app.py

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()