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Update app.py
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app.py
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@@ -7,13 +7,11 @@ from docx import Document
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from fpdf import FPDF
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from langdetect import detect
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import urllib.request
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import gradio.themes as gt
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# === 🗂️
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os.makedirs("/app/models", exist_ok=True)
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FONT_PATH = "DejaVuSans.ttf"
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# Если шрифт отсутствует — скачиваем
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if not os.path.exists(FONT_PATH):
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print("⬇️ Загружаю шрифт DejaVuSans.ttf ...")
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urllib.request.urlretrieve(
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@@ -21,7 +19,7 @@ if not os.path.exists(FONT_PATH):
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FONT_PATH
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)
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# === ⚙️ Загрузка моделей
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def load_model(task, model_name):
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print(f"🔹 Загружается модель: {model_name}")
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return pipeline(task, model=model_name, cache_dir="/app/models")
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@@ -32,13 +30,12 @@ summarizers = {
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"kz": load_model("summarization", "csebuetnlp/mT5_multilingual_XLSum")
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}
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# === 📄
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def read_file(file):
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if not file:
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return ""
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filename = file.name.lower()
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text = ""
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try:
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if filename.endswith(".pdf"):
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with pdfplumber.open(file.name) as pdf:
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@@ -52,7 +49,6 @@ def read_file(file):
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text = file.read().decode("utf-8", errors="ignore")
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except Exception as e:
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return f"⚠️ Ошибка при чтении файла: {e}"
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return text.strip()
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# === 🌐 Определение языка ===
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@@ -68,18 +64,17 @@ def detect_language(text):
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except:
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return "en"
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# === 🧠 Суммаризация
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def summarize_text(text):
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if not text or len(text) < 50:
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return "⚠️ Недостаточно текста для анализа.", "❌", "❌", 0, 0, "❌"
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lang = detect_language(text)
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model = summarizers.get(lang, summarizers["en"])
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flags = {"ru": "🇷🇺 Русский", "kz": "🇰🇿 Қазақ тілі", "en": "🇬🇧 English"}
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lang_label = flags.get(lang, "🌍 Unknown")
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model_label = model.model.name_or_path if hasattr(model.model, "name_or_path") else "Custom"
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chunk_size = 2500
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overlap = 200
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summaries = []
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@@ -99,7 +94,7 @@ def summarize_text(text):
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return summary, lang_label, model_label, src_len, sum_len, f"{compression}%"
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# === 💾 Сохранение
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def save_summary_as_txt(summary_text):
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path = "summary.txt"
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with open(path, "w", encoding="utf-8") as f:
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@@ -124,7 +119,7 @@ def save_summary_as_pdf(summary_text):
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pdf.output(path)
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return path
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# === 🚀
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def summarize_file(file):
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text = read_file(file)
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if text.startswith("⚠️"):
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return summary, lang_label, model_label, src_len, sum_len, compression, txt_path, docx_path, pdf_path
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# ===
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gr.
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gr.Number(label="
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gr.
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gr.File(label="
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gr.File(label="
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demo.launch(server_name="0.0.0.0", server_port=7860)
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from fpdf import FPDF
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from langdetect import detect
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import urllib.request
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# === 🗂️ Подготовка окружения и шрифта ===
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os.makedirs("/app/models", exist_ok=True)
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FONT_PATH = "DejaVuSans.ttf"
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if not os.path.exists(FONT_PATH):
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print("⬇️ Загружаю шрифт DejaVuSans.ttf ...")
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urllib.request.urlretrieve(
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FONT_PATH
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)
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# === ⚙️ Загрузка моделей ===
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def load_model(task, model_name):
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print(f"🔹 Загружается модель: {model_name}")
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return pipeline(task, model=model_name, cache_dir="/app/models")
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"kz": load_model("summarization", "csebuetnlp/mT5_multilingual_XLSum")
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}
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# === 📄 Чтение документов ===
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def read_file(file):
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if not file:
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return ""
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filename = file.name.lower()
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text = ""
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try:
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if filename.endswith(".pdf"):
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with pdfplumber.open(file.name) as pdf:
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text = file.read().decode("utf-8", errors="ignore")
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except Exception as e:
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return f"⚠️ Ошибка при чтении файла: {e}"
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return text.strip()
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# === 🌐 Определение языка ===
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except:
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return "en"
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# === 🧠 Суммаризация ===
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def summarize_text(text):
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if not text or len(text) < 50:
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return "⚠️ Недостаточно текста для анализа.", "❌", "❌", 0, 0, "❌"
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lang = detect_language(text)
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model = summarizers.get(lang, summarizers["en"])
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flags = {"ru": "🇷🇺 Русский", "kz": "🇰🇿 Қазақ тілі", "en": "🇬🇧 English"}
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lang_label = flags.get(lang, "🌍 Unknown")
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model_label = model.model.name_or_path if hasattr(model.model, "name_or_path") else "Custom"
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chunk_size = 2500
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overlap = 200
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summaries = []
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return summary, lang_label, model_label, src_len, sum_len, f"{compression}%"
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# === 💾 Сохранение результатов ===
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def save_summary_as_txt(summary_text):
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path = "summary.txt"
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with open(path, "w", encoding="utf-8") as f:
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pdf.output(path)
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return path
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# === 🚀 Основная функция ===
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def summarize_file(file):
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text = read_file(file)
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if text.startswith("⚠️"):
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return summary, lang_label, model_label, src_len, sum_len, compression, txt_path, docx_path, pdf_path
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# === 🧩 Современный интерфейс через Blocks (Gradio 4.44+) ===
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with gr.Blocks(css=".gradio-container {max-width: 900px !important}") as demo:
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gr.Markdown("## 🧠 Eroha Summarizer PRO (автономная версия)")
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gr.Markdown("🚀 Определяет язык (🇷🇺 / 🇰🇿 / 🇬🇧), создаёт краткое резюме и сохраняет в TXT, DOCX, PDF с поддержкой кириллицы.")
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with gr.Row():
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file_input = gr.File(label="📂 Загрузите документ (.pdf, .docx, .txt)")
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with gr.Row():
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summary_output = gr.Textbox(label="🧾 Краткое резюме", lines=10)
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with gr.Row():
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lang_output = gr.Textbox(label="🌍 Определённый язык")
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model_output = gr.Textbox(label="🧠 Используемая модель")
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with gr.Row():
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src_len = gr.Number(label="📄 Длина исходного текста")
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sum_len = gr.Number(label="📝 Длина резюме")
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compression = gr.Textbox(label="📉 Степень сокращения")
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with gr.Row():
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txt_file = gr.File(label="📄 TXT файл")
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docx_file = gr.File(label="📘 DOCX файл")
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pdf_file = gr.File(label="📕 PDF файл")
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run_btn = gr.Button("🔍 Сгенерировать резюме", variant="primary")
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run_btn.click(
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summarize_file,
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inputs=[file_input],
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outputs=[
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summary_output, lang_output, model_output,
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src_len, sum_len, compression,
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txt_file, docx_file, pdf_file
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]
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)
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demo.launch(server_name="0.0.0.0", server_port=7860)
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