import gradio as gr import tabulate import matplotlib.pyplot as plt import networkx as nx def render_dependency_tree(words, parents, labels): fig, ax = plt.subplots(figsize=(10, 6)) # Create a directed graph G = nx.DiGraph() # Adding nodes to the graph for i, word in enumerate(words): G.add_node(i, label=word) # Adding edges with labels for i, (parent, label) in enumerate(zip(parents, labels)): if parent != -1: G.add_edge(parent, i, label=label) # Position nodes using Graphviz pos = nx.nx_agraph.graphviz_layout(G, prog='dot') # Draw the graph nx.draw(G, pos, ax=ax, with_labels=True, labels=nx.get_node_attributes(G, 'label'), arrows=True, node_color='white', node_size=3000) # Draw edge labels edge_labels = nx.get_edge_attributes(G, 'label') nx.draw_networkx_edge_labels(G, pos, ax=ax, edge_labels=edge_labels, rotate=False) return fig description = """

Norsk UD (Bokmål og Nynorsk)

""" text = """1 President President PROPN NNP Number=Sing 5 nsubj 5:nsubj _ 2 Bush Bush PROPN NNP Number=Sing 1 flat 1:flat _ 3 on on ADP IN _ 4 case 4:case _ 4 Tuesday Tuesday PROPN NNP Number=Sing 5 obl 5:obl:on _ 5 nominated nominate VERB VBD Mood=Ind|Number=Sing|Person=3|Tense=Past|VerbForm=Fin 0 root 0:root _ 6 two two NUM CD NumType=Card 7 nummod 7:nummod _ 7 individuals individual NOUN NNS Number=Plur 5 obj 5:obj _ 8 to to PART TO _ 9 mark 9:mark _ 9 replace replace VERB VB VerbForm=Inf 5 advcl 5:advcl:to _ 10 retiring retire VERB VBG VerbForm=Ger 11 amod 11:amod _ 11 jurists jurist NOUN NNS Number=Plur 9 obj 9:obj _ 12 on on ADP IN _ 14 case 14:case _ 13 federal federal ADJ JJ Degree=Pos 14 amod 14:amod _ 14 courts court NOUN NNS Number=Plur 11 nmod 11:nmod:on _ 15 in in ADP IN _ 18 case 18:case _ 16 the the DET DT Definite=Def|PronType=Art 18 det 18:det _ 17 Washington Washington PROPN NNP Number=Sing 18 compound 18:compound _ 18 area area NOUN NN Number=Sing 14 nmod 14:nmod:in SpaceAfter=No 19 . . PUNCT . _ 5 punct 5:punct _""" forms = [ line.split("\t")[1] for line in text.split("\n") if line and not line.startswith("#") ] lemmas = [ line.split("\t")[2] for line in text.split("\n") if line and not line.startswith("#") ] upos = [ line.split("\t")[3] for line in text.split("\n") if line and not line.startswith("#") ] xpos = [ line.split("\t")[4] for line in text.split("\n") if line and not line.startswith("#") ] feats = [ line.split("\t")[5] for line in text.split("\n") if line and not line.startswith("#") ] metadata = [ line.split("\t")[9] for line in text.split("\n") if line and not line.startswith("#") ] edges = [ int(line.split("\t")[6]) for line in text.split("\n") if line and not line.startswith("#") ] edge_labels = [ line.split("\t")[7] for line in text.split("\n") if line and not line.startswith("#") ] def render_table(forms, lemmas, upos, xpos, feats, metadata, edges, edge_labels): feats = [[f"*{f.split('=')[0]}:* {f.split('=')[1]}" for f in (feat.split("|")) if '=' in f] for feat in feats] max_len = max(1, max([len(feat) for feat in feats])) feats = [feat + [""] * (max_len - len(feat)) for feat in feats] feats = list(zip(*feats)) array = [ [""] + forms, ["*LEMMAS:*"] + lemmas, ["*UPOS:*"] + upos, ["*XPOS:*"] + xpos, ["*UFEATS:*"] + list(feats[0]), *([""] + list(row) for row in feats[1:]) ] #return tabulate.tabulate(array, headers="firstrow", tablefmt="unsafehtml") return {"value": array[1:], "headers": array[0]} custom_css = \ """ /* Hide sort buttons at gr.DataFrame */ .sort-button { display: none !important; } """ with gr.Blocks(theme='sudeepshouche/minimalist', css=custom_css) as demo: gr.HTML(description) gr.DataFrame(**render_table(forms, lemmas, upos, xpos, feats, metadata, edges, edge_labels), interactive=False, datatype="markdown") gr.Plot(render_dependency_tree(forms, edges, edge_labels), interactive=False) demo.launch()