def save_article(url):
apikey = "087e404043fb6f7df4c4ed55e72f3f7f"
appid = "dc8dc66d"
configuration = aylien_news_api.Configuration()
configuration.api_key['X-AYLIEN-NewsAPI-Application-ID'] = appid
configuration.api_key['X-AYLIEN-NewsAPI-Application-Key'] = apikey
client = aylien_news_api.ApiClient(configuration)
api_instance = aylien_news_api.DefaultApi(client)
try:
print("URRRLLLL:")
print(url)
api_response = api_instance.list_stories(
published_at_end='NOW',
links_permalink=[url],
)
if len(api_response.stories) == 0:
return JsonResponse({"msg": "No article found for this url"},
status=404)
if len(api_response.stories) > 1:
# print(api_response.stories)
return JsonResponse(
{
"msg":
"More than one article found for this url. Articles found: "
+ str(len(api_response.stories))
},
status=404)
story = api_response.stories[0]
save_from_story(story)
return JsonResponse({"msg": "Success"}, status=200)
except ApiException as e:
return JsonResponse({"msg": "Internal API error"}, status=500)
def generate_articles():
"""
Function to generate article models to put into the database.
Args:
none
Returns:
none
"""
apikey = "087e404043fb6f7df4c4ed55e72f3f7f"
appid = "dc8dc66d"
configuration = aylien_news_api.Configuration()
configuration.api_key['X-AYLIEN-NewsAPI-Application-ID'] = appid
configuration.api_key['X-AYLIEN-NewsAPI-Application-Key'] = apikey
client = aylien_news_api.ApiClient(configuration)
api_instance = aylien_news_api.DefaultApi(client)
# 1. Fetch articles
eligible_publishers = [
"theguardian.com", "nytimes.com", "ft.com", "bloomberg.com",
"reuters.com", "apnews.com", "thetimes.co.uk", "washingtonpost.com",
"afp.com", "abcnews.go.com", "time.com", "wsj.com", "economist.com",
"politico.com", "bbc.com", "pbs.com", "thehill.com", "usatoday.com",
"npr.org", "cbsnews.com", "axios.com", "huffpost.com", "newyorker.com",
"nationalreview.com", "slate.com", "theatlantic.com", "theweek.com",
"vanityfair.com", "msnbc.com", "cnn.com",
"theamericanconservative.com", "vox.com", "mic.com",
"independent.co.uk", "thesun.co.uk", "metro.co.uk", "dailymail.co.uk",
"telegraph.co.uk", "latimes.com", "cnet.com", "engadget.com",
"theverge.com", "vice.com", "hollywoodreporter.com", "newsweek.com",
"forbes.com", "sciencemag.org", "rte.com", "natgeo.com",
"wanderlust.co.uk", "skysports.com", "espn.com", "theathletic.co.uk",
"phys.org", "physicsworld.com", "sky.com", "techradar.com",
"entertainmentdaily.co.uk", "digitalspy.com", "inews.co.uk", "ign.com",
"france24.com", "dw.com", "euronews.com", "thelocal.it", "elpais.com",
"cbc.ca", "globalnews.ca", "nationalpost.com", "msn.com",
"nbcnews.com", "abc.net.au", "scmp.com", "seattletimes.com",
"independent.ie", "standard.co.uk", "wired.co.uk", "fortune.com",
"techcrunch.com", "usnews.com"
]
for publisher in eligible_publishers:
api_response = fetch_articles(api_instance, publisher)
if api_response:
stories = api_response.stories
# TBD error handling
# 2. Get the full text for every article
# 2.1 Analyse sentiment
for story in stories:
save_from_story(story)
def config(self, Aylien_ID, Aylien_key):
configuration = aylien_news_api.Configuration()
client = aylien_news_api.ApiClient(configuration)
configuration.api_key['X-AYLIEN-NewsAPI-Application-ID'] = Aylien_ID
configuration.api_key['X-AYLIEN-NewsAPI-Application-Key'] = Aylien_key
self.api_instance = aylien_news_api.DefaultApi(client)
def updateData():
############################################### Update aylien_news.csv ###############################################
df = pd.read_csv("data/aylien_news.csv")
df['date'] = pd.to_datetime(df.date, format='%Y/%m/%d')
df.sort_values(by='date', inplace=True, ascending=False)
configuration = aylien_news_api.Configuration()
configuration.api_key['X-AYLIEN-NewsAPI-Application-ID'] = '97c45927'
configuration = aylien_news_api.Configuration()
configuration.api_key['X-AYLIEN-NewsAPI-Application-Key'] = '8e5a95f7bafa835d340a1be85a90588a'
configuration.host = "https://api.aylien.com/news"
api_instance = aylien_news_api.DefaultApi(aylien_news_api.ApiClient(configuration))
date = list(df["date"])[0]
e = str(datetime.date.today())
s = str((date + datetime.timedelta(days=1)).date())
from_date = s
to_date = str((datetime.datetime.strptime(str(from_date), "%Y-%m-%d") + datetime.timedelta(days=5)).date())
rows = []
crimes = {'burglary': 0, 'robbery': 0, 'murder': 0, 'kidnapping': 0, 'rape': 0}
total_news_count = 0
while dateutil.parser.parse(from_date).date() < dateutil.parser.parse(e).date():
if dateutil.parser.parse(to_date).date() > dateutil.parser.parse(e).date():
to_date = e
for crime in crimes.keys():
opts = {
'title': crime,
'sort_by': 'social_shares_count.facebook',
'language': ['en'],
'published_at_start': from_date + 'T00:00:00Z',
'published_at_end': to_date + 'T00:00:00Z',
'per_page': 100,
'source_locations_country': ['IN'],
'source_locations_state': ['Delhi'],
'source_locations_city': ['Delhi'],
}
headlines = api_instance.list_stories_with_http_info(**opts)
for i in headlines[0].stories:
date = i.published_at.date()
source = i.source.name
title = i.title
url = i.links.permalink
content = i.body
rows.append([date, source, title, crime, url, content])
total_news_count += 1
crimes[crime] += 1
from_date = str((datetime.datetime.strptime(str(to_date), "%Y-%m-%d") + datetime.timedelta(days=1)).date())
to_date = str((datetime.datetime.strptime(str(from_date), "%Y-%m-%d") + datetime.timedelta(days=5)).date())
with open('data/aylien_news.csv', 'a') as f:
writer = csv.writer(f)
writer.writerows(rows)
############################################### Update news_data.csv ###############################################
contents = ""
for i in range(len(rows)):
contents = contents + rows[i][5] + " "
locations = findLocations(contents)
f = open('data/loc.txt', 'r')
s = f.read()
loc = s.split('\n')
delhi_locs = []
for i in loc:
delhi_locs.append(i.lower())
all_locations = list(set(delhi_locs).intersection(locations))
if len(all_locations) > 0:
df1 = pd.read_csv("data/news_data.csv")
d = {}
for i in range(len(df1)):
lst = list(df1.iloc[i])
location = lst[0]
dates_lst = lst[1].split("\t")
dates = []
for j in dates_lst:
dates.append(str(j))
srcs = lst[2].split("\t")
headlines = lst[3].split("\t")
types = lst[4].split("\t")
articles = lst[5].split("\t")
ages_lst = lst[6].split("\t")
ages = []
for i in ages_lst:
ages.append(int(i))
businessmans_lst = lst[7].split("\t")
businessmans = []
for i in businessmans_lst:
businessmans.append(int(i))
urls = lst[8].split("\t")
d[location] = [dates, headlines, types, articles, ages, businessmans, urls, srcs]
business = ['businessman', 'jeweller', 'jeweler', 'shop owner', 'property dealer']
c = 0
for i in range(len(rows)):
article = rows[i][-1] # [date, source, title, crime, url, content]
date = rows[i][0]
headline = rows[i][2]
crime_type = rows[i][3]
news_url = rows[i][-2]
if str(news_url) == "nan":
news_url = "na"
news_src = rows[i][1]
businessman = 0
for j in business:
if j in article:
businessman = 1
break
age = findAge(article)
for i in all_locations:
if i in article.lower():
c += 1
flag = 1
for j in loc:
if i == j.lower() or i == j:
loc_name = j
break
lst = d.get(loc_name, [[], [], [], [], [], [], [], []])
lst[0].append(str(date))
lst[1].append(headline)
lst[2].append(crime_type)
lst[3].append(article)
lst[4].append(age)
lst[5].append(businessman)
lst[6].append(news_url)
lst[7].append(news_src)
d[i] = lst
crime_counts = {}
max_count = 0
for i in d.keys():
crime_counts[i] = len(d[i][0])
if len(d[i][0]) > max_count:
max_count = len(d[i][0])
percentile = {}
for i in crime_counts.keys():
p = 100 - int((crime_counts[i] / max_count) * 100)
percentile[i] = p
rows = []
header = ['location', 'date', 'source', 'headline', 'crime_type', 'article', 'age', 'businessman', 'url',
'percentile']
rows.append(header)
for i in d.keys():
for j in loc:
if i == j.lower() or i == j:
loc_name = j
break
row = [loc_name]
lst = d[i]
row.append('\t'.join(lst[0]))
row.append('\t'.join(lst[7]))
row.append('\t'.join(lst[1]))
row.append('\t'.join(lst[2]))
row.append('\t'.join(lst[3]))
row.append('\t'.join(repr(n) for n in lst[4]))
row.append('\t'.join(repr(n) for n in lst[5]))
row.append('\t'.join(lst[6]))
row.append(percentile[i])
rows.append(row)
data = open('data/news_data.csv', 'w')
writer = csv.writer(data)
writer.writerows(rows)
data.close()
df1 = pd.read_csv("data/news_data.csv")
df2 = pd.read_csv("data/area.csv")
crime_locations = list(df1["location"])
rows = [list(df2.columns)]
for i in range(len(df2)):
loc = df2.iloc[i, 0]
if loc == "AIIMS":
continue
if loc not in crime_locations:
rows.append(list(df2.iloc[i]))
else:
dt = list(df1[df1["location"] == loc].iloc[0])
age_lst = dt[-4].split("\t")
crimes = dt[4].split("\t")
businessman_count = list(dt[-3].split("\t")).count('1')
crime_count = len(age_lst)
age_crimes = {"0-21": 0, "22-50": 0, "50+": 0}
crime_type = {'burglary': 0, 'robbery': 0, 'murder': 0, 'kidnapping': 0, 'rape': 0}
for j in age_lst:
age = int(j)
if age > 0 and age <= 21:
age_crimes["0-21"] += 1
elif age > 21 and age <= 50:
age_crimes["22-50"] += 1
elif age > 50:
age_crimes["50+"] += 1
for j in crimes:
crime_type[j] += 1
lst = list(df2.iloc[i])
lst[1] = crime_count
lst[2] = age_crimes["0-21"]
lst[3] = age_crimes["22-50"]
lst[4] = age_crimes["50+"]
lst[5] = crime_type["murder"]
lst[6] = crime_type["burglary"]
lst[7] = crime_type["robbery"]
lst[8] = crime_type["kidnapping"]
lst[9] = crime_type["rape"]
lst[10] = 100 - dt[-1]
lst[-1] = businessman_count
rows.append(lst)
with open('data/area.csv', 'w') as f:
writer = csv.writer(f)
writer.writerows(rows)
import os
import aylien_news_api
configuration = aylien_news_api.Configuration()
configuration.api_key['X-AYLIEN-NewsAPI-Application-ID'] = os.environ.get(
'NEWSAPI_APP_ID')
configuration.api_key['X-AYLIEN-NewsAPI-Application-Key'] = os.environ.get(
'NEWSAPI_APP_KEY')
client = aylien_news_api.ApiClient(configuration)
api_instance = aylien_news_api.DefaultApi(client)
def get_stories():
"""
Returns a list of story objects
"""
response = api_instance.list_stories(title='Donald Trump',
published_at_start='NOW-6HOURS',
per_page=100)
return response.stories
stories = get_stories()
clustered_stories = {}
clusters = []
for story in stories:
if len(story.clusters) > 0:
cluster = story.clusters[0]
import aylien_news_api
from aylien_news_api.rest import ApiException
from pprint import pprint as pp
import pandas as pd
import time
# import json
## Configure your connection to the API
configuration = aylien_news_api.Configuration()
configuration.api_key['X-AYLIEN-NewsAPI-Application-ID'] = 'xxx'
configuration.api_key['X-AYLIEN-NewsAPI-Application-Key'] = 'xxx'
configuration.host = "https://api.aylien.com/news"
api_instance = aylien_news_api.DefaultApi(aylien_news_api.ApiClient(configuration))
## List our parameters as search operators
opts_energy= {
# 'title': '"energy transition" OR "climate change"',
'title': '"energy transition',
'body': '"oil company" OR "oil companies"',
'language': ['en'],
'published_at_start': 'NOW-30DAYS',
'published_at_end': 'NOW',
'per_page': 100,
'sort_by': 'relevance'
}
opts_digital_twin= {
# 'title': '"energy transition" OR "climate change"',
'title': '"digital twin',
'body': 'upstream E&P',
'language': ['en'],