# app.py
import time
import gradio as gr
from transformers import pipeline

# Пример: sentiment-analysis
TASK = "text-classification"
MODEL_NAME_1 = "s-nlp/russian_toxicity_classifier"

pipe = pipeline(TASK, model=MODEL_NAME_1)

MAX_CHARS = 2000

def run(text: str):
    if text is None or not text.strip():
        return "Ошибка: пустой ввод.", None, None
    
    text = text.strip()
    if len(text) > MAX_CHARS:
        text = text[:MAX_CHARS]

    t0 = time.time()
    try:
        result = pipe(text)
        latency = round((time.time() - t0) * 1000, 1)
        return "ОК.", result, f"{latency} ms"
    except Exception as e:
        return f"Ошибка: {type(e).__name__}: {e}", None, None

with gr.Blocks() as demo:
    gr.Markdown(f"""
    # NLP-приложение (Hugging Face Spaces + Gradio)
    **Задача:** {TASK}  
    **Модель:** {MODEL_NAME_1}
    """)

    inp = gr.Textbox(label="Введите текст", lines=6, placeholder="Скопируйте сюда текст...")
    btn = gr.Button("Обработать")
    status = gr.Textbox(label="Статус")
    out = gr.JSON(label="Результат модели")
    latency = gr.Textbox(label="Время ответа")

    btn.click(run, inputs=inp, outputs=[status, out, latency])

    gr.Examples(
        examples=[
            ["Я тебя люблю."],
            ["Я тебя ненавижу."],
            ["Все в порядке."]
        ],
        inputs=inp
    )

demo.launch()