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scisctractGen.py
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scisctractGen.py
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# #!/usr/bin/env python
# Scrapes data using https://arxiv.org/ API, parses it using beaufiful soup 4.
# Saves output to .csv file and runs a keras example using the scrapped web data for training.
# Generates scientific word vomits.
# Author: TristanCB
import time
import requests
import csv
from bs4 import BeautifulSoup
import pandas as pd
import argparse
import kerasCholetTextGenExample
def str2bool(v: str or bool) -> bool:
"""
Taken from: https://stackoverflow.com/questions/15008758/parsing-boolean-values-with-argparse
Function which handles string arguments given to argparse to work with bool logic in main program
"""
if isinstance(v, bool):
return v
if v.lower() in ('yes', 'true', 't', 'y', '1'):
return True
elif v.lower() in ('no', 'false', 'f', 'n', '0'):
return False
else:
raise argparse.ArgumentTypeError('Boolean value expected.')
# Accept command line arguments
parser = argparse.ArgumentParser()
parser.add_argument('--topic', type=str, help='A topic of your choice', default='Fluids')
parser.add_argument('--outputData', type=str, help='Output filename. Should end with .csv', default='dataTrain.csv')
parser.add_argument('--queryAmnt', type=int, help='Amount of papers returned for single request made with API', default=50)
parser.add_argument('--totalAmnt', type=int, help='Total amount of papers we wish to scan', default=2000)
parser.add_argument('--fromSave', type=str2bool, help='starts example from previously generated .csv file', default=False)
args = parser.parse_args()
# Get vars
topic = args.topic
outputData = args.outputData
fromSave = args.fromSave
amntPerQuery = args.queryAmnt
total = args.totalAmnt
# Generate appropriate start indices depending on amount of entries requested
linSpace = [i for i in range(1, total, amntPerQuery)]
# If we do not already have data, use the following loop to scrape data from arxiv.org using provided API
# For more info on API refer to the user manual: https://arxiv.org/help/api/user-manual
if fromSave != True:
# Append the data to a csv file for future use.
with open(outputData, 'w', newline='', encoding="utf-8") as csvfile:
def writeEntry(summary : str, abstract : str):
"""
Small function to append data
"""
spamwriter = csv.writer(csvfile)
spamwriter.writerow([summary,abstract])
# Writes header:
writeEntry('summary','abstract')
# Get data in batches
for start in linSpace:
# Construct query.
url = f'http://export.arxiv.org/api/query?search_query=all:{topic}&start={start}&max_results={amntPerQuery}'
print(f"Extracting data using url: {url}")
# Get text data from request
request = requests.get(url).text
# Soup object to parse out data obtained from the previously made request.
soup = BeautifulSoup(request,'lxml')
# Get all papers.
entries = soup.find_all('entry')
# Extract titles and summaries using list comprehension and strip white space.
titles = [i.title.string.strip(" ").lower() for i in entries]
summaries = [i.summary.string.strip(" ").lower() for i in entries]
# Make sure both lengths match
assert len(titles) == len(summaries)
# Iterates through both lists to append data to csv using function
for title, summary in zip(titles,summaries):
writeEntry(summary,title)
# Retrieve data from csv file.
dataTrain = pd.read_csv(outputData)
# Assemble all the text from web scrapped summaries
text = dataTrain[['summary']].sum()[0]
# Use keras example to generate text
kerasCholetTextGenExample.run(text)