Create app.py

app.py

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+import os
+import pickle
+import re
+
+import gradio as gr
+import matplotlib.pyplot as plt
+import networkx as nx
+from tqdm import tqdm
+
+from Utility.utils import load_json_from_path
+
+
+class Visualizer:
+
+ def __init__(self, cache_root="."):
+ tree_lookup_path = os.path.join(cache_root, "lang_1_to_lang_2_to_tree_dist.json")
+ self.tree_dist = load_json_from_path(tree_lookup_path)
+
+ map_lookup_path = os.path.join(cache_root, "lang_1_to_lang_2_to_map_dist.json")
+ self.map_dist = load_json_from_path(map_lookup_path)
+ largest_value_map_dist = 0.0
+ for _, values in self.map_dist.items():
+ for _, value in values.items():
+ largest_value_map_dist = max(largest_value_map_dist, value)
+ for key1 in self.map_dist:
+ for key2 in self.map_dist[key1]:
+ self.map_dist[key1][key2] = self.map_dist[key1][key2] / largest_value_map_dist
+
+ 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())
+ self.asp_dist = dict()
+ seen_langs = set()
+ for lang_1 in lang_list:
+ if lang_1 not in seen_langs:
+ seen_langs.add(lang_1)
+ self.asp_dist[lang_1] = dict()
+ for index, lang_2 in enumerate(lang_list):
+ if lang_2 not in seen_langs: # it's symmetric
+ self.asp_dist[lang_1][lang_2] = 1 - asp_sim[lang_1][index]
+
+ 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])
+
+ 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":
+ distance_measure = self.tree_dist
+ elif distance_type == "Angular Distance between the Frequencies of Phonemes":
+ distance_measure = self.asp_dist
+ elif distance_type == "Physical Distance between Language Centroids on the Globe":
+ distance_measure = self.map_dist
+
+ distances = list()
+
+ for lang_1 in distance_measure:
+ if lang_1 not in self.iso_codes_to_names:
+ continue
+ for lang_2 in distance_measure[lang_1]:
+ if lang_2 not in self.iso_codes_to_names:
+ continue
+ distances.append((self.iso_codes_to_names[lang_1], self.iso_codes_to_names[lang_2], distance_measure[lang_1][lang_2]))
+
+ G = nx.Graph()
+ min_dist = min(d for _, _, d in distances)
+ max_dist = max(d for _, _, d in distances)
+ normalized_distances = [(entity1, entity2, (d - min_dist) / (max_dist - min_dist)) for entity1, entity2, d in distances]
+
+ d_dist = list()
+ for entity1, entity2, d in tqdm(normalized_distances):
+ if neighbor == entity2 or neighbor == entity1:
+ if entity1 != entity2:
+ 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 (entity1 != entity2):
+ neighbors.add(entity1)
+ neighbors.add(entity2)
+ spring_tension = (thresh - d) * 10 # for vis purposes
+ G.add_edge(entity1, entity2, weight=spring_tension)
+ neighbors.remove(neighbor)
+ for entity1, entity2, d in tqdm(normalized_distances):
+ if entity2 in neighbors and entity1 in neighbors:
+ if entity1 != entity2:
+ spring_tension = thresh - d
+ G.add_edge(entity1, entity2, weight=spring_tension)
+
+ pos = nx.spring_layout(G, weight="weight") # 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)
+ # 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=0.1, 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 - (d['weight'] / 10)) * 10, 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>",
+ fill_width=True,
+ allow_flagging="never")
+ iface.launch()