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This repository was archived by the owner on May 25, 2022. It is now read-only.

Commit 8023218

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‎README.md‎

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37 | [Find IMDB movie ratings](https://github.com/chavarera/python-mini-projects/tree/master/projects/Find%20IMDb%20rating) | [ShivSt](https://github.com/ShivSt)
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38 | [Convert dictionary to python object](https://github.com/chavarera/python-mini-projects/tree/master/projects/convert_dictionary_to_python_object) | [Varun-22](https://github.com/Varun-22)
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39 | [move files to alphabetically arranged folders](https://github.com/chavarera/python-mini-projects/tree/master/projects/Write%20script%20to%20move%20files%20into%20alphabetically%20ordered%20folder) | [Chathura Nimesh](https://github.com/kana800/)
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40 | [Scrape Youtube video comment](https://github.com/chavarera/python-mini-projects/tree/master/projects/Web%20scraping%20a%20youtube%20comment) | [Saicharan67](https://github.com/Saicharan67)
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41 | [Website Summerization](https://github.com/chavarera/python-mini-projects/tree/master/projects/Web%20scraping%20a%20youtube%20comment) | [Believe Ohiozua](https://github.com/believeohiozua)
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42 | [Text To speech(mp3)](https://github.com/chavarera/python-mini-projects/tree/master/projects/TextToSpeech) | [Sergej Dikun](https://github.com/Serhazor)
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‎projects/TextToSpeech/abc.txt‎

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Thanks to Gail Cleaver, Beth Barrack, Bingo Nightly, Emily Webber and Sharon Counts. Finally, special thanks to Casey Cromwell. Radio Lab is produced by WNYC New York public radio, and distributed by NPR, National Public Radio.

‎projects/TextToSpeech/txtToSpeech.py‎

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from gtts import gTTS
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import os
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file = open("abc.txt", "r").read()
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speech = gTTS(text=file, lang='en', slow=False)
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speech.save("voice.mp3")
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os.system("voice.mp3")
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#print(file)

‎projects/TextToSpeech/voice.mp3‎

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# -*- coding: utf-8 -*-
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"""
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Created on Tue Jul 21 16:55:39 2020
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@author: hp
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"""
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from selenium import webdriver
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import csv
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import time
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items=[]
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driver=webdriver.Chrome(r"C:/Users/hp/Anaconda3/chromedriver.exe")
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driver.get('https://www.youtube.com/watch?v=iFPMz36std4')
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driver.execute_script('window.scrollTo(1, 500);')
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#now wait let load the comments
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time.sleep(5)
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driver.execute_script('window.scrollTo(1, 3000);')
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username_elems = driver.find_elements_by_xpath('//*[@id="author-text"]')
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comment_elems = driver.find_elements_by_xpath('//*[@id="content-text"]')
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for username, comment in zip(username_elems, comment_elems):
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item = {}
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item['Author'] = username.text
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item['Comment'] = comment.text
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items.append(item)
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filename = 'C:/Users/hp/Desktop/commentlist.csv'
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with open(filename, 'w', newline='', encoding='utf-8') as f:
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w = csv.DictWriter(f,['Author','Comment'])
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w.writeheader()
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for item in items:
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w.writerow(item)
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# Byte-compiled / optimized / DLL files
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__pycache__/
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*.py[cod]
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*$py.class
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# C extensions
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*.so
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# Distribution / packaging
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.Python
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build/
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develop-eggs/
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dist/
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downloads/
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eggs/
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.eggs/
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lib/
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lib64/
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parts/
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sdist/
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var/
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wheels/
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*.egg-info/
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.installed.cfg
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*.egg
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MANIFEST
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# PyInstaller
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# Usually these files are written by a python script from a template
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# before PyInstaller builds the exe, so as to inject date/other infos into it.
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*.manifest
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*.spec
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# Installer logs
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pip-log.txt
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pip-delete-this-directory.txt
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# Unit test / coverage reports
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htmlcov/
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.tox/
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.coverage
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.coverage.*
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.cache
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nosetests.xml
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coverage.xml
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*.cover
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.hypothesis/
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.pytest_cache/
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# Translations
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*.mo
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*.pot
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# Django stuff:
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*.log
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local_settings.py
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db.sqlite3
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# Flask stuff:
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instance/
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.webassets-cache
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# Scrapy stuff:
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.scrapy
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# Sphinx documentation
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docs/_build/
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# PyBuilder
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target/
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# Jupyter Notebook
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.ipynb_checkpoints
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# pyenv
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.python-version
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# celery beat schedule file
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celerybeat-schedule
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# SageMath parsed files
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*.sage.py
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# Environments
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.env
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.venv
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venv/
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env.bak/
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venv.bak/
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env/
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# Spyder project settings
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.spyderproject
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.spyproject
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# Rope project settings
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.ropeproject
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# mkdocs documentation
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/site
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# mypy
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.mypy_cache/
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__pycache__
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.vscode
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settings.json
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Dependency directories
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node_modules/
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jspm_packages/
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# Optional npm cache directory
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.npm
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.DS_Store
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.DS_Store
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datasets
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datasets/
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new_datasets/
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node_modules
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yarn.lock
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app
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__pycache__/
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dist
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build
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mlclassification-darwin-x64
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release-builds
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Classifi
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app
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dist
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build
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Summarize.spec
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__pycache__
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applog.log
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csv/
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beneficiary.csv
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.DS_Store
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applog.log
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# Website Summarization API
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This project is carried out for the purpose of building a machine learning model for summarising a website from urls;
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## Getting Started
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These instructions will get you a copy of the project up and running on your local machine for development and testing purposes.
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### Prerequisites
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Python distribution
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```
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Anaconda
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```
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### Installing
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Install Anaconda python distribution on your system
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Create a virtual environment called env.
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```
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python -m venv app
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```
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Activate the virtual environment
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```
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LINUX/Mac: source app/bin/activate
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Windows: app\Scripts\activate
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```
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Upgrade to the latest pip
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```
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pip install --upgrade pip
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```
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Install dependencies using requirements file
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```
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pip install -r requirements.txt
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```
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**Note: Your virtual environment must always be activated before running any command**
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## Deployment
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Start app (Make sure to enter a valid website to an existing website)
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Example of valid commands
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```
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python app.py simple --url https://facebook.com --sentence 1 --language english
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python app.py simple --url https://facebook.com
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python app.py simple --url https://korapay.com
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python app.py bulk --path ./csv/valid_websites.csv
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```
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### APIs
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This are command options in full:
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```
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A command line utility for website Summarization.
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-----------------------------------------------
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These are common commands for this app.
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positional arguments:
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action This has to be 'summarize'
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optional arguments:
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-h, --help show this help message and exit
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--website PATH website of the url to be summarised
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## License
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This project is licensed under the MIT License - see the [LICENSE](LICENSE.md) file for details
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