ResumeVisualizationWithNLP

Overview

Application is aimed to prove usage of Natural Language Processing (NLP) to analyze resume and visualize its content.

Features

• Resume Parsing: Extracts structured information from resumes in various formats (PDF, DOCX, etc.).
• Keyword Extraction: Identifies and highlights key skills, job roles, and other critical elements.
• Data Visualization: Generates insightful visualizations such as skill distributions, experience timelines, and keyword frequency graphs.
• Natural Language Processing: Uses NLP to analyze text for skills and experience.

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Technologies Used

• Programming Languages: Java, Python
• Libraries and Frameworks:
  • Apache OpenNLP
  • ModelMapper Mapstruct
  • Apache PDFBox
  • Apache Commons IO
  • Spring Boot
  • Spring Data JPA
  • gRPC
  • Hibernate ORM
• Database: PostgreSQL
• Deployment: Docker

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Installation

Prerequisites

• Java 17+
• Maven 3.6+
• Docker (optional for containerized deployment)

Steps

1. Clone the repository:

   git clone https://github.com/AlexejDumka/ResumeVisualizationWithNLP.git
   cd ResumeVisualizationWithNLP

2. Run the PostgreSQL database from container using Dockerfile:

      docker build -t resumeDB 

      docker run --name resumeDB-container -p 5432:5432 -d resumeDB

3. Run GRPC server from container using Docker compose:

      docker-compose up

4. Build the project using Maven:

      mvn clean install

5. Copy the resume file to the Resumes directory:
6. Run the application:

      mvn spring-boot:run

7. Access the of web UI of application in your browser at http://localhost:8080.

NOTE: To compile the gRPC server directly, navigate to the grpc_server directory and run the grpc_compile.sh script:

NOTE: To compile the gRPC client directly, performs 2 steps:

1. Check the pom file regarding the following dependencies:

    <!-- gRPC Dependencies -->
    <dependency>
        <groupId>io.grpc</groupId>
        <artifactId>grpc-netty-shaded</artifactId>
        <version>1.41.0</version>
    </dependency>
    <dependency>
        <groupId>io.grpc</groupId>
        <artifactId>grpc-protobuf</artifactId>
        <version>1.41.0</version>
    </dependency>
    <dependency>
        <groupId>io.grpc</groupId>
        <artifactId>grpc-stub</artifactId>
        <version>1.41.0</version>
    </dependency>
    
    <!-- Protocol Buffers -->
    <dependency>
        <groupId>com.google.protobuf</groupId>
        <artifactId>protobuf-java</artifactId>
        <version>3.17.3</version>
    </dependency>

2. run the following command:

protoc --java_out=src/main/java --grpc-java_out=src/main/java --proto_path=. text_processor.proto

Project folders and files structure

ResumeVisualizationWithNLP/
|-- pom.xml                        # Maven build file
|-- src/main/java/                 # Java source code
|-- src/main/resources/            # Application resources (templates, properties)
|-- README.md                      # Project documentation
|-- docker-compose.yml             # Docker Compose configuration
|-- Dockerfile                     # Application Dockerfile
|-- grpc_server/                   # gRPC server implementation
|-- grpc_server/Dockerfile         # gRPC server Dockerfile
|-- grpc_server/grpc_compile.py    # Script to compile gRPC protos
|-- grpc_server/docker-compose.yml # Docker Compose configuration for gRPC server
|-- grpc_server/gRPC.proto         # gRPC service definition 
|-- grpc_server/grpc_pb2.py        # gRPC Python bindings
|-- grpc_server/grpc_pb2_grpc.py   # gRPC Python bindings 
|-- grpc_server/requirerments.txt  # Python dependencies
|-- grpc_server/server.py          # gRPC server implementation
|-- postgreSQL/                    # PostgreSQL database setup
|-- postgreSQL/init-schema.sql     # SQL script for database scheme initialization
|-- postgreSQL/Dockerfile          # PostgreSQL Dockerfile
|-- postgreSQL/init-db.sh          # Script to initialize the database
|-- Resumes/                       # Directory for storing resume files
|-- Resumes/Processed/             # Directory for storing processed resume

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