graph_html
def update_query(selected_location):
return f"""
PREFIX schema: <http://schema.org/>
SELECT * WHERE {{
?s schema:name "{selected_location}" .
?s ?p ?o .
}}
"""
with gr.Blocks() as demo:
gr.Markdown("# Visualização de Query SPARQL")
with gr.Column():
selected_location = gr.Dropdown(choices=names, label="Selecione o Local")
query_input = gr.Textbox(label="Consulta SPARQL", value=update_query(names[0]) if names else "", lines=10)
run_button = gr.Button("Executar Consulta")
graph_output = gr.HTML()
def on_location_change(loc):
return update_query(loc)
selected_location.change(fn=on_location_change, inputs=selected_location, outputs=query_input)
def on_run_button_click(query):
return run_query_and_visualize(query, jsonld_url)
run_button.click(fn=on_run_button_click, inputs=[query_input], outputs=graph_output)
demo.launch()
|