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from PyPDF2 import PdfReader
import streamlit as st
from embeddings import text_embedding
import scipy
def extract_and_rank(resumes, job_description):
out_embed_dict = {}
out_text_dict = {}
for resume in resumes:
reader = PdfReader(resume)
raw_text = "".join(page.extract_text() for page in reader.pages)
embedding = text_embedding(raw_text)
d1 = {resume.name: (embedding)}
d2 = {resume.name: raw_text}
out_embed_dict.update(d1)
out_text_dict.update(d2)
ranked_output = rankings(out_dict=out_embed_dict, query=job_description)
return ranked_output, out_embed_dict, out_text_dict
def get_sim(query_embedding, average_vec):
try:
sim = [(1 - scipy.spatial.distance.cosine(query_embedding, average_vec))]
return sim
except:
return [0]
def rankings(out_dict, query):
query_embedding = text_embedding(query)
rank = []
for k, v in out_dict.items():
rank.append((k, get_sim(query_embedding, v)))
rank = sorted(rank, key=lambda t: t[1], reverse=True)
return rank
# def data_clean(text):
# pattern = r'[^a-zA-Z0-9\s]'
# text = re.sub(pattern,'',' '.join(text))
# tokens = [token.strip() for token in text.split()]
# filtered = [token for token in tokens if token.lower() not in stopword_list]
# filtered = ' '.join(filtered)
# return filtered
# def embeddings(word):
# # print(word)
# if word in wv.key_to_index:
# return wv.get_vector(word)
# else:
# return np.zeros(300)
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