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| import gradio as gr | |
| import pandas as pd | |
| import spacy | |
| import concepcy | |
| LIST_RELATIONS = ["RelatedTo", "FormOf", "IsA", "PartOf", "HasA", "UserFor", "CapableOf", "AtLocation", "Causes", | |
| "HasSubevent", "HasFirstSubevent", "HasLastSubevent", "HasPrerequisite", "HasProperty", | |
| "MotivatedByGoal", "ObstructedBy", "Desires", "CreatedBy", "Synonym", "Antonym", "DistinctFrom", | |
| "DerivedFrom", "SymbolOf", "DefinedAs", "MannerOf", "LocatedNear", "HasContext", "SimilarTo", | |
| "EtymologicallyRelatedTo", "EtymologicallyDerivedFrom", "CausesDesire", "MadeOf", "ReceivesAction", | |
| "ExternalURL"] | |
| nlp = spacy.load("en_core_web_sm") | |
| nlp.add_pipe( | |
| "concepcy", | |
| config={ | |
| "relations_of_interest": LIST_RELATIONS, | |
| "filter_missing_text": True, | |
| "filter_edge_weight": 2, | |
| } | |
| ) | |
| def greet(text, relations, to_enrich): | |
| doc = nlp(text) | |
| outs0, outs1 = [], [] | |
| if "document" in to_enrich: | |
| outs0 = [] | |
| for relation in relations: | |
| for r in doc._.get(relation.lower()).values(): | |
| outs0.extend([[relation, elt["text"]] for elt in r]) | |
| if "token" in to_enrich: | |
| for token in doc: | |
| for relation in relations: | |
| rels = token._.get(relation.lower()) | |
| if len(rels) > 0: | |
| print(rels) | |
| print([[token.text, relation, r["text"]] for r in rels]) | |
| outs1.extend([[token.text, relation, r["text"]] for r in rels]) | |
| return pd.DataFrame(outs0, columns=["relation", "text"]), pd.DataFrame(outs1, columns=["word", "relation", "text"]) | |
| iface = gr.Interface( | |
| fn=greet, | |
| title="Playground for <a href='https://github.com/JulesBelveze/concepcy'>concepCy</a>", | |
| description="This demo enables you to play around with SpaCy's concepCy wrapper, a wrapper for ConceptNet!" | |
| "To get started: enter a piece of text, check the relations you are interested in and if you want to " | |
| "retrieve relations at a document-level and/or token-level.\n" | |
| "Relations will be displayed as tables on the right hand side! Have fun!\n", | |
| inputs=[ | |
| gr.Textbox(placeholder="Enter sentence here...", lines=5, value="I love eating pizzas"), | |
| gr.CheckboxGroup(choices=LIST_RELATIONS, value=["IsA"]), | |
| gr.CheckboxGroup(choices=["document", "token"], value=["document"]) | |
| ], | |
| outputs=[ | |
| gr.Dataframe(headers=["relation", "text"], label="Document-level relations"), | |
| gr.Dataframe(headers=["word", "relation", "text"], label="Token-level relations") | |
| ] | |
| ) | |
| iface.launch() | |