Resume Processing API

This repository contains a FastAPI-based application designed to upload resumes, process them, and extract structured data in JSON format. The application integrates Kafka for asynchronous messaging and supports file uploads via the /upload endpoint.

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Features

• File Upload and Processing: Upload resumes (e.g., PDFs or text files) and process them in a single step.
• Structured Data Extraction: Extracts structured information such as name, contact details, education, skills, and experience from resumes.
• Kafka Integration: Sends processed data to a Kafka topic for further downstream processing.
• Scalable Architecture: Built with FastAPI and Kafka for high performance and scalability.

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Prerequisites

1. Docker: Ensure Docker is installed for running Kafka and Zookeeper.
2. Python: Install Python 3.8+.
3. Dependencies: Install the required Python packages listed in requirements.txt.

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Project Structure

parser/
├── main_application/
│   ├── app.py                # Main FastAPI application
│   ├── routes/
│   │   └── upload.py         # Upload and processing endpoint
│   ├── services/             # Kafka producer and consumer services
│   ├── utils/                # Utility functions (e.g., JSON extraction, logging)
│   ├── models/               # Pydantic models for structured data
│   ├── config.py             # Configuration file for environment variables
├── docker-compose.yml        # Docker configuration for Kafka and Zookeeper
├── requirements.txt          # Python dependencies

Setup Instructions

1. Clone the Repository

git clone https://github.com/your-repo/resume-processing-api.git
cd resume-processing-api

2. Start Kafka Using Docker

Run the following command to start Kafka and Zookeeper:

docker-compose up -d

3. Install Python Dependencies

Create a virtual environment and install the dependencies:

python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate
pip install -r [requirements.txt]

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Running the Application

1. Start the FastAPI Server

Run the following command to start the application:

python app.py

The server will start at http://127.0.0.1:8000.

2. Test the /upload Endpoint

Use Postman or curl to test the /upload endpoint.

Example response

{
  "filename": "resume.pdf",
  "result": {
    "name": "John Doe",
    "contact": {
      "phone": "+1-123-456-7890",
      "email": "johndoe@example.com",
      "linkedin": "https://linkedin.com/in/johndoe",
      "github": "https://github.com/johndoe"
    },
    "education": [
      {
        "degree": "Master of Science",
        "field": "Computer Engineering",
        "university": "San Jose State University",
        "start_date": "Aug. 2023",
        "end_date": "May 2025"
      }
    ],
    "technical_skills": {
      "languages": ["Python", "C/C++", "SQL"],
      "tools": ["TensorFlow", "PyTorch", "Docker"]
    },
    "experience": [
      {
        "title": "Deep Learning Project Assistant",
        "company": "San Jose State University",
        "location": "San Jose, CA",
        "start_date": "Aug. 2024",
        "end_date": "Present",
        "responsibilities": [
          "Utilizing Semantic Textual Similarity (STS) and pre-trained generative models to build an AI-driven recruitment platform.",
          "Integrating real-time data streams using Kafka for asynchronous processing."
        ]
      }
    ]
  }
}

Key Components

/upload Endpoint Method: POST Description: Handles file uploads and processes the content to extract structured data. Input: A file (e.g., PDF or text file). Output: JSON containing structured resume data.

Kafka Integration

Producer: Sends processed resume data to the processed_resume_topic. Consumer: Can be extended to consume messages for downstream processing.

Development Notes

The application is tested for PDF files but can be extended to support other formats. The /process endpoint is not used, as the /upload endpoint handles both uploading and processing. Ensure Kafka is running before starting the application.

Future Enhancements

Add support for additional file formats (e.g., DOCX). Implement authentication and authorization for secure API access. Extend Kafka consumer functionality for real-time analytics.

License

This project is licensed under the MIT License. See the LICENSE file for details.