Updates

Similar.py

@@ -1,5 +1,4 @@
 import textdistance as td
-import Cleaner
 
 
 def match(resume, job_des):
@@ -8,6 +7,5 @@ def match(resume, job_des):
     c = td.cosine.similarity(resume, job_des)
     o = td.overlap.normalized_similarity(resume, job_des)
     total = (j+s+c+o)/4
+    # total = (s+o)/2
     return total*100
-
-

app.py

@@ -105,7 +105,7 @@ def calculate_scores(resumes, job_description):
     scores = []
     for x in range(resumes.shape[0]):
         score = Similar.match(
-            resumes['TF_Based'][x], job_description['Selective_Reduced'][index])
+            resumes['TF_Based'][x], job_description['TF_Based'][index])
         scores.append(score)
     return scores
 
@@ -160,7 +160,7 @@ document = get_list_of_words(Resumes['Cleaned'])
 id2word = corpora.Dictionary(document)
 corpus = [id2word.doc2bow(text) for text in document]
 
-lda_model = gensim.models.ldamodel.LdaModel(corpus=corpus, id2word=id2word, num_topics=5, random_state=100,
+lda_model = gensim.models.ldamodel.LdaModel(corpus=corpus, id2word=id2word, num_topics=6, random_state=100,
                                             update_every=1, chunksize=100, passes=50, alpha='auto', per_word_topics=True)
 
 ################################### LDA CODE ##############################################
@@ -203,7 +203,7 @@ cloud = WordCloud(background_color='white',
 
 topics = lda_model.show_topics(formatted=False)
 
-fig, axes = plt.subplots(2, 2, figsize=(10, 10), sharex=True, sharey=True)
+fig, axes = plt.subplots(2, 3, figsize=(10, 10), sharex=True, sharey=True)
 
 for i, ax in enumerate(axes.flatten()):
     fig.add_subplot(ax)
@@ -243,7 +243,7 @@ st.write(fig3)
 ############################## RESUME PRINTING #############################
 
 option_2 = st.selectbox("Show the Best Matching Resumes?", options=[
-    'NO', 'YES'])
+    'YES', 'NO'])
 if option_2 == 'YES':
     indx = st.slider("Which resume to display ?:",
                      1, Ranked_resumes.shape[0], 1)

fileReader.py

@@ -1,7 +1,6 @@
 from operator import index
 from pandas._config.config import options
 import Cleaner
-import Similar
 import textract as tx
 import pandas as pd
 import os

tf_idf.py

@@ -2,7 +2,7 @@ from sklearn.feature_extraction.text import TfidfVectorizer
 
 
 def do_tfidf(token):
-    tfidf = TfidfVectorizer(max_df=0.07, min_df=0.001)
+    tfidf = TfidfVectorizer(max_df=0.05, min_df=0.002)
     words = tfidf.fit_transform(token)
     sentence = " ".join(tfidf.get_feature_names())
     return sentence