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import requests
import json
import networkx as nx
import matplotlib.pyplot as plt
from fuzzywuzzy import fuzz
from fuzzywuzzy import process
from lib.memory import *
class APIRequester:
def __init__(self):
pass
def make_request(self, url):
response = requests.get(url)
if response.status_code == 200:
return response.json()
else:
return None
class Grapher:
def __init__(self, memoria_nlp, threshold=70):
self.threshold = threshold
self.graph = nx.Graph()
self.memoria_nlp = memoria_nlp
def parse_json(self, data, parent=None):
if isinstance(data, dict):
for key, value in data.items():
if parent:
self.graph.add_node(parent)
self.graph.add_node(key)
self.graph.add_edge(parent, key)
for node in self.graph.nodes():
if node != parent and fuzz.ratio(node, key) >= self.threshold:
self.graph = nx.contracted_nodes(self.graph, node, key, self_loops=False)
self.memoria_nlp.agregar_concepto("keys", [(key, 1.0)])
if isinstance(value, (dict, list)):
self.parse_json(value, key)
else:
self.memoria_nlp.agregar_concepto("values", [(str(value), 1.0)])
if parent:
self.graph.add_node(value)
self.graph.add_edge(key, value)
for node in self.graph.nodes():
if node != value and fuzz.ratio(node, value) >= self.threshold:
self.graph = nx.contracted_nodes(self.graph, node, value, self_loops=False)
elif isinstance(data, list):
for item in data:
self.parse_json(item, parent)
def draw_graph(self):
pos = nx.spring_layout(self.graph, seed=42)
nx.draw(self.graph, pos, with_labels=True, node_size=700, node_color='skyblue', font_size=10, font_weight='bold')
plt.title("JSON Graph")
plt.show()
def guardar_en_memoria(self):
keys = self.memoria_nlp.obtener_conceptos_acotados(100)
with open("memoria.json", "w") as file:
json.dump(keys, file)
def buscar_nodo(self, nodo):
return process.extractOne(nodo, self.graph.nodes())[0]
def eliminar_nodo(self, nodo):
self.graph.remove_node(nodo)
def agregar_nodo(self, nodo):
self.graph.add_node(nodo)
def distancia_entre_nodos(self, nodo1, nodo2):
return nx.shortest_path_length(self.graph, source=nodo1, target=nodo2)
def ruta_entre_nodos(self, nodo1, nodo2):
return nx.shortest_path(self.graph, source=nodo1, target=nodo2)
def unir_grafos(self, otro_grafo, umbral):
for nodo in otro_grafo.nodes():
nodo_similar = process.extractOne(nodo, self.graph.nodes())[0]
if fuzz.ratio(nodo, nodo_similar) >= umbral:
self.graph = nx.contracted_nodes(self.graph, nodo_similar, nodo, self_loops=False)
else:
self.graph.add_node(nodo)
for vecino in otro_grafo.neighbors(nodo):
self.graph.add_edge(nodo, vecino)
if __name__ == "__main__":
# Ejemplo de uso
memoria_nlp = MemoriaRobotNLP(max_size=100)
json_parser = JSONParser(memoria_nlp)
api_requester = APIRequester()
url = "https://jsonplaceholder.typicode.com/posts"
data = api_requester.make_request(url)
if data:
json_parser.parse_json(data)
json_parser.draw_graph()
otro_parser = JSONParser(MemoriaRobotNLP(max_size=100))
otro_parser.parse_json({"id": 101, "title": "New Title", "userId": 11})
print("Uniendo los grafos...")
json_parser.unir_grafos(otro_parser.graph, umbral=80)
print("Grafo unido:")
json_parser.draw_graph()
json_parser.guardar_en_memoria()
else:
print("Error al realizar la solicitud a la API.")
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