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Runtime error
| from typing import Dict, List, Union | |
| import gradio as gr | |
| from lexenlem.preprocessing.adhoc import AdHocLemmatizer | |
| title = "Lexicon-enhanced lemmatization for Estonian" | |
| with open("./article.md") as file: | |
| article: str = file.read() | |
| with open("./description.txt") as file: | |
| description: str = file.read() | |
| models: Dict[str, AdHocLemmatizer] = { | |
| "Base": AdHocLemmatizer(path="vb_stanza_no_compound_no_deriv.pt", use_stanza=True), | |
| "Compound separators": AdHocLemmatizer( | |
| path="vb_stanza_compound.pt", use_stanza=True, allow_compound_separator=True, allow_derivation_sign=False | |
| ), | |
| "UD format": AdHocLemmatizer( | |
| path="vb_stanza_symbols.pt", use_stanza=True, allow_compound_separator=True, allow_derivation_sign=True | |
| ), | |
| } | |
| choices = list(models.keys()) | |
| examples: List[List[Union[str, bool]]] = [] | |
| with open("examples.tsv") as file: | |
| for line in file: | |
| ex, index = line.split("\t") | |
| index = int(index) | |
| examples.append( | |
| [ex, choices[index]] | |
| ) | |
| def predict(text: str, key: str) -> List[str]: | |
| if key not in models: | |
| raise RuntimeError("Unknown model") | |
| return models[key](text) | |
| demo = gr.Interface( | |
| fn=predict, | |
| title=title, | |
| description=description, | |
| article=article, | |
| inputs=[ | |
| gr.inputs.Textbox(lines=7, label="Text to lemmatize"), | |
| gr.inputs.Radio(choices=choices, label="Mode") | |
| ], | |
| outputs=[ | |
| gr.outputs.Textbox(label="Lemmas") | |
| ], | |
| examples=examples, | |
| allow_screenshot=False, | |
| allow_flagging="never", | |
| ) | |
| demo.launch( | |
| debug=False, | |
| enable_queue=True, | |
| cache_examples=True, | |
| ) | |