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Indexer.py

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+
+import nltk #NLTK for natural language processing tasks
+from nltk.corpus import stopwords # list of stop word 
+from nltk.tokenize import word_tokenize # To tokenize each word
+from nltk.stem import PorterStemmer # For specific rules to transform words to their stems
+
+
+# Preprosessing data before indexing
+with open('scraper_results.json', 'r') as doc: scraper_results=doc.read()
+
+# Initialize empty lists to store publication name, URL, author, and date
+    
+pubName = []
+pubURL = []
+pubCUAuthor = []
+pubDate = []
+
+# Load the scraped results using ujson
+data_dict = ujson.loads(scraper_results)
+
+# Get the length of the data_dict (number of publications)
+array_length = len(data_dict)
+# Print the number of publications
+print(array_length)
+
+#Seperate name, url, date, author in different file
+for item in data_dict:
+    pubName.append(item["name"])
+    pubURL.append(item["pub_url"])
+    pubCUAuthor.append(item["cu_author"])
+    pubDate.append(item["date"])
+with open('pub_name.json', 'w') as f:ujson.dump(pubName, f)
+with open('pub_url.json', 'w') as f:ujson.dump(pubURL, f)
+with open('pub_cu_author.json', 'w') as f:ujson.dump(pubCUAuthor, f)
+with open('pub_date.json', 'w') as f: ujson.dump(pubDate, f)
+
+
+#Open a file with publication names in read mode
+with open('pub_name.json','r') as f:publication=f.read()
+
+#Load JSON File
+pubName = ujson.loads(publication)
+
+#Downloading libraries to use its methods
+nltk.download('stopwords')
+nltk.download('punkt')
+
+#Predefined stopwords in nltk are used
+stop_words = stopwords.words('english')
+stemmer = PorterStemmer()
+pub_list_first_stem = []
+pub_list = []
+pub_list_wo_sc = []
+print(len(pubName))
+
+for file in pubName:
+    #Splitting strings to tokens(words)
+    words = word_tokenize(file)
+    stem_word = ""
+    for i in words:
+        if i.lower() not in stop_words:
+            stem_word += stemmer.stem(i) + " "
+    pub_list_first_stem.append(stem_word)
+    pub_list.append(file)
+
+#Removing all below characters
+special_characters = '''!()-—[]{};:'"\, <>./?@#$%^&*_~0123456789+=’‘'''
+for file in pub_list:
+    word_wo_sc = ""
+    if len(file.split()) ==1 : pub_list_wo_sc.append(file)
+    else:
+        for a in file:
+            if a in special_characters:
+                word_wo_sc += ' '
+            else:
+                word_wo_sc += a
+        #print(word_wo_sc)
+        pub_list_wo_sc.append(word_wo_sc)
+
+#Stemming Process
+pub_list_stem_wo_sw = []
+for name in pub_list_wo_sc:
+    words = word_tokenize(name)
+    stem_word = ""
+    for a in words:
+        if a.lower() not in stop_words:
+            stem_word += stemmer.stem(a) + ' '
+    pub_list_stem_wo_sw.append(stem_word.lower())
+
+data_dict = {} #Inverted Index holder
+
+# Indexing process
+for a in range(len(pub_list_stem_wo_sw)):
+    for b in pub_list_stem_wo_sw[a].split():
+        if b not in data_dict:
+             data_dict[b] = [a]
+        else:
+            data_dict[b].append(a)
+
+# printing the lenght
+print(len(pub_list_wo_sc))
+print(len(pub_list_stem_wo_sw))
+print(len(pub_list_first_stem))
+print(len(pub_list))
+
+# with open('publication_list.json', 'w') as f:
+#     ujson.dump(pub_list, f)
+
+with open('publication_list_stemmed.json', 'w') as f:
+    ujson.dump(pub_list_first_stem, f)
+
+with open('publication_indexed_dictionary.json', 'w') as f:
+    ujson.dump(data_dict, f)

LICENSE

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+MIT License
+
+Copyright (c) 2023 MalaDeep 
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

Scrapper.py

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+#import os  # Module for interacting with the operating system
+import time  # Module for time-related operations
+import ujson  # Module for working with JSON data
+from random import randint  # Module for generating random numbers
+from typing import Dict, List, Any  # Type hinting imports
+
+import requests  # Library for making HTTP requests
+from bs4 import BeautifulSoup  # Library for parsing HTML data
+from selenium import webdriver  # Library for browser automation
+from selenium.common.exceptions import NoSuchElementException  # Exception for missing elements
+from webdriver_manager.chrome import ChromeDriverManager  # Driver manager for Chrome (We are using Chromium based )
+
+
+
+# Delete files if present
+# try:
+#     os.remove('Authors_URL.txt')
+#     os.remove('scraper_results.json')
+# except OSError:
+#     pass
+
+def write_authors(list1, file_name):
+     # Function to write authors' URLs to a file
+    with open(file_name, 'w', encoding='utf-8') as f:
+        for i in range(0, len(list1)):
+            f.write(list1[i] + '\n')
+
+
+def initCrawlerScraper(seed,max_profiles=500):
+    # Initialize driver for Chrome
+    webOpt = webdriver.ChromeOptions()
+    webOpt.add_experimental_option('excludeSwitches', ['enable-logging'])
+    webOpt.add_argument('--ignore-certificate-errors')
+    webOpt.add_argument('--incognito')
+    webOpt.headless = True
+    driver = webdriver.Chrome(ChromeDriverManager().install(), options=webOpt)
+    driver.get(seed)  # Start with the original link
+
+    Links = []  # Array with pureportal profiles URL
+    pub_data = []  # To store publication information for each pureportal profile
+
+    nextLink = driver.find_element_by_css_selector(".nextLink").is_enabled()  # Check if the next page link is enabled
+    print("Crawler has begun...")
+    while (nextLink):
+        page = driver.page_source
+        # XML parser to parse each URL
+        bs = BeautifulSoup(page, "lxml")  # Parse the page source using BeautifulSoup
+
+        # Extracting exact URL by spliting string into list
+        for link in bs.findAll('a', class_='link person'):
+            url = str(link)[str(link).find('https://pureportal.coventry.ac.uk/en/persons/'):].split('"')
+            Links.append(url[0])
+            
+        # Click on Next button to visit next page
+        try:
+            if driver.find_element_by_css_selector(".nextLink"):
+                element = driver.find_element_by_css_selector(".nextLink")
+                driver.execute_script("arguments[0].click();", element)
+            else:
+                nextLink = False
+        except NoSuchElementException:
+            break
+            
+        # Check if the maximum number of profiles is reached
+        if len(Links) >= max_profiles:
+            break
+            
+    print("Crawler has found ", len(Links), " pureportal profiles")
+    write_authors(Links, 'Authors_URL.txt') # Write the authors' URLs to a file
+
+    print("Scraping publication data for ", len(Links), " pureportal profiles...")
+    count = 0
+    for link in Links:
+        # Visit each link to get data
+        time.sleep(1)  # Delay of 1 second to hit next data
+        driver.get(link)
+        try:
+            if driver.find_elements_by_css_selector(".portal_link.btn-primary.btn-large"):
+                element = driver.find_elements_by_css_selector(".portal_link.btn-primary.btn-large")
+                for a in element:
+                    if "research output".lower() in a.text.lower():
+                        driver.execute_script("arguments[0].click();", a)
+                        driver.get(driver.current_url)
+                        # Get name of Author
+                        name = driver.find_element_by_css_selector("div[class='header person-details']>h1")
+                        r = requests.get(driver.current_url)
+                        # Parse all the data via BeautifulSoup
+                        soup = BeautifulSoup(r.content, 'lxml')
+
+                        # Extracting publication name, publication url, date and CU Authors
+                        table = soup.find('ul', attrs={'class': 'list-results'})
+                        if table != None:
+                            for row in table.findAll('div', attrs={'class': 'result-container'}):
+                                data = {}
+                                data['name'] = row.h3.a.text
+                                data['pub_url'] = row.h3.a['href']
+                                date = row.find("span", class_="date")
+
+                                rowitem = row.find_all(['div'])
+                                span = row.find_all(['span'])
+                                data['cu_author'] = name.text
+                                data['date'] = date.text
+                                print("Publication Name :", row.h3.a.text)
+                                print("Publication URL :", row.h3.a['href'])
+                                print("CU Author :", name.text)
+                                print("Date :", date.text)
+                                print("\n")
+                                pub_data.append(data)
+            else:
+                # Get name of Author
+                name = driver.find_element_by_css_selector("div[class='header person-details']>h1")
+                r = requests.get(link)
+                # Parse all the data via BeautifulSoup
+                soup = BeautifulSoup(r.content, 'lxml')
+                # Extracting publication name, publication url, date and CU Authors
+                table = soup.find('div', attrs={'class': 'relation-list relation-list-publications'})
+                if table != None:
+                    for row in table.findAll('div', attrs={'class': 'result-container'}):
+                        data = {}
+                        data["name"] = row.h3.a.text
+                        data['pub_url'] = row.h3.a['href']
+                        date = row.find("span", class_="date")
+                        rowitem = row.find_all(['div'])
+                        span = row.find_all(['span'])
+                        data['cu_author'] = name.text
+                        data['date'] = date.text
+                        print("Publication Name :", row.h3.a.text)
+                        print("Publication URL :", row.h3.a['href'])
+                        print("CU Author :", name.text)
+                        print("Date :", date.text)
+                        print("\n")
+                        pub_data.append(data)
+        except Exception:
+            continue
+
+    print("Crawler has scrapped data for ", len(pub_data), " pureportal publications")
+    driver.quit()
+    # Writing all the scraped results in a file with JSON format
+    with open('scraper_results.json', 'w') as f:
+        ujson.dump(pub_data, f)
+
+
+initCrawlerScraper('https://pureportal.coventry.ac.uk/en/organisations/coventry-university/persons/', max_profiles=500)
+
+
+

app.py

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+import streamlit as st
+from PIL import Image
+import ujson
+from sklearn.feature_extraction.text import TfidfVectorizer
+from sklearn.metrics.pairwise import cosine_similarity
+from nltk.tokenize import word_tokenize
+from nltk.corpus import stopwords
+from nltk.stem import PorterStemmer
+
+import nltk
+nltk.download('stopwords')
+nltk.download('punkt')
+
+
+# Set up the NLTK components
+stemmer = PorterStemmer()
+stop_words = stopwords.words('english')
+tfidf = TfidfVectorizer()
+
+# Load the data
+with open('publication_list_stemmed.json', 'r') as f:
+    pub_list_first_stem = ujson.load(f)
+with open('publication_indexed_dictionary.json', 'r') as f:
+    pub_index = ujson.load(f)
+with open('author_list_stemmed.json', 'r') as f:
+    author_list_first_stem = ujson.load(f)
+with open('author_indexed_dictionary.json', 'r') as f:
+    author_index = ujson.load(f)
+with open('author_names.json', 'r') as f:
+    author_name = ujson.load(f)
+with open('pub_name.json', 'r') as f:
+    pub_name = ujson.load(f)
+with open('pub_url.json', 'r') as f:
+    pub_url = ujson.load(f)
+with open('pub_cu_author.json', 'r') as f:
+    pub_cu_author = ujson.load(f)
+with open('pub_date.json', 'r') as f:
+    pub_date = ujson.load(f)
+
+
+def search_data(input_text, operator_val, search_type):
+    output_data = {}
+    if operator_val == 2:
+        input_text = input_text.lower().split()
+        pointer = []
+        for token in input_text:
+            if len(input_text) < 2:
+                st.warning("Please enter at least 2 words to apply the operator.")
+                break
+            # if len(token) <= 3:
+            #     st.warning("Please enter more than 4 characters.")
+            #     break
+            stem_temp = ""
+            stem_word_file = []
+            temp_file = []
+            word_list = word_tokenize(token)
+
+            for x in word_list:
+                if x not in stop_words:
+                    stem_temp += stemmer.stem(x) + " "
+            stem_word_file.append(stem_temp)
+
+            if search_type == "publication" and pub_index.get(stem_word_file[0].strip()):
+                pointer = pub_index.get(stem_word_file[0].strip())
+            elif search_type == "author" and author_index.get(stem_word_file[0].strip()):
+                pointer = author_index.get(stem_word_file[0].strip())
+
+            if len(pointer) == 0:
+                output_data = {}
+            else:
+                for j in pointer:
+                    if search_type == "publication":
+                        temp_file.append(pub_list_first_stem[j])
+                    elif search_type == "author":
+                        temp_file.append(author_list_first_stem[j])
+
+                temp_file = tfidf.fit_transform(temp_file)
+                cosine_output = cosine_similarity(temp_file, tfidf.transform(stem_word_file))
+
+                for j in pointer:
+                    output_data[j] = cosine_output[pointer.index(j)]
+
+    else:  # Relevant operator (OR)
+        input_text = input_text.lower().split()
+        pointer = []
+        match_word = []
+        for token in input_text:
+            if len(input_text) < 2:
+                st.warning("Please enter at least 2 words to apply the operator.")
+                break
+            # if len(token) <= 3:
+            #     st.warning("Please enter more than 4 characters.")
+            #     break
+            temp_file = []
+            set2 = set()
+            stem_word_file = []
+            word_list = word_tokenize(token)
+            stem_temp = ""
+            for x in word_list:
+                if x not in stop_words:
+                    stem_temp += stemmer.stem(x) + " "
+            stem_word_file.append(stem_temp)
+
+            if search_type == "publication" and pub_index.get(stem_word_file[0].strip()):
+                set1 = set(pub_index.get(stem_word_file[0].strip()))
+                pointer.extend(list(set1))
+            elif search_type == "author" and author_index.get(stem_word_file[0].strip()):
+                set1 = set(author_index.get(stem_word_file[0].strip()))
+                pointer.extend(list(set1))
+
+            if match_word == []:
+                match_word = list({z for z in pointer if z in set2 or (set2.add(z) or False)})
+            else:
+                match_word.extend(list(set1))
+                match_word = list({z for z in match_word if z in set2 or (set2.add(z) or False)})
+
+        if len(input_text) > 1:
+            match_word = {z for z in match_word if z in set2 or (set2.add(z) or False)}
+
+            if len(match_word) == 0:
+                output_data = {}
+            else:
+                for j in list(match_word):
+                    if search_type == "publication":
+                        temp_file.append(pub_list_first_stem[j])
+                    elif search_type == "author":
+                        temp_file.append(author_list_first_stem[j])
+
+                temp_file = tfidf.fit_transform(temp_file)
+                cosine_output = cosine_similarity(temp_file, tfidf.transform(stem_word_file))
+
+                for j in list(match_word):
+                    output_data[j] = cosine_output[list(match_word).index(j)]
+        else:
+            if len(pointer) == 0:
+                output_data = {}
+            else:
+                for j in pointer:
+                    if search_type == "publication":
+                        temp_file.append(pub_list_first_stem[j])
+                    elif search_type == "author":
+                        temp_file.append(author_list_first_stem[j])
+
+                temp_file = tfidf.fit_transform(temp_file)
+                cosine_output = cosine_similarity(temp_file, tfidf.transform(stem_word_file))
+
+                for j in pointer:
+                    output_data[j] = cosine_output[pointer.index(j)]
+
+    return output_data
+
+
+def app():
+
+        # Load the image and display it
+    image = Image.open('cire.png')
+    st.image(image)
+
+    # Add a text description
+    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)
+
+
+    input_text = st.text_input("Search research:", key="query_input")
+    operator_val = st.radio(
+        "Search Filters",
+        ['Exact', 'Relevant'],
+        index=1,
+        key="operator_input",
+        horizontal=True,
+    )
+    search_type = st.radio(
+        "Search in:",
+        ['Publications', 'Authors'],
+        index=0,
+        key="search_type_input",
+        horizontal=True,
+    )
+
+    if st.button("SEARCH"):
+        if search_type == "Publications":
+            output_data = search_data(input_text, 1 if operator_val == 'Exact' else 2, "publication")
+        elif search_type == "Authors":
+            output_data = search_data(input_text, 1 if operator_val == 'Exact' else 2, "author")
+        else:
+            output_data = {}
+
+        # Display the search results
+        show_results(output_data, search_type)
+
+    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)
+
+
+def show_results(output_data, search_type):
+    aa = 0
+    rank_sorting = sorted(output_data.items(), key=lambda z: z[1], reverse=True)
+
+    # Show the total number of research results
+    st.info(f"Showing results for: {len(rank_sorting)}")
+
+    # Show the cards
+    N_cards_per_row = 3
+    for n_row, (id_val, ranking) in enumerate(rank_sorting):
+        i = n_row % N_cards_per_row
+        if i == 0:
+            st.write("---")
+            cols = st.columns(N_cards_per_row, gap="large")
+        # Draw the card
+        with cols[n_row % N_cards_per_row]:
+            if search_type == "Publications":
+                st.caption(f"{pub_date[id_val].strip()}")
+                st.markdown(f"**{pub_cu_author[id_val].strip()}**")
+                st.markdown(f"*{pub_name[id_val].strip()}*")
+                st.markdown(f"**{pub_url[id_val]}**")
+            elif search_type == "Authors":
+                st.caption(f"{pub_date[id_val].strip()}")
+                st.markdown(f"**{author_name[id_val].strip()}**")
+                st.markdown(f"*{pub_name[id_val].strip()}*")
+                st.markdown(f"**{pub_url[id_val]}**")
+                st.markdown(f"Ranking: {ranking[0]:.2f}")
+
+        aa += 1
+
+    if aa == 0:
+        st.info("No results found. Please try again.")
+    else:
+        st.info(f"Results shown for: {aa}")
+
+
+if __name__ == '__main__':
+    app()
+

author_indexed_dictionary.json

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authorindexer.py

@@ -0,0 +1,61 @@
+import nltk
+from nltk.corpus import stopwords
+from nltk.tokenize import word_tokenize
+from nltk.stem import PorterStemmer
+import ujson
+
+# Load the JSON file containing scraped results
+with open('scraper_results.json', 'r') as doc:
+    scraper_results = doc.read()
+
+# Extract author names from the JSON data
+authors = []
+data_dict = ujson.loads(scraper_results)
+for item in data_dict:
+    authors.append(item["cu_author"])
+
+# Write the author names to a JSON file
+with open('author_names.json', 'w') as f:
+    ujson.dump(authors, f)
+
+# Download necessary NLTK resources
+nltk.download('stopwords')
+nltk.download('punkt')
+
+# Load the JSON file containing author names
+with open('author_names.json', 'r') as f:
+    author_data = f.read()
+
+# Load JSON data
+authors = ujson.loads(author_data)
+
+# Preprocess the author names
+stop_words = stopwords.words('english')
+stemmer = PorterStemmer()
+authors_list_first_stem = []
+authors_list = []
+
+for author in authors:
+    words = word_tokenize(author)
+    stem_word = ""
+    for word in words:
+        if word.lower() not in stop_words:
+            stem_word += stemmer.stem(word) + " "
+    authors_list_first_stem.append(stem_word)
+    authors_list.append(author)
+
+# Indexing process
+data_dict = {}
+for i in range(len(authors_list_first_stem)):
+    for word in authors_list_first_stem[i].split():
+        if word not in data_dict:
+            data_dict[word] = [i]
+        else:
+            data_dict[word].append(i)
+
+# Write the preprocessed author names and indexed dictionary to JSON files
+with open('author_list_stemmed.json', 'w') as f:
+    ujson.dump(authors_list_first_stem, f)
+
+with open('author_indexed_dictionary.json', 'w') as f:
+    ujson.dump(data_dict, f)

classifier.py

@@ -0,0 +1,125 @@
+
+import numpy as np  # Enables working with arrays and mathematical operations
+import pandas as pd  # Provides data structures and data analysis tools
+import seaborn as sns; sns.set()  # Enhances the visual appearance of plots and figures
+from nltk.corpus import stopwords  # Provides stopwords for natural language processing tasks
+from nltk.stem import PorterStemmer  # Implements the Porter stemming algorithm for word normalization
+from nltk.tokenize import word_tokenize  # Splits text into words or tokens
+from sklearn.feature_extraction.text import TfidfVectorizer  # Transforms text data into numerical features
+from sklearn.metrics import confusion_matrix, f1_score, accuracy_score, classification_report  # Evaluates model performance metrics
+from sklearn.naive_bayes import MultinomialNB  # Implements the Multinomial Naive Bayes classifier
+from skmultilearn.problem_transform import ClassifierChain  # Implements problem transformation techniques for multi-label classification
+import matplotlib.pyplot as plt  # Creates visualizations and plots
+import pickle  # Enables object serialization and deserialization
+from sklearn.pipeline import Pipeline  # Chains multiple steps into a single unit for machine learning workflows
+
+
+
+train_data = pd.read_csv('Train.csv')
+test_data = pd.read_csv('Test.csv')
+
+abstract_list_train = []
+abstract_list_test = []
+stemmer = PorterStemmer()
+stop_words = stopwords.words('english')
+
+#Remove StopWords and Stemming
+def remove_stopwords(data = []):
+    data_list = []
+    for name in data:
+        words = word_tokenize(name)
+        stem_word = ""
+        for a in words:
+            if a.lower() not in stop_words:
+                stem_word += stemmer.stem(a) + ' '
+        data_list.append(stem_word.lower())
+    return data_list
+
+#Remove Special Characters
+def remove_special_character(data = []):
+    abstract_list_wo_sc = []
+    special_characters = '''!()-—[]{};:'"\, <>./?@#$%^&*_~0123456789+=’‘'''
+    for file in data:
+        word_wo_sc = ""
+        if len(file.split()) == 1:
+            abstract_list_wo_sc.append(file)
+        else:
+            for a in file:
+                if a in special_characters:
+                    word_wo_sc += ' '
+                else:
+                    word_wo_sc += a
+            abstract_list_wo_sc.append(word_wo_sc)
+    return abstract_list_wo_sc
+
+#Remove stopwords from Train Data
+data_train = np.array(train_data['ABSTRACT'])
+abstract_list_train = remove_stopwords(data_train)
+
+#Remove stopwords from Test Data
+data_test = np.array(test_data['ABSTRACT'])
+abstract_list_test = remove_stopwords(data_test)
+
+#Removing speaial characters from Train Data and Test Data
+abstract_list_wo_sc_train = remove_special_character(abstract_list_train)
+abstract_list_wo_sc_test = remove_special_character(abstract_list_test)
+
+categories=['Engineering', 'Business', 'Art']
+
+x_train = abstract_list_wo_sc_train
+y_train = train_data[categories]
+x_test = abstract_list_wo_sc_test
+y_test = test_data[categories]
+
+print("There are ", len(x_train), " input training samples")
+print("There are ", len(x_test), " input testing samples")
+print("There are ", y_train.shape, " output training samples")
+print("There are ", y_test.shape, " output testing samples")
+
+
+
+
+
+# defining parameters for pipeline
+parameters = Pipeline([('tfidf', TfidfVectorizer(stop_words=stop_words)),('clf', ClassifierChain(MultinomialNB())),])
+
+# train data
+parameters.fit(x_train, y_train)
+
+
+# predict
+predictions = parameters.predict(x_test)
+
+
+
+# Print accuracy score
+accuracy = accuracy_score(y_test, predictions)
+print('Accuracy:', accuracy)
+
+# Print F1 score
+f1 = f1_score(y_test, predictions, average='micro')
+print('F1 score:', f1)
+
+# Print classification report
+report = classification_report(y_test, predictions)
+print('Classification Report:')
+print(report)
+
+
+
+
+# Confusion Matrix and HeatMap Generation
+mat = confusion_matrix(y_test.values.argmax(axis=1), predictions.argmax(axis=1))
+
+plt.figure(figsize=(8, 6))
+sns.heatmap(mat.T, square=True, annot=True, fmt='d', cbar=False, cmap='Blues')
+plt.xlabel('True Label')
+plt.ylabel('Predicted Label')
+plt.title('Confusion Matrix')
+
+plt.show()
+
+
+# Save as picklefile
+with open('model_MultiNB.pkl', 'wb') as picklefile:
+    pickle.dump(parameters.named_steps['clf'], picklefile)

crontab scrapper

@@ -0,0 +1,2 @@
+0 0 * * 0 python /Users/maladeep/Desktop/MSCDATA/Information_Retrival/Pure 
+Search/Scrapper.py

crontab.save

@@ -0,0 +1,2 @@
+0 0 * * 0 python /Users/maladeep/Desktop/MSCDATA/Information_Retrival/Pure 
+Search

model_MultiNB.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:45b429be151d88c36f2b3f4bd92daa82700809bb7c44d73fd28981f960cd1c1a
+size 3557756

pub_cu_author.json

@@ -0,0 +1 @@
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requirements.txt

@@ -0,0 +1,14 @@
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+Pillow
+ujson
+scikit-learn
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+pandas
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