import json
import os
import pickle
import re

import gradio as gr
import matplotlib.pyplot as plt
import networkx as nx
from tqdm import tqdm


def load_json_from_path(path):
    with open(path, "r", encoding="utf8") as f:
        obj = json.loads(f.read())

    return obj


class Visualizer:

    def __init__(self, cache_root="."):
        self.iso_codes_to_names = load_json_from_path(os.path.join(cache_root, "iso_to_fullname.json"))
        for code in self.iso_codes_to_names:
            self.iso_codes_to_names[code] = re.sub("\(.*?\)", "", self.iso_codes_to_names[code])

        tree_lookup_path = os.path.join(cache_root, "lang_1_to_lang_2_to_tree_dist.json")
        tree_dist = load_json_from_path(tree_lookup_path)
        distances = list()
        for lang_1 in tree_dist:
            if lang_1 not in self.iso_codes_to_names:
                continue
            for lang_2 in tree_dist[lang_1]:
                if lang_2 not in self.iso_codes_to_names:
                    continue
                if lang_1 != lang_2:
                    distances.append((self.iso_codes_to_names[lang_1], self.iso_codes_to_names[lang_2], tree_dist[lang_1][lang_2]))
        min_dist = min(d for _, _, d in distances)
        max_dist = max(d for _, _, d in distances)
        self.tree_distances = [(entity1, entity2, (d - min_dist) / (max_dist - min_dist)) for entity1, entity2, d in distances]

        map_lookup_path = os.path.join(cache_root, "lang_1_to_lang_2_to_map_dist.json")
        map_dist = load_json_from_path(map_lookup_path)
        distances = list()
        for lang_1 in map_dist:
            if lang_1 not in self.iso_codes_to_names:
                continue
            for lang_2 in map_dist[lang_1]:
                if lang_2 not in self.iso_codes_to_names:
                    continue
                if lang_1 != lang_2:
                    distances.append((self.iso_codes_to_names[lang_1], self.iso_codes_to_names[lang_2], map_dist[lang_1][lang_2]))
        min_dist = min(d for _, _, d in distances)
        max_dist = max(d for _, _, d in distances)
        self.map_distances = [(entity1, entity2, (d - min_dist) / (max_dist - min_dist)) for entity1, entity2, d in distances]

        asp_dict_path = os.path.join(cache_root, "asp_dict.pkl")
        with open(asp_dict_path, 'rb') as dictfile:
            asp_sim = pickle.load(dictfile)
        lang_list = list(asp_sim.keys())
        asp_dist = dict()
        seen_langs = set()
        for lang_1 in lang_list:
            if lang_1 not in seen_langs:
                seen_langs.add(lang_1)
                asp_dist[lang_1] = dict()
            for index, lang_2 in enumerate(lang_list):
                if lang_2 not in seen_langs:  # it's symmetric
                    asp_dist[lang_1][lang_2] = 1 - asp_sim[lang_1][index]
        distances = list()
        for lang_1 in asp_dist:
            if lang_1 not in self.iso_codes_to_names:
                continue
            for lang_2 in asp_dist[lang_1]:
                if lang_2 not in self.iso_codes_to_names:
                    continue
                if lang_1 != lang_2:
                    distances.append((self.iso_codes_to_names[lang_1], self.iso_codes_to_names[lang_2], asp_dist[lang_1][lang_2]))
        min_dist = min(d for _, _, d in distances)
        max_dist = max(d for _, _, d in distances)
        self.asp_distances = [(entity1, entity2, (d - min_dist) / (max_dist - min_dist)) for entity1, entity2, d in distances]

    def visualize(self, distance_type, neighbor, num_neighbors):
        plt.clf()
        plt.figure(figsize=(12, 12))

        assert distance_type in ["Physical Distance between Language Centroids on the Globe",
                                 "Distance to the Lowest Common Ancestor in the Language Family Tree",
                                 "Angular Distance between the Frequencies of Phonemes"]
        if distance_type == "Distance to the Lowest Common Ancestor in the Language Family Tree":
            normalized_distances = self.tree_distances
        elif distance_type == "Angular Distance between the Frequencies of Phonemes":
            normalized_distances = self.asp_distances
        elif distance_type == "Physical Distance between Language Centroids on the Globe":
            normalized_distances = self.map_distances

        G = nx.Graph()
        d_dist = list()
        for entity1, entity2, d in tqdm(normalized_distances):
            if neighbor == entity2 or neighbor == entity1:
                d_dist.append(d)
        thresh = sorted(d_dist)[num_neighbors]
        neighbors = set()
        for entity1, entity2, d in tqdm(normalized_distances):
            if d <= thresh and (neighbor == entity2 or neighbor == entity1) and len(neighbors) < num_neighbors + 1:
                neighbors.add(entity1)
                neighbors.add(entity2)
                spring_tension = ((thresh + 0.1) - d) * 100  # for vis purposes
                G.add_edge(entity1, entity2, weight=spring_tension)
        neighbors.remove(neighbor)
        thresh_for_neighbors = max([x for _, _, x in normalized_distances])
        for entity1, entity2, d in tqdm(normalized_distances):
            if entity2 in neighbors and entity1 in neighbors:
                spring_tension = (thresh_for_neighbors + 0.1) - d
                G.add_edge(entity1, entity2, weight=spring_tension)

        pos = nx.spring_layout(G, weight="weight", iterations=200, threshold=1e-6)  # Positions for all nodes
        edges = G.edges(data=True)
        nx.draw_networkx_nodes(G, pos, node_size=1, alpha=0.01)
        edges_connected_to_specific_node = [(u, v) for u, v in G.edges() if u == neighbor or v == neighbor]
        nx.draw_networkx_edges(G, pos, edgelist=edges_connected_to_specific_node, edge_color='orange', alpha=0.4, width=3)
        if num_neighbors < 6:
            edges_not_connected_to_specific_node = [(u, v) for u, v in G.edges() if u != neighbor and v != neighbor]
            nx.draw_networkx_edges(G, pos, edgelist=edges_not_connected_to_specific_node, edge_color='gray', alpha=0.05, width=1)
        for u, v, d in edges:
            if u == neighbor or v == neighbor:
                nx.draw_networkx_edge_labels(G, pos, edge_labels={(u, v): round(((thresh + 0.1) - (d['weight'] / 100)) * 100, 2)}, font_color="red", alpha=0.4)  # reverse modifications
        nx.draw_networkx_labels(G, pos, font_size=14, font_family='sans-serif', font_color='green')
        nx.draw_networkx_labels(G, pos, labels={neighbor: neighbor}, font_size=14, font_family='sans-serif', font_color='red')
        plt.title(f'Graph of {distance_type}')
        plt.subplots_adjust(left=0, right=1, top=0.9, bottom=0)
        plt.tight_layout()
        return plt.gcf()


if __name__ == '__main__':
    vis = Visualizer(cache_root=".")
    text_selection = [f"{vis.iso_codes_to_names[iso_code]}" for iso_code in vis.iso_codes_to_names]
    iface = gr.Interface(fn=vis.visualize,
                         inputs=[gr.Dropdown(["Physical Distance between Language Centroids on the Globe",
                                              "Distance to the Lowest Common Ancestor in the Language Family Tree",
                                              "Angular Distance between the Frequencies of Phonemes"],
                                             type="value",
                                             value='Physical Distance between Language Centroids on the Globe',
                                             label="Select the Type of Distance"),
                                 gr.Dropdown(text_selection,
                                             type="value",
                                             value="German",
                                             label="Select the second Language (type on your keyboard to find it quickly)"),
                                 gr.Slider(minimum=0, maximum=100, step=1,
                                           value=12,
                                           label="How many Nearest Neighbors should be displayed?")
                                 ],
                         outputs=[gr.Plot(label="", show_label=False, format="png", container=True)],
                         description="<br><br> This demo allows you to find the nearest neighbors of a language from the ISO 639-3 list according to several distance measurement functions. "
                                     "For more information, check out our paper: https://arxiv.org/abs/2406.06403 and our text-to-speech tool, in which we make use of "
                                     "this technique: https://github.com/DigitalPhonetics/IMS-Toucan <br><br>",
                         allow_flagging="never")
    iface.launch()