import os
import gradio as gr
from transformers import pipeline
import pdfplumber
import docx2txt
from docx import Document
from fpdf import FPDF
from langdetect import detect
import urllib.request
import gradio.themes as gt

# === 🗂️ Создание папок и установка шрифта ===
os.makedirs("/app/models", exist_ok=True)
FONT_PATH = "DejaVuSans.ttf"

# Если шрифт отсутствует — скачиваем
if not os.path.exists(FONT_PATH):
    print("⬇️ Загружаю шрифт DejaVuSans.ttf ...")
    urllib.request.urlretrieve(
        "https://github.com/dejavu-fonts/dejavu-fonts/raw/master/ttf/DejaVuSans.ttf",
        FONT_PATH
    )

# === ⚙️ Загрузка моделей один раз при запуске ===
def load_model(task, model_name):
    print(f"🔹 Загружается модель: {model_name}")
    return pipeline(task, model=model_name, cache_dir="/app/models")

summarizers = {
    "en": load_model("summarization", "facebook/bart-large-cnn"),
    "ru": load_model("summarization", "IlyaGusev/mbart_ru_sum_gazeta"),
    "kz": load_model("summarization", "csebuetnlp/mT5_multilingual_XLSum")
}

# === 📄 Функция чтения текстов из файлов ===
def read_file(file):
    if not file:
        return ""
    filename = file.name.lower()
    text = ""

    try:
        if filename.endswith(".pdf"):
            with pdfplumber.open(file.name) as pdf:
                for page in pdf.pages:
                    page_text = page.extract_text()
                    if page_text:
                        text += page_text + "\n"
        elif filename.endswith(".docx"):
            text = docx2txt.process(file.name)
        else:
            text = file.read().decode("utf-8", errors="ignore")
    except Exception as e:
        return f"⚠️ Ошибка при чтении файла: {e}"

    return text.strip()

# === 🌐 Определение языка ===
def detect_language(text):
    try:
        lang = detect(text)
        if lang.startswith("ru"):
            return "ru"
        elif lang.startswith("kk") or any(x in text for x in "әіңғүұқөһ"):
            return "kz"
        else:
            return "en"
    except:
        return "en"

# === 🧠 Суммаризация текста ===
def summarize_text(text):
    if not text or len(text) < 50:
        return "⚠️ Недостаточно текста для анализа.", "❌", "❌", 0, 0, "❌"

    lang = detect_language(text)
    model = summarizers.get(lang, summarizers["en"])

    flags = {"ru": "🇷🇺 Русский", "kz": "🇰🇿 Қазақ тілі", "en": "🇬🇧 English"}
    lang_label = flags.get(lang, "🌍 Unknown")

    model_label = model.model.name_or_path if hasattr(model.model, "name_or_path") else "Custom"
    chunk_size = 2500
    overlap = 200
    summaries = []

    for i in range(0, len(text), chunk_size - overlap):
        chunk = text[i:i + chunk_size]
        try:
            result = model(chunk, max_length=180, min_length=40, do_sample=False)
            summaries.append(result[0]['summary_text'])
        except Exception as e:
            summaries.append(f"[Ошибка в части {len(summaries)+1}: {e}]")

    summary = "\n\n".join(summaries).strip()
    src_len = len(text)
    sum_len = len(summary)
    compression = round(100 * (1 - sum_len / src_len), 1) if src_len > 0 else 0

    return summary, lang_label, model_label, src_len, sum_len, f"{compression}%"

# === 💾 Сохранение файлов ===
def save_summary_as_txt(summary_text):
    path = "summary.txt"
    with open(path, "w", encoding="utf-8") as f:
        f.write(summary_text)
    return path

def save_summary_as_docx(summary_text):
    path = "summary.docx"
    doc = Document()
    doc.add_heading("Резюме документа", level=1)
    doc.add_paragraph(summary_text)
    doc.save(path)
    return path

def save_summary_as_pdf(summary_text):
    path = "summary.pdf"
    pdf = FPDF()
    pdf.add_page()
    pdf.add_font('DejaVu', '', FONT_PATH, uni=True)
    pdf.set_font('DejaVu', '', 12)
    pdf.multi_cell(0, 10, summary_text)
    pdf.output(path)
    return path

# === 🚀 Главная функция обработки ===
def summarize_file(file):
    text = read_file(file)
    if text.startswith("⚠️"):
        return text, "❌", "❌", 0, 0, "❌", None, None, None

    with gr.Progress(track_tqdm=True) as progress:
        progress(0, desc="🧠 Анализ текста...")
        summary, lang_label, model_label, src_len, sum_len, compression = summarize_text(text)
        progress(1, desc="✅ Готово!")

    txt_path = save_summary_as_txt(summary)
    docx_path = save_summary_as_docx(summary)
    pdf_path = save_summary_as_pdf(summary)

    return summary, lang_label, model_label, src_len, sum_len, compression, txt_path, docx_path, pdf_path

# === 🎨 Интерфейс Gradio ===
custom_theme = gt.Default(
    primary_hue="purple",
    secondary_hue="violet",
).set(
    body_background_fill="#f8f6ff",
    button_primary_background_fill="linear-gradient(90deg, #7e3ff2, #c084fc)",
    button_primary_background_fill_hover="linear-gradient(90deg, #6b21a8, #9333ea)",
)

demo = gr.Interface(
    fn=summarize_file,
    inputs=gr.File(label="📂 Загрузите документ (.pdf, .docx, .txt)"),
    outputs=[
        gr.Textbox(label="🧾 Краткое резюме"),
        gr.Textbox(label="🌍 Определённый язык"),
        gr.Textbox(label="🧠 Используемая модель"),
        gr.Number(label="📄 Длина исходного текста"),
        gr.Number(label="📝 Длина резюме"),
        gr.Textbox(label="📉 Степень сокращения"),
        gr.File(label="📄 Скачать TXT"),
        gr.File(label="📘 Скачать DOCX"),
        gr.File(label="📕 Скачать PDF"),
    ],
    title="🧠 Eroha Summarizer PRO (Автономная версия)",
    description="🚀 Определяет язык (🇷🇺 / 🇰🇿 / 🇬🇧), создаёт краткое резюме и сохраняет в TXT, DOCX, PDF с поддержкой кириллицы.",
    theme=custom_theme,
    allow_flagging="never"
)

demo.launch(server_name="0.0.0.0", server_port=7860)