def draw_chart(word,n):
table_name = 'PageDetails'
response = db.get_top_urls_for_word(word, n, 'PageDetails')
G = nx.Graph()
G.add_node(word)
#urls = response(['Items'])
# print(response)
for url in response['Items']:
G.add_node(url['url'])
G.add_edge(word,url['url'],weight = url['word_count'])
nx.draw(G,with_labels=True)
plt.savefig("chart.png")
plt.show()
url = stdin.readline().rstrip('\n')
print("Choose (bfs / dfs): ")
search_type = stdin.readline().rstrip('\n')
print("Enter the maximum number of web-pages to crawl (around 200 gives good coverage): ")
max_pages = int(stdin.readline().rstrip('\n'))
# url = "http://www.cmu.edu"
if search_type == 'bfs':
bfs_spider(url, keyword, max_pages)
else:
dfs_spider(url, keyword, max_pages)
print(' ')
print("In the generated star graph, the closer a URL is to the the word in the center, the more is its relevance.")
print("Relevance is decided by the number of times a word occurs in a URL.")
draw_chart(keyword, 15)
else:
top_urls = get_top_urls_for_word(keyword, 10, 'PageDetails')
if top_urls['Count'] == 0:
print('This keyword is not present in the database.')
else:
print(' ')
print('The top 10 pages with this keyword are: ')
for item in top_urls['Items']:
print(item['url'])
print(' ')
print("In the generated star graph, the closer a URL is to the the word in the center, the more is its relevance.")
print("Relevance is decided by the number of times a word occurs in a URL.")
draw_chart(keyword, 10)
# for item in top_urls['Items']:
# print(item['word_count'])
