Resume to Job Analysis Suite

A web application that analyzes a provided resumes and job posting using AI to better assess canidate skills and quality, identify resume factual omissions or strong points, and create a customized assesment suite, based on required skills and experience. For example, the assesment will generate code identifiation tasks for the candidate, tailored to the area the posting is for.

The application uses FastAPI for the backend, Next.js for the frontend, and LangChain for AI processing.

Prerequisites

• Python 3.8 or higher
• uv package manager
• Node.js 14 or higher
• npm
• OpenAI API key
• Groq API Key
• Hyperbolic API Key
• GitHub Token

Installation

1. Clone the Repository

git clone https://github.com/Abuynits/boilermake25
cd boilermake25

2. Set Up Python Environment

uv sync
source .venv/bin/activate

3. Set Up Frontend

# Navigate to frontend directory
cd frontend

# Install dependencies
npm install

4. Configure OpenAI API Key

Create a _secrets.py file in the repository root, and add these keys:

GROQ_KEY="your_groq_key"
GITHUB_TOKEN="your_github_token"
OPENAI_API_KEY="your_openai_api_key"
HYPERBOLIC_API_KEY="your_hyperbolic_api_key"

Running the Application

1. Start the Backend Server

In one terminal:

python -m backend.main

The backend will run on http://localhost:8000

2. Start the Frontend Development Server

In another terminal:

cd frontend
npm run dev

The frontend will run on http://localhost:3000

Usage

1. Open your browser and navigate to http://localhost:3000
2. Upload a resume (PDF or TXT format)
3. Paste the job posting text in the provided text area
4. Click Submit to analyze
5. View the analysis results below the form

Running any Python file

Say you wanna run folder_name/file_name.py. Do so from the repository root:

$ pwd
/path/to/boilermake25
$ source .venv/bin/activate # make sure venv is activated
$ python -m folder_name.file_name

# for example
$ python -m code_comprehension <github url> "<topics>"

You don't need to do a .something after code_comprehension because that folder has a __main__.py file.

Output

The application will:

• Display the analysis results on the web page
• Save JSON files with the analysis results in the backend/output directory
• Generate unique filenames with timestamps for each analysis