🤖 AI Job Application Generator

Automate your entire job search — from scraping listings to generating tailored CVs and cover letters — powered by LLMs and Streamlit.

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📌 Overview

Job hunting is repetitive and time-consuming. This project automates the entire workflow:

1. Scrape LinkedIn job postings with Selenium
2. Analyse job market trends with EDA
3. Generate tailored CVs and cover letters using an LLM
4. Deploy everything through a clean Streamlit interface

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🔁 Pipeline

LinkedIn Scraper → Job Dataset → EDA → LLM Generator → Streamlit App

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✨ Features

• 🔍 Scrape LinkedIn job postings by role and location
• 📊 Analyse hiring trends, keywords, and remote vs on-site patterns
• 🧠 Generate tailored CVs matched to specific job descriptions
• ✉️ Generate personalised cover letters per company
• 💻 Simple Streamlit UI — paste your CV, get your applications
• 📥 Download generated documents directly from the app

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🗂️ Project Structure

project/
│
├── webscraping_linkedin.ipynb   # LinkedIn job scraper
├── EDA.ipynb                    # Exploratory data analysis
├── LLM_cover+CV.ipynb           # LLM prompt engineering & generation
├── app.py                       # Streamlit application
├── data/                        # Scraped job datasets
└── README.md

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🧩 Components

🔎 Web Scraper

Collects LinkedIn job postings using Selenium + BeautifulSoup.

Extracted fields:

• Job title, company name, location
• Full job description
• Company description

📈 Exploratory Data Analysis

Analyses trends in the scraped job dataset:

• Job distribution by location
• Remote vs on-site ratio
• Company hiring patterns
• Keyword frequency in job descriptions

🤖 LLM Application Generator

Uses the OpenAI API to generate personalised job applications.

Inputs: master CV + job description + company info
Outputs: tailored CV + personalised cover letter

🖥️ Streamlit App

User-friendly interface to:

• Paste your CV
• Enter job descriptions
• Generate applications in one click
• Download results

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🚀 Getting Started

1. Clone the repo

git clone https://github.com/gail-mar/final_project.git
cd final_project

2. Install dependencies

pip install -r requirements.txt

3. Set up your API key

Create a .env file in the root directory:

OPENAI_API_KEY=your_api_key_here

4. Run the app

streamlit run app.py

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🔄 Example Workflow

1. Run webscraping_linkedin.ipynb to collect job postings
2. Run EDA.ipynb to explore the dataset
3. Open the Streamlit app
4. Paste your CV and a job description
5. Click Generate — get a tailored CV and cover letter instantly

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🛠️ Tech Stack

Tool	Purpose
Python	Core language
Selenium + BeautifulSoup	Web scraping
Pandas + Matplotlib	Data analysis & visualisation
OpenAI API	LLM-powered generation
Streamlit	Web app interface

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👩‍💻 Author

Gail Marechal — Data Science & AI

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Built as a final project for a Data Science & AI bootcamp.