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CPhub.png ADDED
Coventry PureHuB Dark.png ADDED
Coventry PureHuB light.png ADDED
Indexer.py ADDED
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1
+
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+ import nltk #NLTK for natural language processing tasks
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+ from nltk.corpus import stopwords # list of stop word
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+ from nltk.tokenize import word_tokenize # To tokenize each word
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+ from nltk.stem import PorterStemmer # For specific rules to transform words to their stems
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+
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+
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+ # Preprosessing data before indexing
9
+ with open('scraper_results.json', 'r') as doc: scraper_results=doc.read()
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+
11
+ # Initialize empty lists to store publication name, URL, author, and date
12
+
13
+ pubName = []
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+ pubURL = []
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+ pubCUAuthor = []
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+ pubDate = []
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+
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+ # Load the scraped results using ujson
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+ data_dict = ujson.loads(scraper_results)
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+
21
+ # Get the length of the data_dict (number of publications)
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+ array_length = len(data_dict)
23
+ # Print the number of publications
24
+ print(array_length)
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+
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+ #Seperate name, url, date, author in different file
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+ for item in data_dict:
28
+ pubName.append(item["name"])
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+ pubURL.append(item["pub_url"])
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+ pubCUAuthor.append(item["cu_author"])
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+ pubDate.append(item["date"])
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+ with open('pub_name.json', 'w') as f:ujson.dump(pubName, f)
33
+ with open('pub_url.json', 'w') as f:ujson.dump(pubURL, f)
34
+ with open('pub_cu_author.json', 'w') as f:ujson.dump(pubCUAuthor, f)
35
+ with open('pub_date.json', 'w') as f: ujson.dump(pubDate, f)
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+
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+
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+ #Open a file with publication names in read mode
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+ with open('pub_name.json','r') as f:publication=f.read()
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+
41
+ #Load JSON File
42
+ pubName = ujson.loads(publication)
43
+
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+ #Downloading libraries to use its methods
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+ nltk.download('stopwords')
46
+ nltk.download('punkt')
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+
48
+ #Predefined stopwords in nltk are used
49
+ stop_words = stopwords.words('english')
50
+ stemmer = PorterStemmer()
51
+ pub_list_first_stem = []
52
+ pub_list = []
53
+ pub_list_wo_sc = []
54
+ print(len(pubName))
55
+
56
+ for file in pubName:
57
+ #Splitting strings to tokens(words)
58
+ words = word_tokenize(file)
59
+ stem_word = ""
60
+ for i in words:
61
+ if i.lower() not in stop_words:
62
+ stem_word += stemmer.stem(i) + " "
63
+ pub_list_first_stem.append(stem_word)
64
+ pub_list.append(file)
65
+
66
+ #Removing all below characters
67
+ special_characters = '''!()-—[]{};:'"\, <>./?@#$%^&*_~0123456789+=’‘'''
68
+ for file in pub_list:
69
+ word_wo_sc = ""
70
+ if len(file.split()) ==1 : pub_list_wo_sc.append(file)
71
+ else:
72
+ for a in file:
73
+ if a in special_characters:
74
+ word_wo_sc += ' '
75
+ else:
76
+ word_wo_sc += a
77
+ #print(word_wo_sc)
78
+ pub_list_wo_sc.append(word_wo_sc)
79
+
80
+ #Stemming Process
81
+ pub_list_stem_wo_sw = []
82
+ for name in pub_list_wo_sc:
83
+ words = word_tokenize(name)
84
+ stem_word = ""
85
+ for a in words:
86
+ if a.lower() not in stop_words:
87
+ stem_word += stemmer.stem(a) + ' '
88
+ pub_list_stem_wo_sw.append(stem_word.lower())
89
+
90
+ data_dict = {} #Inverted Index holder
91
+
92
+ # Indexing process
93
+ for a in range(len(pub_list_stem_wo_sw)):
94
+ for b in pub_list_stem_wo_sw[a].split():
95
+ if b not in data_dict:
96
+ data_dict[b] = [a]
97
+ else:
98
+ data_dict[b].append(a)
99
+
100
+ # printing the lenght
101
+ print(len(pub_list_wo_sc))
102
+ print(len(pub_list_stem_wo_sw))
103
+ print(len(pub_list_first_stem))
104
+ print(len(pub_list))
105
+
106
+ # with open('publication_list.json', 'w') as f:
107
+ # ujson.dump(pub_list, f)
108
+
109
+ with open('publication_list_stemmed.json', 'w') as f:
110
+ ujson.dump(pub_list_first_stem, f)
111
+
112
+ with open('publication_indexed_dictionary.json', 'w') as f:
113
+ ujson.dump(data_dict, f)
LICENSE ADDED
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1
+ MIT License
2
+
3
+ Copyright (c) 2023 MalaDeep
4
+
5
+ Permission is hereby granted, free of charge, to any person obtaining a copy
6
+ of this software and associated documentation files (the "Software"), to deal
7
+ in the Software without restriction, including without limitation the rights
8
+ to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
9
+ copies of the Software, and to permit persons to whom the Software is
10
+ furnished to do so, subject to the following conditions:
11
+
12
+ The above copyright notice and this permission notice shall be included in all
13
+ copies or substantial portions of the Software.
14
+
15
+ THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
16
+ IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
17
+ FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
18
+ AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
19
+ LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
20
+ OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
21
+ SOFTWARE.
Scrapper.py ADDED
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1
+ #import os # Module for interacting with the operating system
2
+ import time # Module for time-related operations
3
+ import ujson # Module for working with JSON data
4
+ from random import randint # Module for generating random numbers
5
+ from typing import Dict, List, Any # Type hinting imports
6
+
7
+ import requests # Library for making HTTP requests
8
+ from bs4 import BeautifulSoup # Library for parsing HTML data
9
+ from selenium import webdriver # Library for browser automation
10
+ from selenium.common.exceptions import NoSuchElementException # Exception for missing elements
11
+ from webdriver_manager.chrome import ChromeDriverManager # Driver manager for Chrome (We are using Chromium based )
12
+
13
+
14
+
15
+ # Delete files if present
16
+ # try:
17
+ # os.remove('Authors_URL.txt')
18
+ # os.remove('scraper_results.json')
19
+ # except OSError:
20
+ # pass
21
+
22
+ def write_authors(list1, file_name):
23
+ # Function to write authors' URLs to a file
24
+ with open(file_name, 'w', encoding='utf-8') as f:
25
+ for i in range(0, len(list1)):
26
+ f.write(list1[i] + '\n')
27
+
28
+
29
+ def initCrawlerScraper(seed,max_profiles=500):
30
+ # Initialize driver for Chrome
31
+ webOpt = webdriver.ChromeOptions()
32
+ webOpt.add_experimental_option('excludeSwitches', ['enable-logging'])
33
+ webOpt.add_argument('--ignore-certificate-errors')
34
+ webOpt.add_argument('--incognito')
35
+ webOpt.headless = True
36
+ driver = webdriver.Chrome(ChromeDriverManager().install(), options=webOpt)
37
+ driver.get(seed) # Start with the original link
38
+
39
+ Links = [] # Array with pureportal profiles URL
40
+ pub_data = [] # To store publication information for each pureportal profile
41
+
42
+ nextLink = driver.find_element_by_css_selector(".nextLink").is_enabled() # Check if the next page link is enabled
43
+ print("Crawler has begun...")
44
+ while (nextLink):
45
+ page = driver.page_source
46
+ # XML parser to parse each URL
47
+ bs = BeautifulSoup(page, "lxml") # Parse the page source using BeautifulSoup
48
+
49
+ # Extracting exact URL by spliting string into list
50
+ for link in bs.findAll('a', class_='link person'):
51
+ url = str(link)[str(link).find('https://pureportal.coventry.ac.uk/en/persons/'):].split('"')
52
+ Links.append(url[0])
53
+
54
+ # Click on Next button to visit next page
55
+ try:
56
+ if driver.find_element_by_css_selector(".nextLink"):
57
+ element = driver.find_element_by_css_selector(".nextLink")
58
+ driver.execute_script("arguments[0].click();", element)
59
+ else:
60
+ nextLink = False
61
+ except NoSuchElementException:
62
+ break
63
+
64
+ # Check if the maximum number of profiles is reached
65
+ if len(Links) >= max_profiles:
66
+ break
67
+
68
+ print("Crawler has found ", len(Links), " pureportal profiles")
69
+ write_authors(Links, 'Authors_URL.txt') # Write the authors' URLs to a file
70
+
71
+ print("Scraping publication data for ", len(Links), " pureportal profiles...")
72
+ count = 0
73
+ for link in Links:
74
+ # Visit each link to get data
75
+ time.sleep(1) # Delay of 1 second to hit next data
76
+ driver.get(link)
77
+ try:
78
+ if driver.find_elements_by_css_selector(".portal_link.btn-primary.btn-large"):
79
+ element = driver.find_elements_by_css_selector(".portal_link.btn-primary.btn-large")
80
+ for a in element:
81
+ if "research output".lower() in a.text.lower():
82
+ driver.execute_script("arguments[0].click();", a)
83
+ driver.get(driver.current_url)
84
+ # Get name of Author
85
+ name = driver.find_element_by_css_selector("div[class='header person-details']>h1")
86
+ r = requests.get(driver.current_url)
87
+ # Parse all the data via BeautifulSoup
88
+ soup = BeautifulSoup(r.content, 'lxml')
89
+
90
+ # Extracting publication name, publication url, date and CU Authors
91
+ table = soup.find('ul', attrs={'class': 'list-results'})
92
+ if table != None:
93
+ for row in table.findAll('div', attrs={'class': 'result-container'}):
94
+ data = {}
95
+ data['name'] = row.h3.a.text
96
+ data['pub_url'] = row.h3.a['href']
97
+ date = row.find("span", class_="date")
98
+
99
+ rowitem = row.find_all(['div'])
100
+ span = row.find_all(['span'])
101
+ data['cu_author'] = name.text
102
+ data['date'] = date.text
103
+ print("Publication Name :", row.h3.a.text)
104
+ print("Publication URL :", row.h3.a['href'])
105
+ print("CU Author :", name.text)
106
+ print("Date :", date.text)
107
+ print("\n")
108
+ pub_data.append(data)
109
+ else:
110
+ # Get name of Author
111
+ name = driver.find_element_by_css_selector("div[class='header person-details']>h1")
112
+ r = requests.get(link)
113
+ # Parse all the data via BeautifulSoup
114
+ soup = BeautifulSoup(r.content, 'lxml')
115
+ # Extracting publication name, publication url, date and CU Authors
116
+ table = soup.find('div', attrs={'class': 'relation-list relation-list-publications'})
117
+ if table != None:
118
+ for row in table.findAll('div', attrs={'class': 'result-container'}):
119
+ data = {}
120
+ data["name"] = row.h3.a.text
121
+ data['pub_url'] = row.h3.a['href']
122
+ date = row.find("span", class_="date")
123
+ rowitem = row.find_all(['div'])
124
+ span = row.find_all(['span'])
125
+ data['cu_author'] = name.text
126
+ data['date'] = date.text
127
+ print("Publication Name :", row.h3.a.text)
128
+ print("Publication URL :", row.h3.a['href'])
129
+ print("CU Author :", name.text)
130
+ print("Date :", date.text)
131
+ print("\n")
132
+ pub_data.append(data)
133
+ except Exception:
134
+ continue
135
+
136
+ print("Crawler has scrapped data for ", len(pub_data), " pureportal publications")
137
+ driver.quit()
138
+ # Writing all the scraped results in a file with JSON format
139
+ with open('scraper_results.json', 'w') as f:
140
+ ujson.dump(pub_data, f)
141
+
142
+
143
+ initCrawlerScraper('https://pureportal.coventry.ac.uk/en/organisations/coventry-university/persons/', max_profiles=500)
144
+
145
+
146
+
app.py ADDED
@@ -0,0 +1,231 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import streamlit as st
2
+ from PIL import Image
3
+ import ujson
4
+ from sklearn.feature_extraction.text import TfidfVectorizer
5
+ from sklearn.metrics.pairwise import cosine_similarity
6
+ from nltk.tokenize import word_tokenize
7
+ from nltk.corpus import stopwords
8
+ from nltk.stem import PorterStemmer
9
+
10
+ import nltk
11
+ nltk.download('stopwords')
12
+ nltk.download('punkt')
13
+
14
+
15
+ # Set up the NLTK components
16
+ stemmer = PorterStemmer()
17
+ stop_words = stopwords.words('english')
18
+ tfidf = TfidfVectorizer()
19
+
20
+ # Load the data
21
+ with open('publication_list_stemmed.json', 'r') as f:
22
+ pub_list_first_stem = ujson.load(f)
23
+ with open('publication_indexed_dictionary.json', 'r') as f:
24
+ pub_index = ujson.load(f)
25
+ with open('author_list_stemmed.json', 'r') as f:
26
+ author_list_first_stem = ujson.load(f)
27
+ with open('author_indexed_dictionary.json', 'r') as f:
28
+ author_index = ujson.load(f)
29
+ with open('author_names.json', 'r') as f:
30
+ author_name = ujson.load(f)
31
+ with open('pub_name.json', 'r') as f:
32
+ pub_name = ujson.load(f)
33
+ with open('pub_url.json', 'r') as f:
34
+ pub_url = ujson.load(f)
35
+ with open('pub_cu_author.json', 'r') as f:
36
+ pub_cu_author = ujson.load(f)
37
+ with open('pub_date.json', 'r') as f:
38
+ pub_date = ujson.load(f)
39
+
40
+
41
+ def search_data(input_text, operator_val, search_type):
42
+ output_data = {}
43
+ if operator_val == 2:
44
+ input_text = input_text.lower().split()
45
+ pointer = []
46
+ for token in input_text:
47
+ if len(input_text) < 2:
48
+ st.warning("Please enter at least 2 words to apply the operator.")
49
+ break
50
+ # if len(token) <= 3:
51
+ # st.warning("Please enter more than 4 characters.")
52
+ # break
53
+ stem_temp = ""
54
+ stem_word_file = []
55
+ temp_file = []
56
+ word_list = word_tokenize(token)
57
+
58
+ for x in word_list:
59
+ if x not in stop_words:
60
+ stem_temp += stemmer.stem(x) + " "
61
+ stem_word_file.append(stem_temp)
62
+
63
+ if search_type == "publication" and pub_index.get(stem_word_file[0].strip()):
64
+ pointer = pub_index.get(stem_word_file[0].strip())
65
+ elif search_type == "author" and author_index.get(stem_word_file[0].strip()):
66
+ pointer = author_index.get(stem_word_file[0].strip())
67
+
68
+ if len(pointer) == 0:
69
+ output_data = {}
70
+ else:
71
+ for j in pointer:
72
+ if search_type == "publication":
73
+ temp_file.append(pub_list_first_stem[j])
74
+ elif search_type == "author":
75
+ temp_file.append(author_list_first_stem[j])
76
+
77
+ temp_file = tfidf.fit_transform(temp_file)
78
+ cosine_output = cosine_similarity(temp_file, tfidf.transform(stem_word_file))
79
+
80
+ for j in pointer:
81
+ output_data[j] = cosine_output[pointer.index(j)]
82
+
83
+ else: # Relevant operator (OR)
84
+ input_text = input_text.lower().split()
85
+ pointer = []
86
+ match_word = []
87
+ for token in input_text:
88
+ if len(input_text) < 2:
89
+ st.warning("Please enter at least 2 words to apply the operator.")
90
+ break
91
+ # if len(token) <= 3:
92
+ # st.warning("Please enter more than 4 characters.")
93
+ # break
94
+ temp_file = []
95
+ set2 = set()
96
+ stem_word_file = []
97
+ word_list = word_tokenize(token)
98
+ stem_temp = ""
99
+ for x in word_list:
100
+ if x not in stop_words:
101
+ stem_temp += stemmer.stem(x) + " "
102
+ stem_word_file.append(stem_temp)
103
+
104
+ if search_type == "publication" and pub_index.get(stem_word_file[0].strip()):
105
+ set1 = set(pub_index.get(stem_word_file[0].strip()))
106
+ pointer.extend(list(set1))
107
+ elif search_type == "author" and author_index.get(stem_word_file[0].strip()):
108
+ set1 = set(author_index.get(stem_word_file[0].strip()))
109
+ pointer.extend(list(set1))
110
+
111
+ if match_word == []:
112
+ match_word = list({z for z in pointer if z in set2 or (set2.add(z) or False)})
113
+ else:
114
+ match_word.extend(list(set1))
115
+ match_word = list({z for z in match_word if z in set2 or (set2.add(z) or False)})
116
+
117
+ if len(input_text) > 1:
118
+ match_word = {z for z in match_word if z in set2 or (set2.add(z) or False)}
119
+
120
+ if len(match_word) == 0:
121
+ output_data = {}
122
+ else:
123
+ for j in list(match_word):
124
+ if search_type == "publication":
125
+ temp_file.append(pub_list_first_stem[j])
126
+ elif search_type == "author":
127
+ temp_file.append(author_list_first_stem[j])
128
+
129
+ temp_file = tfidf.fit_transform(temp_file)
130
+ cosine_output = cosine_similarity(temp_file, tfidf.transform(stem_word_file))
131
+
132
+ for j in list(match_word):
133
+ output_data[j] = cosine_output[list(match_word).index(j)]
134
+ else:
135
+ if len(pointer) == 0:
136
+ output_data = {}
137
+ else:
138
+ for j in pointer:
139
+ if search_type == "publication":
140
+ temp_file.append(pub_list_first_stem[j])
141
+ elif search_type == "author":
142
+ temp_file.append(author_list_first_stem[j])
143
+
144
+ temp_file = tfidf.fit_transform(temp_file)
145
+ cosine_output = cosine_similarity(temp_file, tfidf.transform(stem_word_file))
146
+
147
+ for j in pointer:
148
+ output_data[j] = cosine_output[pointer.index(j)]
149
+
150
+ return output_data
151
+
152
+
153
+ def app():
154
+
155
+ # Load the image and display it
156
+ image = Image.open('cire.png')
157
+ st.image(image)
158
+
159
+ # Add a text description
160
+ st.markdown("<p style='text-align: center;'> Uncover the brilliance: Explore profiles, groundbreaking work, and cutting-edge research by the exceptional minds of Coventry University.</p>", unsafe_allow_html=True)
161
+
162
+
163
+ input_text = st.text_input("Search research:", key="query_input")
164
+ operator_val = st.radio(
165
+ "Search Filters",
166
+ ['Exact', 'Relevant'],
167
+ index=1,
168
+ key="operator_input",
169
+ horizontal=True,
170
+ )
171
+ search_type = st.radio(
172
+ "Search in:",
173
+ ['Publications', 'Authors'],
174
+ index=0,
175
+ key="search_type_input",
176
+ horizontal=True,
177
+ )
178
+
179
+ if st.button("SEARCH"):
180
+ if search_type == "Publications":
181
+ output_data = search_data(input_text, 1 if operator_val == 'Exact' else 2, "publication")
182
+ elif search_type == "Authors":
183
+ output_data = search_data(input_text, 1 if operator_val == 'Exact' else 2, "author")
184
+ else:
185
+ output_data = {}
186
+
187
+ # Display the search results
188
+ show_results(output_data, search_type)
189
+
190
+ st.markdown("<p style='text-align: center;'> Brought to you with ❤ by <a href='https://github.com/maladeep'>Mala Deep</a> | Data © Coventry University </p>", unsafe_allow_html=True)
191
+
192
+
193
+ def show_results(output_data, search_type):
194
+ aa = 0
195
+ rank_sorting = sorted(output_data.items(), key=lambda z: z[1], reverse=True)
196
+
197
+ # Show the total number of research results
198
+ st.info(f"Showing results for: {len(rank_sorting)}")
199
+
200
+ # Show the cards
201
+ N_cards_per_row = 3
202
+ for n_row, (id_val, ranking) in enumerate(rank_sorting):
203
+ i = n_row % N_cards_per_row
204
+ if i == 0:
205
+ st.write("---")
206
+ cols = st.columns(N_cards_per_row, gap="large")
207
+ # Draw the card
208
+ with cols[n_row % N_cards_per_row]:
209
+ if search_type == "Publications":
210
+ st.caption(f"{pub_date[id_val].strip()}")
211
+ st.markdown(f"**{pub_cu_author[id_val].strip()}**")
212
+ st.markdown(f"*{pub_name[id_val].strip()}*")
213
+ st.markdown(f"**{pub_url[id_val]}**")
214
+ elif search_type == "Authors":
215
+ st.caption(f"{pub_date[id_val].strip()}")
216
+ st.markdown(f"**{author_name[id_val].strip()}**")
217
+ st.markdown(f"*{pub_name[id_val].strip()}*")
218
+ st.markdown(f"**{pub_url[id_val]}**")
219
+ st.markdown(f"Ranking: {ranking[0]:.2f}")
220
+
221
+ aa += 1
222
+
223
+ if aa == 0:
224
+ st.info("No results found. Please try again.")
225
+ else:
226
+ st.info(f"Results shown for: {aa}")
227
+
228
+
229
+ if __name__ == '__main__':
230
+ app()
231
+
author_indexed_dictionary.json ADDED
@@ -0,0 +1 @@
 
 
1
+ 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Coad","Jane Coad","Jane Coad","Jane Coad","Jane Coad","Jane Coad","Jane Coad","Jane Coad","Jane Coad","Jane Coad","Jane Coad","Jane Coad","Jane Coad","Jane Coad","Jane Coad","Jane Coad","Jane Coad","Jane Coad","Jane Coad","Jane Coad","Jane Coad","Jane Coad","Jane Coad","Jane Coad","Jane Coad","Jane Coad","Jane Coad","Jane Coad","Jane Coad","Jane Coad","Jane Coad","Jane Coad","Jane Coad","Jane Coad","Jane Coad","Jane Coad","Jane Coad","Jane Coad","Jane Coad","Jane Coad","Jane Coad","Jane Coad","Jane Coad","Eoin Coakley","Eoin Coakley","Eoin Coakley","Eoin Coakley","Eoin Coakley","Eoin Coakley","Eoin Coakley","Eoin Coakley","Eoin Coakley","Eoin Coakley","Eoin Coakley","Eoin Coakley","Andrew Cobley","Andrew Cobley","Andrew Cobley","Andrew Cobley","Andrew Cobley","Andrew Cobley","Andrew Cobley","Andrew Cobley","Andrew Cobley","Andrew Cobley","Andrew Cobley","Andrew Cobley","Andrew Cobley","Andrew Cobley","Andrew Cobley","Andrew Cobley","Andrew Cobley","Andrew Cobley","Andrew 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Crawley","Marie-Louise Crawley","Marie-Louise Crawley","Marie-Louise Crawley","Marie-Louise Crawley","Marie-Louise Crawley","Marie-Louise Crawley","Marie-Louise Crawley","Marie-Louise Crawley","Marie-Louise Crawley","Marie-Louise Crawley","Marie-Louise Crawley","Marie-Louise Crawley","Marie-Louise Crawley","Marie-Louise Crawley","Marie-Louise Crawley","Marie-Louise Crawley","Marie-Louise Crawley","Marie-Louise Crawley","Marie-Louise Crawley","Marie-Louise Crawley","Marie-Louise Crawley","Marie-Louise Crawley","Marie-Louise Crawley","Marie-Louise Crawley","Marie-Louise Crawley","Marie-Louise Crawley","Marie-Louise Crawley","Marie-Louise Crawley","Marie-Louise Crawley","Gill Cressey","Gill Cressey","Gill Cressey","Gill Cressey","Gill Cressey","Gill Cressey","Gill Cressey","Gill Cressey","Gill Cressey","Gill Cressey","Gill Cressey","Gill Cressey","Michael Cribb","Michael Cribb","Michael Cribb","Michael Cribb","Michael Cribb","Michael Cribb","Michael Cribb","Michael Cribb","Michael Cribb","Michael Cribb","Michael Cribb","Michael Cribb","Michael Cribb","Michael Cribb","Michael Cribb","Michael Cribb","Matteo Crotti","Matteo Crotti","Matteo Crotti","Matteo Crotti","Matteo Crotti","Matteo Crotti","Matteo Crotti","Matteo Crotti","Matteo Crotti","Matteo Crotti","Matteo Crotti","Matteo Crotti","Matteo Crotti","Matteo Crotti","Matteo Crotti","Matteo Crotti","Tom Cullen","Tom Cullen","Tom Cullen","Tom Cullen","Tom Cullen","Tom Cullen","Tom Cullen","Tom Cullen","Tom Cullen","Tom Cullen","Tom Cullen","Tom Cullen","Tom Cullen","Tom Cullen","Tom Cullen","Tom Cullen","Tom Cullen","Tom Cullen","Tom Cullen"]
authorindexer.py ADDED
@@ -0,0 +1,61 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import nltk
2
+ from nltk.corpus import stopwords
3
+ from nltk.tokenize import word_tokenize
4
+ from nltk.stem import PorterStemmer
5
+ import ujson
6
+
7
+ # Load the JSON file containing scraped results
8
+ with open('scraper_results.json', 'r') as doc:
9
+ scraper_results = doc.read()
10
+
11
+ # Extract author names from the JSON data
12
+ authors = []
13
+ data_dict = ujson.loads(scraper_results)
14
+ for item in data_dict:
15
+ authors.append(item["cu_author"])
16
+
17
+ # Write the author names to a JSON file
18
+ with open('author_names.json', 'w') as f:
19
+ ujson.dump(authors, f)
20
+
21
+ # Download necessary NLTK resources
22
+ nltk.download('stopwords')
23
+ nltk.download('punkt')
24
+
25
+ # Load the JSON file containing author names
26
+ with open('author_names.json', 'r') as f:
27
+ author_data = f.read()
28
+
29
+ # Load JSON data
30
+ authors = ujson.loads(author_data)
31
+
32
+ # Preprocess the author names
33
+ stop_words = stopwords.words('english')
34
+ stemmer = PorterStemmer()
35
+ authors_list_first_stem = []
36
+ authors_list = []
37
+
38
+ for author in authors:
39
+ words = word_tokenize(author)
40
+ stem_word = ""
41
+ for word in words:
42
+ if word.lower() not in stop_words:
43
+ stem_word += stemmer.stem(word) + " "
44
+ authors_list_first_stem.append(stem_word)
45
+ authors_list.append(author)
46
+
47
+ # Indexing process
48
+ data_dict = {}
49
+ for i in range(len(authors_list_first_stem)):
50
+ for word in authors_list_first_stem[i].split():
51
+ if word not in data_dict:
52
+ data_dict[word] = [i]
53
+ else:
54
+ data_dict[word].append(i)
55
+
56
+ # Write the preprocessed author names and indexed dictionary to JSON files
57
+ with open('author_list_stemmed.json', 'w') as f:
58
+ ujson.dump(authors_list_first_stem, f)
59
+
60
+ with open('author_indexed_dictionary.json', 'w') as f:
61
+ ujson.dump(data_dict, f)
cire.png ADDED
classifier.py ADDED
@@ -0,0 +1,125 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+
2
+ import numpy as np # Enables working with arrays and mathematical operations
3
+ import pandas as pd # Provides data structures and data analysis tools
4
+ import seaborn as sns; sns.set() # Enhances the visual appearance of plots and figures
5
+ from nltk.corpus import stopwords # Provides stopwords for natural language processing tasks
6
+ from nltk.stem import PorterStemmer # Implements the Porter stemming algorithm for word normalization
7
+ from nltk.tokenize import word_tokenize # Splits text into words or tokens
8
+ from sklearn.feature_extraction.text import TfidfVectorizer # Transforms text data into numerical features
9
+ from sklearn.metrics import confusion_matrix, f1_score, accuracy_score, classification_report # Evaluates model performance metrics
10
+ from sklearn.naive_bayes import MultinomialNB # Implements the Multinomial Naive Bayes classifier
11
+ from skmultilearn.problem_transform import ClassifierChain # Implements problem transformation techniques for multi-label classification
12
+ import matplotlib.pyplot as plt # Creates visualizations and plots
13
+ import pickle # Enables object serialization and deserialization
14
+ from sklearn.pipeline import Pipeline # Chains multiple steps into a single unit for machine learning workflows
15
+
16
+
17
+
18
+ train_data = pd.read_csv('Train.csv')
19
+ test_data = pd.read_csv('Test.csv')
20
+
21
+ abstract_list_train = []
22
+ abstract_list_test = []
23
+ stemmer = PorterStemmer()
24
+ stop_words = stopwords.words('english')
25
+
26
+ #Remove StopWords and Stemming
27
+ def remove_stopwords(data = []):
28
+ data_list = []
29
+ for name in data:
30
+ words = word_tokenize(name)
31
+ stem_word = ""
32
+ for a in words:
33
+ if a.lower() not in stop_words:
34
+ stem_word += stemmer.stem(a) + ' '
35
+ data_list.append(stem_word.lower())
36
+ return data_list
37
+
38
+ #Remove Special Characters
39
+ def remove_special_character(data = []):
40
+ abstract_list_wo_sc = []
41
+ special_characters = '''!()-—[]{};:'"\, <>./?@#$%^&*_~0123456789+=’‘'''
42
+ for file in data:
43
+ word_wo_sc = ""
44
+ if len(file.split()) == 1:
45
+ abstract_list_wo_sc.append(file)
46
+ else:
47
+ for a in file:
48
+ if a in special_characters:
49
+ word_wo_sc += ' '
50
+ else:
51
+ word_wo_sc += a
52
+ abstract_list_wo_sc.append(word_wo_sc)
53
+ return abstract_list_wo_sc
54
+
55
+ #Remove stopwords from Train Data
56
+ data_train = np.array(train_data['ABSTRACT'])
57
+ abstract_list_train = remove_stopwords(data_train)
58
+
59
+ #Remove stopwords from Test Data
60
+ data_test = np.array(test_data['ABSTRACT'])
61
+ abstract_list_test = remove_stopwords(data_test)
62
+
63
+ #Removing speaial characters from Train Data and Test Data
64
+ abstract_list_wo_sc_train = remove_special_character(abstract_list_train)
65
+ abstract_list_wo_sc_test = remove_special_character(abstract_list_test)
66
+
67
+ categories=['Engineering', 'Business', 'Art']
68
+
69
+ x_train = abstract_list_wo_sc_train
70
+ y_train = train_data[categories]
71
+ x_test = abstract_list_wo_sc_test
72
+ y_test = test_data[categories]
73
+
74
+ print("There are ", len(x_train), " input training samples")
75
+ print("There are ", len(x_test), " input testing samples")
76
+ print("There are ", y_train.shape, " output training samples")
77
+ print("There are ", y_test.shape, " output testing samples")
78
+
79
+
80
+
81
+
82
+
83
+ # defining parameters for pipeline
84
+ parameters = Pipeline([('tfidf', TfidfVectorizer(stop_words=stop_words)),('clf', ClassifierChain(MultinomialNB())),])
85
+
86
+ # train data
87
+ parameters.fit(x_train, y_train)
88
+
89
+
90
+ # predict
91
+ predictions = parameters.predict(x_test)
92
+
93
+
94
+
95
+ # Print accuracy score
96
+ accuracy = accuracy_score(y_test, predictions)
97
+ print('Accuracy:', accuracy)
98
+
99
+ # Print F1 score
100
+ f1 = f1_score(y_test, predictions, average='micro')
101
+ print('F1 score:', f1)
102
+
103
+ # Print classification report
104
+ report = classification_report(y_test, predictions)
105
+ print('Classification Report:')
106
+ print(report)
107
+
108
+
109
+
110
+
111
+ # Confusion Matrix and HeatMap Generation
112
+ mat = confusion_matrix(y_test.values.argmax(axis=1), predictions.argmax(axis=1))
113
+
114
+ plt.figure(figsize=(8, 6))
115
+ sns.heatmap(mat.T, square=True, annot=True, fmt='d', cbar=False, cmap='Blues')
116
+ plt.xlabel('True Label')
117
+ plt.ylabel('Predicted Label')
118
+ plt.title('Confusion Matrix')
119
+
120
+ plt.show()
121
+
122
+
123
+ # Save as picklefile
124
+ with open('model_MultiNB.pkl', 'wb') as picklefile:
125
+ pickle.dump(parameters.named_steps['clf'], picklefile)
crontab scrapper ADDED
@@ -0,0 +1,2 @@
 
 
 
1
+ 0 0 * * 0 python /Users/maladeep/Desktop/MSCDATA/Information_Retrival/Pure
2
+ Search/Scrapper.py
crontab.save ADDED
@@ -0,0 +1,2 @@
 
 
 
1
+ 0 0 * * 0 python /Users/maladeep/Desktop/MSCDATA/Information_Retrival/Pure
2
+ Search
model_MultiNB.pkl ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
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+ oid sha256:45b429be151d88c36f2b3f4bd92daa82700809bb7c44d73fd28981f960cd1c1a
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+ size 3557756
pub_cu_author.json ADDED
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pub_name.json ADDED
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pub_url.json ADDED
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publication_indexed_dictionary.json ADDED
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requirements.txt ADDED
@@ -0,0 +1,14 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ streamlit
2
+ Pillow
3
+ ujson
4
+ scikit-learn
5
+ nltk
6
+ numpy
7
+ pandas
8
+ seaborn
9
+ matplotlib
10
+ scikit-multilearn
11
+ requests
12
+ beautifulsoup4
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+ selenium
14
+ webdriver_manager
scraper_results.json ADDED
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