CV_parsing_phaseIII.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package stopwords to\n",
      "[nltk_data]     C:\\Users\\user\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package stopwords is already up-to-date!\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import spacy\n",
    "from spacy.matcher import Matcher\n",
    "import os\n",
    "import docx\n",
    "import numpy as np \n",
    "import pandas as pd \n",
    "\n",
    "\n",
    "import sys\n",
    "import nltk\n",
    "\n",
    "nlp = spacy.load('en_core_web_sm')\n",
    "\n",
    "from nanonets import NANONETSOCR\n",
    "\n",
    "nltk.download(\"stopwords\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#extract_text_from_pdf: This function takes a PDF or  file path as input and extracts text from all the pages in the PDF using NANONETSOCR\n",
    "\n",
    "def extract_text_from_pdf_or_image(pdf_path):\n",
    "    model = NANONETSOCR()\n",
    "    model.set_token('87dcd5e7-53e9-11ee-bb5e-c26e11187d6d')\n",
    "    text = model.convert_to_string(pdf_path,formatting='lines') \n",
    "    return text\n",
    "\n",
    "\n",
    "#extract_text_from_docx: This function takes a DOCX file path as input and extracts text from the document using the docx library.\n",
    "def extract_text_from_docx(docx_path):\n",
    "    doc = docx.Document(docx_path)\n",
    "    text = '\\n'.join([para.text for para in doc.paragraphs])\n",
    "    return text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Use spaCy to parse the resume and perform NER to identify named entities.\n",
    "def parse_resume(resume_text):\n",
    "    # Parse the resume text using spaCy\n",
    "    doc = nlp(resume_text)\n",
    "    \n",
    "    # Extract named entities\n",
    "    named_entities = [(ent.text, ent.label_) for ent in doc.ents]\n",
    "    \n",
    "    return named_entities\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "#categorize the entities into different types (e.g., names, organizations, locations, dates), you can filter the named entities based on their entity types\n",
    "def categorize_entities(named_entities):\n",
    "    entity_categories = {\n",
    "        'person': [],\n",
    "        'organization': [],\n",
    "        'location': [],\n",
    "        'date': [],\n",
    "        'other': []\n",
    "    }\n",
    "    \n",
    "    for entity, entity_type in named_entities:\n",
    "        if entity_type == 'PERSON':\n",
    "            entity_categories['person'].append(entity)\n",
    "        elif entity_type == 'ORG':\n",
    "            entity_categories['organization'].append(entity)\n",
    "        elif entity_type == 'LOC':\n",
    "            entity_categories['location'].append(entity)\n",
    "        elif entity_type == 'DATE':\n",
    "            entity_categories['date'].append(entity)\n",
    "        else:\n",
    "            entity_categories['other'].append(entity)\n",
    "    \n",
    "    return entity_categories\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unsupported File Format: 25.jfif\n",
      "Unsupported File Format: 26.jfif\n",
      "Unsupported File Format: 28.jpeg\n",
      "Unsupported File Format: 29.jfif\n",
      "Unsupported File Format: prog2.ipynb\n"
     ]
    }
   ],
   "source": [
    "if __name__ == '__main__':\n",
    "    # Directory containing candidate resumes (PDF, DOCX, and images)\n",
    "    directory = 'C:/Users/user/Documents/GOMYCODE/ResumeParsing/dataset'\n",
    "    Allcat_entities=[]\n",
    "    #resume_paths = [r\"C:/Users/user/Documents/GOMYCODE/ResumeParsing/dataset/3.pdf\"]\n",
    "    #file_path =os.listdir(directory)\n",
    "    #for filename in file_path:\n",
    "    for filename in os.listdir(directory):\n",
    "        file_path = os.path.join(directory, filename)        \n",
    "        if os.path.isfile(file_path) and (file_path.lower().endswith('.pdf') or file_path.lower().endswith('.png') or file_path.lower().endswith('.jpg'))  :\n",
    "            #resume_text = extract_text_from_pdf(file_path)\n",
    "            resume_text=extract_text_from_pdf_or_image(file_path)\n",
    "            # Parse the resume and extract named entities\n",
    "            named_entities = parse_resume(resume_text)\n",
    "            # Categorize the named entities into types\n",
    "            categorized_entities = categorize_entities(named_entities)\n",
    "            Allcat_entities.append(categorized_entities)\n",
    "        elif os.path.isfile(file_path) and file_path.lower().endswith('.docx'):\n",
    "            resume_text = extract_text_from_docx(file_path)\n",
    "            # Parse the resume and extract named entities\n",
    "            named_entities = parse_resume(resume_text)\n",
    "            # Categorize the named entities into types\n",
    "            categorized_entities = categorize_entities(named_entities)\n",
    "            Allcat_entities.append(categorized_entities)  \n",
    "        else:\n",
    "            print('Unsupported File Format:', filename)\n",
    "        continue\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "   'Githul',\n",
       "   'AirAsia Digital',\n",
       "   'Scikit Pandas Numpy',\n",
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       "   'RSS',\n",
       "   'google yahoo finance & bloomberg\\nProcessi',\n",
       "   'Acourses Data Research Executive',\n",
       "   'Cassucalion and Vector Spaces MarketsandMarkets',\n",
       "   'News Type Classification Model',\n",
       "   'Keyword Extraction Model For Domain\\n& Industry'],\n",
       "  'location': [],\n",
       "  'date': ['863465923 3 years',\n",
       "   'June 2021',\n",
       "   '2018 2020 1',\n",
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       "   'Jan 2020 | June 2021'],\n",
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       "   'Tensorflow\\nLanguages Databases Tools & ML Libraries',\n",
       "   'Jupyter Engineering NLP',\n",
       "   'Tableau',\n",
       "   'Udemy',\n",
       "   'Verified Proiect',\n",
       "   'LGBM Classifier',\n",
       "   'Random Forest Classifier',\n",
       "   'Keras Skopt',\n",
       "   'Lasso Ridge'],\n",
       "  'organization': ['AlmaBetter',\n",
       "   'Pvthon SQL PostgreSQL',\n",
       "   'Pandas',\n",
       "   'Seaborn NLTK SHAP',\n",
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       "   'AWS Google Colab',\n",
       "   'Notebook Tableau &',\n",
       "   'Admin Panel & DB Management Zixokart',\n",
       "   'PUBLICATIONS',\n",
       "   'SVD',\n",
       "   'Webapp',\n",
       "   'Created',\n",
       "   'Hackerrank',\n",
       "   '02/2023\\nHealth Insurance Cross Sell Prediction ( Classification I AlmaBetter Badges',\n",
       "   'Vehicle',\n",
       "   'Data Science',\n",
       "   'Applied Scaling Technique',\n",
       "   'Performed',\n",
       "   'Present\\nMachine Learning & Deep Learning',\n",
       "   'SVM',\n",
       "   'KNN Naive',\n",
       "   'CCS',\n",
       "   'LGBM University',\n",
       "   'Appliance Energy Prediction ( Regression AlmaBetter',\n",
       "   'Ridge Random Forest Regression',\n",
       "   'Linear Regression',\n",
       "   'Random Forest Regression',\n",
       "   'Obtained SHAP'],\n",
       "  'location': ['PROJECTS Medium Blog\\nBook Recommendation System AlmaBetter Data Science for Healthcare O 01/2023'],\n",
       "  'date': ['12/2019', '2023', 'the past year'],\n",
       "  'other': ['8443405050',\n",
       "   'github.com/Fulkar-khan',\n",
       "   'Numpy',\n",
       "   'AlmaBetter',\n",
       "   '03/2023',\n",
       "   'Collaborative and Content',\n",
       "   'Vectorizer Cold',\n",
       "   '2 10/2022',\n",
       "   'Heroku',\n",
       "   'Pycharm',\n",
       "   'Flask',\n",
       "   '23%',\n",
       "   '30%',\n",
       "   '03/2023',\n",
       "   'Certified in Top : Machine Learning',\n",
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       "   '10/2022 11/2022',\n",
       "   'Logistic Regression Decision Tree Classifier',\n",
       "   'Logistic Regression LGBM Classifier RF Classifier',\n",
       "   '2',\n",
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       "   'First',\n",
       "   '10/2022',\n",
       "   '2',\n",
       "   'Completed',\n",
       "   '70%',\n",
       "   'R2',\n",
       "   'hours']},\n",
       " {'person': ['Al Imabetter',\n",
       "   'Learn SciPy',\n",
       "   'Pandas NumPy Matplotlib Seaborn',\n",
       "   'Utilizing Flusk',\n",
       "   'Jupyter Notebook',\n",
       "   'GitHub Git',\n",
       "   'Grid',\n",
       "   'cV',\n",
       "   'Story -Telling',\n",
       "   'Tableau Dashboards',\n",
       "   'Deep Learning',\n",
       "   'M. Tech',\n",
       "   'Sklearn SciPy',\n",
       "   'Wordcloud Silhouette Flbow',\n",
       "   'Proiect',\n",
       "   'DB Scan',\n",
       "   'ML Pandas',\n",
       "   'Seaborn Sklearn Feature',\n",
       "   'Handling',\n",
       "   'Removing Outliers'],\n",
       "  'organization': ['Data Science',\n",
       "   'Data Science and Machine\\nLearning',\n",
       "   'Banglore(Remote Database Machine Learning & Deep Learning',\n",
       "   'KNN',\n",
       "   'Google Colab',\n",
       "   'Tableau Power BI',\n",
       "   'Anal Ivtical Skills',\n",
       "   'Random Forest',\n",
       "   'Pvthon R SQL Machine Learning',\n",
       "   'Google Colab Postgre',\n",
       "   'SQL Tableau',\n",
       "   'ML Libraries',\n",
       "   'ML',\n",
       "   'ML',\n",
       "   'Data Science',\n",
       "   'VLSI Design',\n",
       "   '07/2022',\n",
       "   'Tags Unsupervised ML Topic Modeling',\n",
       "   'B.E in Electronics Engineering',\n",
       "   'Customers and Company',\n",
       "   'Nagpur',\n",
       "   'Performed the EDA',\n",
       "   'NLP Techniques',\n",
       "   'K',\n",
       "   'Elbow',\n",
       "   'Dendrogram RESEARCH & PUBLICATIONS',\n",
       "   'Internship & Assistantship',\n",
       "   'VLSI Design &\\nCompany Bankruptcy Prediction Al ImaBetter',\n",
       "   'Verified Proiect 7',\n",
       "   'UPTU',\n",
       "   '05/2022 06/2022',\n",
       "   'Imbalanced LO',\n",
       "   'Random Forest Logistic Regression',\n",
       "   'Amazon',\n",
       "   'EDA Capping',\n",
       "   'Handling Imbalanced',\n",
       "   'Performed OneHotEncoding',\n",
       "   'Feature Selections',\n",
       "   'XGBoost',\n",
       "   'Python & SQL Gold',\n",
       "   'HackerRank Coding Challenges'],\n",
       "  'location': [],\n",
       "  'date': ['2014 2017', '2017', '2022', '2022'],\n",
       "  'other': ['1',\n",
       "   'Exce Anaconda Vs',\n",
       "   '06/2021 Present Bangalore (Remote Data Engineering Data Visualization Data Dashboarding NLP',\n",
       "   'Logistic',\n",
       "   '8',\n",
       "   'Practiced Hands- On ML',\n",
       "   '2',\n",
       "   'IETW',\n",
       "   '2',\n",
       "   '4',\n",
       "   'Blogging',\n",
       "   'SMOTE',\n",
       "   'Model Testing',\n",
       "   'around 96%']},\n",
       " {'person': ['KAPIL GAWANDE',\n",
       "   'Topic Model ng Tensorflow / Keras',\n",
       "   'Sentiment Ana Iysi',\n",
       "   'Machine Learn',\n",
       "   'Subiect Matter Expertise'],\n",
       "  'organization': ['AI & ML',\n",
       "   'PROFILE',\n",
       "   'Experienced AI & ML Enginee',\n",
       "   'ML',\n",
       "   'OpenAland Hugging Face APIfor',\n",
       "   'NLP',\n",
       "   'NATURAL',\n",
       "   'SKILL',\n",
       "   'NLP PySpark',\n",
       "   'Al & ML',\n",
       "   'Utilized',\n",
       "   'Regression Classification',\n",
       "   'Data Cleaning Feature Engineering',\n",
       "   'Data Visualization\\nExperienced',\n",
       "   'Python for Data Science',\n",
       "   'Al &',\n",
       "   'IBM',\n",
       "   'Al & Development',\n",
       "   'IBM',\n",
       "   'NLP',\n",
       "   'Maharashtra Model',\n",
       "   'Microsoft Excel Auto',\n",
       "   'Bachelor of Engineering',\n",
       "   'Amravati University'],\n",
       "  'location': ['DEEP LEARNING'],\n",
       "  'date': ['9522330896', '2022'],\n",
       "  'other': ['1',\n",
       "   'OpenAI-AP',\n",
       "   'Hugging Face API',\n",
       "   '1',\n",
       "   'OpenAI',\n",
       "   '3\\\\2023',\n",
       "   'LinkedIn',\n",
       "   'Amravati',\n",
       "   'Openai\\nProficient',\n",
       "   '3dsMax']},\n",
       " {'person': ['linkedin.com/in/ kanohar',\n",
       "   'Rao',\n",
       "   'Ambedkar',\n",
       "   'Transfer',\n",
       "   'Pandas Keras\\nNLTK',\n",
       "   'LSTM Worked'],\n",
       "  'organization': ['Image Processing Computer Vision',\n",
       "   'NLP',\n",
       "   'classification& text\\nextraction',\n",
       "   'Feature Engineering\\nFeature',\n",
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       "   'BOQ',\n",
       "   'BOQ',\n",
       "   'Deep Learning',\n",
       "   'NLP',\n",
       "   'HAVC Design',\n",
       "   'Danush Engineering',\n",
       "   'India Pvt Ltd',\n",
       "   'BOQ',\n",
       "   'CGPA',\n",
       "   'SSLC',\n",
       "   'SKILLS\\nMachine learning Algorithms',\n",
       "   'Linear Regression Classification',\n",
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       "   'Support Vector Machines',\n",
       "   'RNN Ensemble Learning',\n",
       "   'CNN',\n",
       "   'Technical Skills',\n",
       "   'CodePipeline',\n",
       "   'Machine Learning',\n",
       "   'Data Analysis',\n",
       "   'Image Processing Data Mining\\nObject Detection & Segmentation',\n",
       "   'CERTIFICATIONS',\n",
       "   'MasteryTensorFlow Developer\\nCertificate',\n",
       "   'Mastery\\nUdemy Issued',\n",
       "   'Credential',\n",
       "   'NLP P Natural',\n",
       "   'Noble Work Foundation',\n",
       "   'Credential',\n",
       "   'UdemyUdemy Issued',\n",
       "   'DateIssued',\n",
       "   'NLP',\n",
       "   'SVM',\n",
       "   'NLTK',\n",
       "   'Handwritten Digit Recognition System',\n",
       "   'ML',\n",
       "   'LR',\n",
       "   'ARIMA',\n",
       "   'RNN',\n",
       "   'ML',\n",
       "   'CNN',\n",
       "   'Keras'],\n",
       "  'location': ['Key Responsibilities\\nExploratory',\n",
       "   'Key Responsibilities\\nProiect',\n",
       "   'Key Responsibilities\\nProject Design'],\n",
       "  'date': ['2022',\n",
       "   '09-2020 -09/ 2022',\n",
       "   'weekly',\n",
       "   'monthly',\n",
       "   '08-2019 -09/ 2020 1',\n",
       "   'weekly',\n",
       "   'monthly',\n",
       "   '2014-2018',\n",
       "   '2014',\n",
       "   '2023',\n",
       "   '2023',\n",
       "   'Mar 2023',\n",
       "   'Dec 2022',\n",
       "   'Oct 2022',\n",
       "   '2022',\n",
       "   '4017',\n",
       "   'Mar 2022',\n",
       "   '2022'],\n",
       "  'other': ['1',\n",
       "   'KANOHARAPV',\n",
       "   'Machine Learning',\n",
       "   'Neural Network Building\\nHVAC ENGINEER',\n",
       "   '2',\n",
       "   '7.80 1',\n",
       "   '70%',\n",
       "   '2ND PUC(PCMB',\n",
       "   '87.4%',\n",
       "   'TensorFlow 2.0',\n",
       "   'Lambda',\n",
       "   '3',\n",
       "   'TensorFlow Developer Certificate',\n",
       "   'Zero',\n",
       "   '7142676E939BCredentia',\n",
       "   '7142676E939B',\n",
       "   '4',\n",
       "   'OPENCV2',\n",
       "   'Tensorflow',\n",
       "   'Development']},\n",
       " {'person': ['Haryana',\n",
       "   'Soft Computing',\n",
       "   'Fuzzy',\n",
       "   'Ultrasound',\n",
       "   'SQL R. Cassandra',\n",
       "   'Kafka Flink 1',\n",
       "   \"John\\nMoore 's\",\n",
       "   'Gurugram',\n",
       "   'Gurugram',\n",
       "   'Prof Rekha',\n",
       "   'Schmitt Trigger',\n",
       "   'Matlab Simulink',\n",
       "   'B. Tech 2013',\n",
       "   'Med',\n",
       "   'Gurugram',\n",
       "   'Rohtak',\n",
       "   'Ape jay College',\n",
       "   'T6KHSXO9X4SD',\n",
       "   'Seminar',\n",
       "   'Kyoti Khan'],\n",
       "  'organization': ['Kvoti Khan Address House No-209',\n",
       "   'Marketing - Data Science : Analytics\\nComputer Vision',\n",
       "   'Neural Networks Speech',\n",
       "   'BERT',\n",
       "   'GPT',\n",
       "   'Computer Science',\n",
       "   'Neural Networks',\n",
       "   'Intuitionistic',\n",
       "   'C Operating Systems',\n",
       "   'C Simulation Software',\n",
       "   'Java\\nC Documentation Tool',\n",
       "   'Microsoft Office C',\n",
       "   'Hadoop',\n",
       "   'Tableau Power',\n",
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       "   'Power BI',\n",
       "   'ATLIQ mart',\n",
       "   'Conducted the Extract Transform I and Load ETL',\n",
       "   'Power BI',\n",
       "   'AirBnb Booking Analysis\\nTech Stack Python',\n",
       "   'Pandas',\n",
       "   'Communicated',\n",
       "   'Performed Exploratory Data Analysis',\n",
       "   'NLP',\n",
       "   'ML'],\n",
       "  'location': [],\n",
       "  'date': ['Jan 2022-Present'],\n",
       "  'other': ['Gained',\n",
       "   'Personal Proiects\\nOlympics Data Analysis using Power Bi\\nTech Stack',\n",
       "   'Olympics',\n",
       "   'Power Bi\\nTech Stack Power B\\nChallenge Defining Key Performance Indicators',\n",
       "   '1',\n",
       "   'Seoul',\n",
       "   'DataSc',\n",
       "   '2',\n",
       "   'one']},\n",
       " {'person': ['Jupyter Notebook',\n",
       "   'Jupyter Notebook',\n",
       "   'Solution',\n",
       "   'Scikit Learn',\n",
       "   'Tree Model',\n",
       "   'Jupyter Notebook',\n",
       "   'Matplotlib',\n",
       "   'Matplotlib',\n",
       "   'Clustering Algorithms',\n",
       "   'Windows Servers',\n",
       "   'Neural Provide'],\n",
       "  'organization': ['AFTENDRA',\n",
       "   'EDA',\n",
       "   'SMOTE',\n",
       "   'Mohitifha.mi58@gmail.com',\n",
       "   'GitHub',\n",
       "   'NLP',\n",
       "   'SKILLS',\n",
       "   'NLP',\n",
       "   'CNN',\n",
       "   'Domain 1 Rating Prediction Proiect\\nNLP Tech Stack Python',\n",
       "   'EDA',\n",
       "   'Classification',\n",
       "   'Pandas Key Achievement Successful',\n",
       "   'Random Forest',\n",
       "   'Seaborn Data Science Intern Flip Robo Technologies Bangalore IN',\n",
       "   'Regression Got',\n",
       "   'NLP',\n",
       "   'Lease Company Gwalior',\n",
       "   'Mar-17',\n",
       "   'Provide Team Members and Management',\n",
       "   'Networks Excellent',\n",
       "   'VMware',\n",
       "   'vCenter vRealize Operations',\n",
       "   'VMware\\nConfiguration',\n",
       "   'INTERESTS',\n",
       "   'BBA',\n",
       "   'CGPA',\n",
       "   'KRISHNAN UNIVERSITY',\n",
       "   'Data Trained Institute'],\n",
       "  'location': [],\n",
       "  'date': ['6 months', 'Nov 21', 'May 22', 'monthly'],\n",
       "  'other': ['2',\n",
       "   'Morar Gwalior',\n",
       "   'Objective Build',\n",
       "   'Tech Stack Python',\n",
       "   'SMOTE',\n",
       "   '6',\n",
       "   '1',\n",
       "   '1/',\n",
       "   'Vectorizer',\n",
       "   '95.2%',\n",
       "   'MySQL Objective Build a Classification',\n",
       "   'Cricket Watching Movies',\n",
       "   'Data']}]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Allcat_entities"
   ]
  }
 ],
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