app.py

from PyPDF2 import PdfReader
from transformers import pipeline, T5ForConditionalGeneration, T5Tokenizer
import torch
import gradio as gr
from gtts import gTTS

# Проверка доступности GPU
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

# Инициализация модели для суммаризации
summarizer = pipeline("summarization", model="facebook/bart-large-cnn", device=0 if device.type == "cuda" else -1)

# Инициализация модели для перевода
model_translation = T5ForConditionalGeneration.from_pretrained('utrobinmv/t5_translate_en_ru_zh_small_1024')
model_translation.to(device)
tokenizer_translation = T5Tokenizer.from_pretrained('utrobinmv/t5_translate_en_ru_zh_small_1024')

def parse_pdf(pdf_file):
    """Функция для извлечения текста из PDF файла."""
    reader = PdfReader(pdf_file)
    extracted_text = ""
    for page in reader.pages:
        extracted_text += page.extract_text() or ""
    return extracted_text

def summarize(text, max_length=1000, min_length=150):
    """Функция для суммаризации текста."""
    max_length = 1000  # Можно настроить
    truncated_text = text[:max_length]
    result = summarizer(truncated_text, max_length=max_length, min_length=min_length, do_sample=False)
    return result[0]['summary_text']

def translate(text):
    """Функция для перевода текста на русский."""
    prefix = 'translate to ru: '
    src_text = prefix + text

    input_ids = tokenizer_translation(src_text, return_tensors="pt")
    generated_tokens = model_translation.generate(**input_ids.to(device))

    result = tokenizer_translation.batch_decode(generated_tokens, skip_special_tokens=True)
    return result[0]

def text_to_speech(text, language='ru'):
    """Функция для преобразования текста в аудиофайл."""
    tts = gTTS(text=text, lang=language)
    audio_file = "output.mp3"
    tts.save(audio_file)
    return audio_file

def process_pdf(pdf_file):
    """Основная функция обработки PDF файла."""
    if not pdf_file:
        return "No input provided."

    # Извлечение текста из PDF
    extracted_text = parse_pdf(pdf_file)
    print(f"Extracted Text: {extracted_text}")

    # Суммаризация текста
    summary = summarize(extracted_text)
    print(f"Summary: {summary}")

    # Перевод текста на русский
    translated_text = translate(summary)
    print(f"Translated Text: {translated_text}")

    # Преобразование текста в аудио
    audio_file = text_to_speech(translated_text)
    return translated_text, audio_file

# Создание Gradio интерфейса
with gr.Blocks() as demo:
    gr.Markdown("# PDF Summarizer, Translator, and Text-to-Speech")
    gr.Markdown("Upload a PDF file to summarize, translate to Russian, and convert to audio.")

    pdf_input = gr.File(label="Upload PDF File", type="filepath")
    text_output = gr.Textbox(label="Translated Text", lines=10)
    audio_output = gr.Audio(label="Generated Audio", type="filepath")
    process_button = gr.Button("Process PDF")

    process_button.click(process_pdf, inputs=pdf_input, outputs=[text_output, audio_output])

# Запуск приложения
demo.launch(debug=True)