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newsapi.py
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import mysecrets
import os
import sys
import math
import pandas as pd
from pathlib import Path
import os.path
import glob
import aiohttp
import asyncio
import requests
from urllib.parse import urlparse
import json
import time
import smtplib
import random
import hashlib
import glob
from difflib import SequenceMatcher
import datetime
#from datetime import timezone
from dateutil import parser
DATA_PATH = Path.cwd()
keywordsFields = ["keyword","language","topic","topicColor","keywordColor","limitPages","ratioNew"]
keywordsDF = pd.read_csv(DATA_PATH / 'keywords.csv', delimiter=',') #,index_col='keyword'
keywordsDF['uniqueString'] = keywordsDF['keyword'] + "_" + keywordsDF['language'] + "_" + keywordsDF['topic']
keywordsDF['crc'] = keywordsDF['uniqueString'].apply(
lambda x:
hashlib.sha256(x.encode()).hexdigest()
)
keywordsDF = keywordsDF.sort_values(by=['ratioNew'], ascending=False)
def getNewsFiles():
fileName = './csv/news_????_??.csv'
files = glob.glob(fileName)
return files
def getNewsDFbyList(files):
newsDF = pd.DataFrame(None)
for file in files:
df = pd.read_csv(file, delimiter=',')
if(newsDF.empty):
newsDF = df
else:
newsDF = pd.concat([newsDF, df])
if(not newsDF.empty):
newsDF = newsDF.sort_values(by=['published'], ascending=True)
return newsDF
def getNewsDF():
files = getNewsFiles()
newsDF = getNewsDFbyList(files)
return newsDF
newsDf = getNewsDF()
keywordsNewsDF = pd.DataFrame(None)
if(not newsDf.empty):
keywordsNewsDF = newsDf.groupby('keyword').count()
keywordsNewsDF = keywordsNewsDF.drop(columns = ['language'])
'''
newsDf['age'] = newsDf['published'].apply(
lambda x:
datetime.datetime.now(datetime.timezone.utc) - parser.parse(x)
)
'''
keywordsNewsDF2 = pd.DataFrame(None)
if(not keywordsNewsDF.empty):
keywordsNewsDF2 = pd.merge(keywordsDF, keywordsNewsDF, how='left', left_on=['keyword'], right_on=['keyword'])
keywordsNewsDF2['index'] = keywordsNewsDF2['index'].fillna(0)
keywordsNewsDF2['index'] = keywordsNewsDF2['index'] - keywordsNewsDF2['ratioNew']
keywordsNewsDF2 = keywordsNewsDF2.sort_values(by=['index'], ascending=True)
rows20 = int(math.ceil(keywordsNewsDF2.shape[0]/5))
keywordsNewsDF2 = keywordsNewsDF2.head(rows20)
print(keywordsNewsDF2)
rows20 = int(math.ceil(keywordsDF.shape[0]/5))
keywordsDF3 = keywordsDF.head(rows20)
print(keywordsDF3)
searchWords = dict(zip(keywordsDF.keyword.values, keywordsDF.language.values))
#print(keywordsDF)
#print(searchWords)
#print(keywordsDF.sample() )
stopDomains = ["www.mydealz.de", "www.techstage.de", "www.nachdenkseiten.de", "www.amazon.de", "www.4players.de", "www.netzwelt.de", "www.nextpit.de",
"www.mein-deal.com", "www.sparbote.de", "www.xda-developers.com" "www.pcgames.de", "blog.google", "www.ingame.de", "playstation.com",
"www.pcgameshardware.de", "9to5mac.com", "roanoke.com", "billingsgazette.com", "richmond.com", "www.rawstory.com", "slate.com",
"www.computerbild.de", "www.giga.de", "www.heise.de", "www.chip.de"
]
#https://github.com/theSoenke/news-crawler/blob/master/data/feeds_de.txt
def dataIsNotBlocked(data):
for blocked in stopDomains:
if blocked in data['domain']:
return False
return True
#replace/drop: "https://www.zeit.de/zustimmung?url="
#get url data (inq) -> check if keyword in title|description and url equal
#see 'https://www.stern.de/panorama/weltgeschehen/news-heute---ocean-viking--rettet-mehr-als-40-menschen-aus-dem-mittelmeer-30598826.html'
collectedNews = {}
def addNewsToCollection(data):
global collectedNews
pubDate = parser.parse(data['published'])
fileDate = 'news_'+pubDate.strftime('%Y_%m')+'.csv'
if(fileDate in collectedNews):
if(not data['url'] in collectedNews[fileDate]):
if(not 'archive' in data):
data = archiveUrl(data)
collectedNews[fileDate][data['url']] = data
return True
return False
def storeCollection():
global collectedNews
print("Inside store")
cols = ['url','valid','domain','title','description','image','published','archive','content','quote','language','keyword']
for dateFile in collectedNews:
df = pd.DataFrame.from_dict(collectedNews[dateFile], orient='index', columns=cols)
df = removeDuplicates(df)
#df.to_csv(DATA_PATH / dateFile, index=True)
if(not os.path.exists(DATA_PATH / 'csv')):
os.mkdir(DATA_PATH / 'csv')
print(["store file: ", dateFile])
df.to_csv(DATA_PATH / 'csv' / dateFile, index_label='index')
collectedNews = {}
# self.randomWordsDF = pd.DataFrame.from_dict(self.randomWords, orient='index', columns=self.randomBase.keys())
# self.randomWordsDF.to_csv(DATA_PATH / self.category / "csv" / ("words_random_"+str(self.randomSize)+".csv"), index=True)
#https://web.archive.org/save/https://translate.google.com/translate?sl=de&tl=en&u=
#https://web.archive.org/save/https://translate.google.com/translate?sl=de&tl=en&u=https://www.nikos-weinwelten.de/beitrag/weinbau_reagiert_auf_den_klimawandel_abschied_vom_oechsle_hin_zur_nachhaltigkeit/
# https://docs.aiohttp.org/en/stable/client_reference.html
#
async def saveArchive(saveUrl):
async with aiohttp.ClientSession() as session:
async with session.get(saveUrl, timeout=120) as response:
print("x")
async def getArchives(urlList):
async with aiohttp.ClientSession() as session:
async with session.get(saveUrl) as response:
print("x")
def findArchives(articles):
foundArticles = []
for article in articles:
data = extractData(article, language, keyWord)
if (dataIsNotBlocked(data)):
a=1
def similar(a, b):
return SequenceMatcher(None, a, b).ratio()
def removeDuplicates(df1):
df1['md5'] = ''
df1['group'] = ''
df1['similarity'] = 0.0
df1 = df1.sort_values(by=['published'], ascending=True)
for index, column in df1.iterrows():
quote = str(column['domain']) + ' ' + str(column['title']) + ' ' + str(column['description'])
md5 = hashlib.md5(quote.encode('utf-8')).hexdigest()
df1.loc[index,'md5'] = md5
day = '1970-01-01'
if(len(str(column['published']))>5):
pubDate = parser.parse(column['published'])
day = pubDate.strftime('%Y-%m-%d')
groupTxt = str(column['domain']) + ' ' + day
group = hashlib.md5(groupTxt.encode('utf-8')).hexdigest()
df1.loc[index,'group'] = group
df1 = df1[~df1.md5.duplicated(keep='first')]
for index1, column1 in df1.iterrows():
quote1 = str(column1['title']) + ' ' + str(column1['description'])
df2 = df1[df1['group']==column1['group']]
for index2, column2 in df2.iterrows():
if(column1['md5']>column2['md5']):
quote2 = str(column2['title']) + ' ' + str(column2['description'])
similarity = similar(quote1,quote2)
if(similarity > df1.loc[index1,'similarity']):
df1.loc[index1,'similarity'] = similarity
df3 = df1[df1['similarity']<0.8]
df3 = df3.drop(columns=['md5', 'group', 'similarity'])
df3 = df3.sort_values(by=['published'], ascending=True)
return df3
def archiveUrl(data):
#timetravelDate = datetime.datetime.strptime(data['published'], '%Y-%m-%d %H:%M:%S').strftime('%Y%m%d')
#pubDate = datetime.datetime.fromisoformat(data['published'])
#pubDate = parser.isoparse(data['published'])
timetravelDate = '19700101'
pubDate = None
try:
pubDate = parser.parse(data['published'])
except:
print('date parse error 1')
if(not pubDate):
try:
pubDate = parser.isoparse(data['published'])
except:
print('date parse error 2')
if(pubDate):
timetravelDate = pubDate.strftime('%Y%m%d')
timetravelUrl = 'http://timetravel.mementoweb.org/api/json/'+timetravelDate+'/'+data['url']
try:
page = requests.get(timetravelUrl, timeout=30)
if page.status_code == 200:
content = page.content
#print(content)
if(content):
#print(content)
jsonData = json.loads(content)
if(jsonData and jsonData['mementos']):
data['archive'] = jsonData['mementos']['closest']['uri'][0]
if('1970-01-01T00:00:00' == data['published']):
data['published'] = jsonData['mementos']['closest']['datetime']
#'closest'
except:
# except Exception as e:
# except json.decoder.JSONDecodeError as e:
# except requests.exceptions.RequestException as e:
e = sys.exc_info()[0]
print("not archived yet")
saveUrl = 'https://web.archive.org/save/' + data['url'] # archive.org
#saveUrl = 'https://archive.is/submit/'
#saveUrl = 'https://archive.ph/submit/'
## pip3 install aiohttp
try:
loop = asyncio.get_event_loop()
loop.run_until_complete(saveArchive(saveUrl))
except:
e2 = sys.exc_info()[0]
print("something more went wrong (timeout/redirect/...)")
#async with aiohttp.ClientSession() as session:
# async with session.get(saveUrl) as response:
# print(await response.status())
'''
try:
page = requests.get(saveUrl, timeout=240) # archive.org
#page = requests.post(saveUrl, data = {'url':data['url']}, timeout=240)
if page.status_code == 200:
print('archived!')
except requests.exceptions.RequestException as e2:
print("not archivable: " + data['url'])
'''
return data
def extractData(article, language, keyWord):
title = article['title']
description = article['description']
url = article['url']
#later use list...
url = url.replace('https://www.zeit.de/zustimmung?url=', '')
url = url.replace('%3A', ':')
url = url.replace('%2F', '/')
domain = urlparse(url).netloc
image = None
if('urlToImage' in article):
image = article['urlToImage']
published = '1970-01-01T00:00:00'
if('publishedAt' in article):
published = article['publishedAt']
content = article['content']
data = {'url':url, 'valid':0, 'domain':domain,'published':published, 'description':description, 'title':title,
'image':image, 'content':content, 'quote':'', 'language': language, 'keyword':keyWord}
return data
def checkArticlesForKeywords(articles, keywordsDF, seldomDF, language, keyWord):
keywordsLangDF = keywordsDF[keywordsDF['language']==language]
foundArticles = []
for article in articles:
data = extractData(article, language, keyWord)
searchQuote = str(data['title']) + " " + str(data['description'])
foundKeywords = []
found = False
for index2, column2 in keywordsLangDF.iterrows():
keyword = column2['keyword']
allFound = True
keywords = keyword.strip("'").split(" ")
for keyw in keywords:
allFound = allFound and (keyw in searchQuote)
if(allFound):
foundKeywords.append(keyword)
found = True
# add seldom keywords twice if
for index2, column2 in seldomDF.iterrows():
keyword = column2['keyword']
allFound = True
keywords = keyword.strip("'").split(" ")
for keyw in keywords:
allFound = allFound and (keyw in searchQuote)
if(allFound):
foundKeywords.append(keyword)
found = True
if(found):
foundKeywords.append(keyWord)
data['keyword'] = random.choice(foundKeywords)
foundArticles.append(data)
return foundArticles
def filterNewAndArchive(articles, language, keyWord):
global collectedNews
newArticles = []
startTime = time.time()
for data in articles:
##data = extractData(article, language, keyWord)
if (dataIsNotBlocked(data)):
pubDate = parser.parse(data['published'])
fileDate = 'news_'+pubDate.strftime('%Y_%m')+'.csv'
if(not fileDate in collectedNews):
if(os.path.isfile(DATA_PATH / 'csv' / fileDate)):
df = pd.read_csv(DATA_PATH / 'csv' / fileDate, delimiter=',',index_col='index')
collectedNews[fileDate] = df.to_dict('index')
else:
collectedNews[fileDate] = {}
if(not data['url'] in collectedNews[fileDate]):
data = archiveUrl(data)
newArticles.append(data)
if((time.time() - startTime) > 60*10):
return newArticles
return newArticles
def getNewsFiles(state='harvest'):
fileName = './csv/news_????_??.csv'
if(state):
fileName = './csv/news_'+state+'_????_??.csv'
files = glob.glob(fileName)
return files
def getLatestFileAge():
minAge = 1E6
now = time.time()
for fileName in getNewsFiles(state=None):
print([os.path.getatime(fileName),os.path.getctime(fileName),os.path.getmtime(fileName)])
modifyDate = os.path.getmtime(fileName)
fileAge = now-modifyDate
print(fileAge)
if(fileAge<minAge):
minAge = fileAge
return minAge
def inqRandomNews():
apiKey = os.getenv('NEWSAPI_KEY')
if(apiKey == '1a2b3c4d5e6f7a8b9c0d1e2f3a4b5c6d7'):
print('Please set newsapi.org key in file: mysecrets.py');
return None
rndKey = keywordsDF.sample()
randomNumber = random.random()
print(['randomNumber: ',randomNumber])
if(not keywordsNewsDF2.empty):
if(randomNumber>0.8):
print("DF2 seldoms")
rndKey = keywordsNewsDF2.sample()
if(not keywordsDF3.empty):
if(randomNumber<0.4):
print("DF3 successors")
rndKey = keywordsDF3.sample()
#if FoundAny: newLimit = minimum(currPage+1,limitPage)
#if foundNothing: newLimit = maximum(1,random.choice(range(currPage-1,limitPage-1)))
## cheat for now!
### keywordEmptyDF = keywordsDF[keywordsDF['keyword']=="'Gasheizung'"]
### rndKey = keywordEmptyDF.sample()
## rm in final version
#keyWord = random.choice(searchWords)
#language = 'de'
#language = 'en'
#language = 'fr'
crc = rndKey['crc'].iloc[0]
keyWord = rndKey['keyword'].iloc[0]
language = rndKey['language'].iloc[0]
limitPages = rndKey['limitPages'].iloc[0]
ratioNew = rndKey['ratioNew'].iloc[0]
currPage = random.choice(range(1,limitPages+1))
newLimit = max(1,random.choice(range(currPage-1,limitPages)))
currRatio = ratioNew
print([keyWord, language])
if(not 'xx'==language):
sort = random.choice(['relevancy', 'popularity', 'publishedAt'])
pageSize = 33
print('keyword: '+keyWord+'; Page: '+str(currPage))
# https://newsapi.org/docs/endpoints/everything
url = ('https://newsapi.org/v2/everything?'+
#'q="'+keyWord+'"&'
'q='+keyWord+'&'
'pageSize='+str(pageSize)+'&'
'language='+language+'&'
'page='+str(currPage)+'&'
'sortBy='+sort+'&'
'apiKey='+apiKey
#'excludeDomains=www.zeit.de,www.reuters.com'
)
# sortBy=relevancy : relevancy, popularity, publishedAt
response = requests.get(url)
response.encoding = response.apparent_encoding
foundNew = False
if(response.text):
jsonData = json.loads(response.text)
if ('ok'==jsonData['status']):
currRatio = 0
if(jsonData['totalResults']>0):
currRatio = jsonData['totalResults']/1E7
if(len(jsonData['articles']) > 0):
currRatio += len(jsonData['articles'])/1E3
deltaLimit = 0
#newLimit = limitPages
if(len(jsonData['articles']) > 20):
deltaLimit += 1
#newLimit = max(currPage+1,limitPages)
print('#found Articles: '+str(len(jsonData['articles'])))
checkedArticles = checkArticlesForKeywords(jsonData['articles'], keywordsDF, keywordsNewsDF2,language, keyWord)
print('#checked Articles: '+str(len(checkedArticles)))
print("archive first")
newArticles = filterNewAndArchive(checkedArticles, language, keyWord)
print('#new Articles: '+str(len(newArticles)))
if(len(newArticles) in [1,2]):
print("sleep")
time.sleep(60)
print("add to collection")
currRatio += len(newArticles)/len(jsonData['articles'])
if(currRatio>0.5):
deltaLimit += 1
#newLimit = max(currPage+2,limitPages)
newLimit = max(currPage+deltaLimit,limitPages)
print(['currRatio',currRatio,'currPage: ',currPage,' limitPages: ',limitPages,' deltaLimit: ',deltaLimit,' new Limit: ', newLimit])
for data in newArticles:
if (dataIsNotBlocked(data)):
#print(str(keyWord)+': '+str(title)+' '+str(url))
print(["addNewsToCollection: ",data])
if(addNewsToCollection(data)):
foundNew = True
print(["+++added"])
else:
print(["---not added"])
#print(["collectedNews: ",collectedNews])
if(foundNew):
storeCollection()
else:
print(response.text)
if(jsonData['code'] == 'maximumResultsReached'):
deltaLimit = -1
newLimit = min(1,currPage+deltaLimit)
# {"status":"error","code":"maximumResultsReached","message":"You have requested too many results. Developer accounts are limited to a max of 100 results. You are trying to request results 100 to 150. Please upgrade to a paid plan if you need more results."}
#print(rndKey.index)
#keywordsDF.at[rndKey.index, 'limitPages'] = newLimit
keywordsDF.loc[keywordsDF['crc'] == crc, 'limitPages'] = newLimit
keywordsDF.loc[keywordsDF['crc'] == crc, 'ratioNew'] = currRatio*0.15+ratioNew*0.85
#b'{"status":"ok","totalResults":1504,
# "articles":[{"source":{"id":null,"name":"heise online"},
# "author":"Stefan Krempl",
# "title":"Wissenschaftler: Klimawandel tr\xc3\xa4gt zum Starkregen bei\xe2\x80\x8b",
# "description":"Die Wolkenbr\xc3\xbcche mit katastrophalen Folgen geh\xc3\xb6ren so nicht mehr zur \xc3\xbcblichen Wetter-Varianz, meinen Wissenschaftler. Sie fordern einen Umbau der Infrastruktur.",
# "url":"https://www.heise.de/news/Wissenschaftler-Klimawandel-traegt-zum-Starkregen-bei-6140856.html",
# "urlToImage":"https://heise.cloudimg.io/bound/1200x1200/q85.png-lossy-85.webp-lossy-85.foil1/_www-heise-de_/imgs/18/3/1/3/9/7/8/0/Ueberschwemmung-c06f751f2932e14b.jpeg",
# "publishedAt":"2021-07-16T15:06:00Z",
# "content":"Nach den langen und heftigen Regenf\xc3\xa4llen Mitte der Woche treten immer mehr katastrophale Folgen zutage: Die Zahl der Toten w\xc3\xa4chst, allein in Rheinland-Pfalz und Nordrhein-Westfalen sind \xc3\xbcber 100 Mens\xe2\x80\xa6 [+4840 chars]"}
'''
#time.sleep(80000)
i=1
while True:
###while (i<50):
print(i)
inqRandomNews()
i += 1
#time.sleep(200) # unless drop none-french
#time.sleep(20)
time.sleep(1000)
age = getLatestFileAge()
print(age)
if(age>60*60*5*0):
inqRandomNews()
'''
inqRandomNews()
#keywordsDF = keywordsDF.sort_values(by=['topic','keyword'])
keywordsDF = keywordsDF.sort_values(by=['ratioNew'], ascending=False)
keywordsDF.to_csv(DATA_PATH / 'keywords.csv', columns=keywordsFields,index=False)
'''
i=1
while True:
###while (i<50):
print(i)
inqRandomNews()
i += 1
#time.sleep(200) # unless drop none-french
#time.sleep(20)
keywordsDF = keywordsDF.sort_values(by=['ratioNew'], ascending=False)
keywordsDF.to_csv(DATA_PATH / 'keywords.csv', columns=keywordsFields,index=False)
time.sleep(1000)
'''