app.py

import gradio as gr
from transformers import pipeline
from transformers import AutoTokenizer

models = {
    "RUPunct-small": "RUPunct/RUPunct_small",
    "RUPunct-big": "RUPunct/RUPunct_big",
    "RUPunct-medium": "RUPunct/RUPunct_medium"
}

pipelines = {}

for model_name, model_path in models.items():
    tokenizer = AutoTokenizer.from_pretrained(model_path, strip_accents=False, add_prefix_space=True)
    pipelines[model_name] = pipeline("ner", model=model_path, tokenizer=tokenizer, aggregation_strategy="first")

def process_token(token, label):
    if label == "LOWER_O":
        return token
    if label == "LOWER_PERIOD":
        return token + "."
    if label == "LOWER_COMMA":
        return token + ","
    if label == "LOWER_QUESTION":
        return token + "?"
    if label == "LOWER_TIRE":
        return token + "—"
    if label == "LOWER_DVOETOCHIE":
        return token + ":"
    if label == "LOWER_VOSKL":
        return token + "!"
    if label == "LOWER_PERIODCOMMA":
        return token + ";"
    if label == "LOWER_DEFIS":
        return token + "-"
    if label == "LOWER_MNOGOTOCHIE":
        return token + "..."
    if label == "LOWER_QUESTIONVOSKL":
        return token + "?!"
    if label == "UPPER_O":
        return token.capitalize()
    if label == "UPPER_PERIOD":
        return token.capitalize() + "."
    if label == "UPPER_COMMA":
        return token.capitalize() + ","
    if label == "UPPER_QUESTION":
        return token.capitalize() + "?"
    if label == "UPPER_TIRE":
        return token.capitalize() + " —"
    if label == "UPPER_DVOETOCHIE":
        return token.capitalize() + ":"
    if label == "UPPER_VOSKL":
        return token.capitalize() + "!"
    if label == "UPPER_PERIODCOMMA":
        return token.capitalize() + ";"
    if label == "UPPER_DEFIS":
        return token.capitalize() + "-"
    if label == "UPPER_MNOGOTOCHIE":
        return token.capitalize() + "..."
    if label == "UPPER_QUESTIONVOSKL":
        return token.capitalize() + "?!"
    if label == "UPPER_TOTAL_O":
        return token.upper()
    if label == "UPPER_TOTAL_PERIOD":
        return token.upper() + "."
    if label == "UPPER_TOTAL_COMMA":
        return token.upper() + ","
    if label == "UPPER_TOTAL_QUESTION":
        return token.upper() + "?"
    if label == "UPPER_TOTAL_TIRE":
        return token.upper() + " —"
    if label == "UPPER_TOTAL_DVOETOCHIE":
        return token.upper() + ":"
    if label == "UPPER_TOTAL_VOSKL":
        return token.upper() + "!"
    if label == "UPPER_TOTAL_PERIODCOMMA":
        return token.upper() + ";"
    if label == "UPPER_TOTAL_DEFIS":
        return token.upper() + "-"
    if label == "UPPER_TOTAL_MNOGOTOCHIE":
        return token.upper() + "..."
    if label == "UPPER_TOTAL_QUESTIONVOSKL":
        return token.upper() + "?!"


def punctuate(input_text, model_name):
    classifier = pipelines[model_name]
    
    preds = classifier(input_text)
    output = ""
    for item in preds:
        if item["word"] == ".":
            item["entity_group"] = "O"
        output += " " + process_token(item['word'].strip(), item['entity_group'])

    return output.strip()

iface = gr.Interface(
    fn=punctuate,
    inputs=[
        gr.inputs.Textbox(lines=5, placeholder="Введите текст"),
        gr.inputs.Radio(list(models.keys()), label="Модель")
    ],
    outputs="text",
    title="RUPunct",
    description="Демо RUPunct - модели для автоматической расстановки знаков препинания в русском тексте.",
)

iface.launch()