def long_process(NAME):
print('enters long process')
verbose = True
ar = online_app_backend.call_from_front_end(NAME, verbose=verbose)
print(ar)
axes, fig = bplot.plot_author(ar, NAME)
return axes, fig
def long_process(NAME):
#return
print('enters long process')
#NAME = "S S Phatak"
verbose = True
ar = online_app_backend.call_from_front_end(NAME, verbose=verbose)
print(ar)
axes, fig = bplot.plot_author(ar, NAME)
out_url = fig_to_uri(fig)
return out_url
theme = px.colors.diverging.Portland
colors = [theme[0], theme[1]]
st.title('Search Reading Difficulty of Academic')
author_name = st.text_input('Enter Author:')
def make_clickable(link):
# target _blank to open new window
# extract clickable text to display for your link
text = link #.split('=')[1]
return f'<a target="_blank" href="{link}">{text}</a>'
if author_name:
ar = call_from_front_end(author_name)
standard_sci = [t['standard'] for t in ar]
group_labels = ['Author: ' + str(author_name)] #, 'Group 2', 'Group 3']
scraped_labels = [str(x['link']) for x in ar]
lods = []
for i, j, k in zip(standard_sci,
[str(author_name) for i in range(0, len(ar))],
scraped_labels):
lods.append({'Reading_Level': i, 'Origin': j, 'Web_Link': k})
df1 = pd.DataFrame(lods)
df = pd.concat([df1, df0])
fig0 = px.histogram(
df,
x="Reading_Level",
#SComplexity.t_analysis
import online_app_backend
import argparse
parser = argparse.ArgumentParser(description='Process some authors.')
parser.add_argument('author', metavar='N', type=str, nargs='+',
help='authors first name')
args = parser.parse_args()
NAME = args.author
online_app_backend.call_from_front_end(NAME)