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from sentence_transformers import SentenceTransformer
from sklearn.metrics.pairwise import cosine_similarity
import pandas as pd
import gradio as gr
import pdfplumber
import texthero as hero
from texthero import preprocessing as ppe
import re
model = SentenceTransformer('sentence-transformers/paraphrase-xlm-r-multilingual-v1')
def remove_special_characters(text):
pattern = r'[^a-zA-Z]'
text = re.sub(pattern, ' ', text)
return text
#word file (Job Description)
def opentxt(filepath):
file_1 = open(filepath, errors="ignore")
file_2 = file_1.read()
file_2 = file_2.replace('\n', ' ')
file_2 = re.sub('www.\S+|www.\S+', '', file_2)
df_1 = pd.DataFrame([file_2], columns = ['text'])
df_1['text'] = df_1['text'].apply(remove_special_characters)
custom_pipeline = [ppe.fillna, ppe.remove_urls, ppe.remove_whitespace]
df_1['cleaned_text'] = hero.clean(df_1['text'], custom_pipeline)
file_2 = df_1['cleaned_text'].astype(str)
return file_2
#pdf file (Resume)
def pdftotext(filepath):
with pdfplumber.open(filepath) as pdf:
first_page = pdf.pages[0]
list_1 = first_page.extract_text(x_tolerance=3, y_tolerance=3)
list_1 = list_1.replace('\n', ' ')
list_1 = re.sub('www.\S+|www.\S+', '', list_1)
df = pd.DataFrame([list_1], columns = ['text'])
df['text'] = df['text'].apply(remove_special_characters)
custom_pipeline = [ppe.fillna, ppe.remove_urls, ppe.remove_whitespace]
df['cleaned_text'] = hero.clean(df['text'], custom_pipeline)
list_1 = df['cleaned_text'].astype(str)
return list_1
def sent_similarity(filepath_1, filepath_2):
txt_1 = pdftotext(filepath_1.name)
txt_2 = opentxt(filepath_2.name)
sentences = [''.join(txt_1), ''.join(txt_2)]
sentence_embeddings = model.encode(sentences)
similarity = cosine_similarity(sentence_embeddings[0].reshape(1, -1),sentence_embeddings[1].reshape(1, -1))[0][0]
return round(similarity*100, 2)
input_1 = gr.inputs.File(file_count="single", type="file", label= 'Upload the Resume (.pdf)', optional=False)
input_2 = gr.inputs.File(file_count="single", type="file", label= 'Upload the Job Description (.txt)', optional=False)
title = "Resume Screener"
description = "Upload your resume(.pdf) and the job description(.txt) and let the sentence similarity model display the similarity percentage !!!"
iface = gr.Interface(
sent_similarity,
[input_1, input_2], "label", title = title, description = description)
if __name__ == "__main__":
iface.launch()
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