diff --git a/science_access/enter_author_name.py b/science_access/enter_author_name.py
index 7cddce82d993f3c60158b0d785555445205441eb..ee997a6e311b7f363c27c5cf6a0baec56262f4d9 100644
--- a/science_access/enter_author_name.py
+++ b/science_access/enter_author_name.py
@@ -31,6 +31,9 @@ from science_access.word_cloud_by_word_len import generate_from_lengths
from science_access.utils import check_passive
import plotly.graph_objects as go
+from typing import List, Any
+import pandas as pd
+
theme = px.colors.diverging.Portland
colors = [theme[-1], theme[-2]]
@@ -211,12 +214,6 @@ def create_giant_strings(ar, not_want_list):
return sci_corpus
-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>'
-
def extra_options(ar, trainingDats, df1):
@@ -353,12 +350,22 @@ def grand_distribution_plot(ar, scraped_labels, standard_sci, df0, author_name="
return df1, fig
-from typing import List, Any
-import pandas as pd
-# import streamlit as st
-# List
-def push_frame_to_screen(contents: Any, readability_vector: List) -> pd.DataFrame():
+
+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>'
+df_links["Web_Link"] = contents["Web_Link"]
+df_links["Reading_Level"] = contents["Reading_Level"]
+df_links.drop_duplicates(subset="Web_Link", inplace=True)
+df_links["Web_Link"] = df_links["Web_Link"].apply(make_clickable)
+df_links = df_links.to_html(escape=False)
+st.write(df_links, unsafe_allow_html=True)
+
+
+def push_frame_to_screen(contents, readability_vector):# -> pd.DataFrame():
if type(contents) is type(list()):
df_links = pd.DataFrame()
df_links["Web_Link"] = pd.Series(contents)
@@ -367,19 +374,16 @@ def push_frame_to_screen(contents: Any, readability_vector: List) -> pd.DataFram
df_links["Web_Link"] = df_links["Web_Link"].apply(make_clickable)
df_links = df_links.to_html(escape=False)
st.write(df_links, unsafe_allow_html=True)
-
- if type(contents) is type(pd.DataFrame()):
+ else:
df_links = pd.DataFrame()
- try:
- df_links["Web_Link"] = contents["Web_Link"]
- df_links["Reading_Level"] = contents["Reading_Level"]
- df_links.drop_duplicates(subset="Web_Link", inplace=True)
- df_links["Web_Link"] = df_links["Web_Link"].apply(make_clickable)
- df_links = df_links.to_html(escape=False)
- st.write(df_links, unsafe_allow_html=True)
-
- except:
- pass
+ #try:
+ df_links["Web_Link"] = contents["Web_Link"]
+ df_links["Reading_Level"] = contents["Reading_Level"]
+ df_links.drop_duplicates(subset="Web_Link", inplace=True)
+ df_links["Web_Link"] = df_links["Web_Link"].apply(make_clickable)
+ df_links = df_links.to_html(escape=False)
+ st.write(df_links, unsafe_allow_html=True)
+
return df_links
diff --git a/science_access/online_app_backend.py b/science_access/online_app_backend.py
index 2628a16f780deb4da8808958aeac274ab7b32bea..01d06cc37649df8fd54a731dcb1f2553a250748f 100644
--- a/science_access/online_app_backend.py
+++ b/science_access/online_app_backend.py
@@ -17,6 +17,9 @@ from tqdm.auto import tqdm
import streamlit as st
from .t_analysis import text_proc
+import streamlit as st
+from dask import compute
+
class tqdm:
"""
@@ -145,7 +148,6 @@ def semantic_scholar_alias(NAME):
return aliases
-import streamlit as st
def visit_semantic_scholar_abstracts(NAME, tns, more_links):
@@ -159,28 +161,31 @@ def visit_semantic_scholar_abstracts(NAME, tns, more_links):
dois, coauthors, titles, visit_urls = author_to_urls(NAME)
for d in tqdm(dois, title="visiting abstracts"):
- paper = sch.paper(d, timeout=8)
-
- urlDat = {}
- if "citationVelocity" in paper.keys():
- urlDat["citationVelocity"] = paper["citationVelocity"]
- if "fieldsOfStudy" in paper.keys():
- urlDat["fieldsOfStudy"] = str(paper["fieldsOfStudy"])
- if "numCitedBy" in paper.keys():
- urlDat["numCitedBy"] = paper["numCitedBy"]
- # urlDat["influentialCitationCount"] = paper["influentialCitationCount"]
- urlDat["semantic"] = True
-
- if "url" in paper.keys():
- urlDat["link"] = paper["title"]
- if aliases is None:
- if "aliases" in paper.keys():
- urlDat["aliases"] = paper["aliases"]
- else:
- pass
- if "abstract" in paper.keys():
- urlDat = text_proc(str(paper["abstract"]), urlDat)
- author_results.append(urlDat)
+ try:
+ paper = sch.paper(d, timeout=16)
+
+ urlDat = {}
+ if "citationVelocity" in paper.keys():
+ urlDat["citationVelocity"] = paper["citationVelocity"]
+ if "fieldsOfStudy" in paper.keys():
+ urlDat["fieldsOfStudy"] = str(paper["fieldsOfStudy"])
+ if "numCitedBy" in paper.keys():
+ urlDat["numCitedBy"] = paper["numCitedBy"]
+ # urlDat["influentialCitationCount"] = paper["influentialCitationCount"]
+ urlDat["semantic"] = True
+
+ if "url" in paper.keys():
+ urlDat["link"] = paper["title"]
+ if aliases is None:
+ if "aliases" in paper.keys():
+ urlDat["aliases"] = paper["aliases"]
+ else:
+ pass
+ if "abstract" in paper.keys():
+ urlDat = text_proc(str(paper["abstract"]), urlDat)
+ author_results.append(urlDat)
+ except:
+ pass
author_results = [
urlDat for urlDat in author_results if not isinstance(urlDat, type(None))
]
@@ -188,7 +193,6 @@ def visit_semantic_scholar_abstracts(NAME, tns, more_links):
return author_results, visit_urls
-from dask import compute
def visit_link_unpaywall(NAME): # ), tns, visit_urls):