persian-text-summarization.ipynb

{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.10.12","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"gpu","dataSources":[],"dockerImageVersionId":30823,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":true}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"# # This Python 3 environment comes with many helpful analytics libraries installed\n# # It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n# # For example, here's several helpful packages to load\n\n# import numpy as np # linear algebra\n# import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n# # Input data files are available in the read-only \"../input/\" directory\n# # For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\n# import os\n# for dirname, _, filenames in os.walk('/kaggle/input'):\n#     for filename in filenames:\n#         print(os.path.join(dirname, filename))\n\n# # You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n# # You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"!pip uninstall -y httpx\n!pip install httpx==0.23.0\n!pip install gradio==3.16.0\n!pip install googletrans==4.0.0-rc1\n!pip install transformers==4.30.2\n!pip install torch==2.0.1\n!pip uninstall -y tensorflow keras tensorflow-text\n!pip install tensorflow==2.11 keras==2.11 transformers==4.30.2\n!pip install torch==2.0.1 sentencepiece\n!pip install googletrans==4.0.0-rc1","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"import gradio as gr\nfrom googletrans import Translator\nfrom transformers import pipeline","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# Initialize translator and summarization model\ntranslator = Translator()\nsummarizer = pipeline(\"summarization\", model=\"facebook/bart-large-cnn\")\n","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# # Initialize translator and summarization model\n# translator = Translator()\n# summarizer = pipeline(\"summarization\", model=\"facebook/bart-large-cnn\")\n\n# Function to translate text\ndef translate_text(text, target_lang):\n    translation = translator.translate(text, dest=target_lang)\n    return translation.text\n\n# Function to summarize and translate back\ndef summarize_and_translate(input_text):\n    # Translate to English\n    translated_text = translate_text(input_text, \"en\")\n    \n    # Summarize the translated text\n    summary = summarizer(translated_text, max_length=130, min_length=30, do_sample=False)\n    summarized_text = summary[0][\"summary_text\"]\n    \n    # Translate summary back to Persian\n    summarized_back = translate_text(summarized_text, \"fa\")\n    \n    return {\n        \"translated_text\": translated_text,\n        \"summary_text\": summarized_text,\n        \"translated_back\": summarized_back\n    }\n\n# Create Gradio interface\ndef run_interface():\n    with gr.Blocks() as demo:\n        gr.Markdown(\"\"\"# متن خود را وارد کنید و عملیات ترجمه و خلاصه‌سازی را انجام دهید.\"\"\")\n        \n        with gr.Row():\n            input_text = gr.Textbox(label=\"متن ورودی (فارسی)\", placeholder=\"متن خود را اینجا وارد کنید.\", lines=5)\n        \n        with gr.Row():\n            output_translated = gr.Textbox(label=\"متن ترجمه شده (انگلیسی)\", lines=5)\n            output_summary = gr.Textbox(label=\"خلاصه (انگلیسی)\", lines=5)\n            output_back = gr.Textbox(label=\"متن خلاصه‌شده ترجمه‌شده (فارسی)\", lines=5)\n        \n        def process(input_text):\n            results = summarize_and_translate(input_text)\n            return results[\"translated_text\"], results[\"summary_text\"], results[\"translated_back\"]\n        \n        submit_button = gr.Button(\"اجرا\")\n        submit_button.click(process, inputs=[input_text], outputs=[output_translated, output_summary, output_back])\n\n    demo.launch()\n\nif __name__ == \"__main__\":\n    run_interface()\n","metadata":{"trusted":true},"outputs":[],"execution_count":null}]}