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.")