ApplyFlow: A Personalized Cold Email Generator for Job Applications

A smart AI-powered tool that auto-generates cold emails from job postings and your resume using LLaMA 3.1 via Groq.

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Overview

This project automates the process of writing personalized cold emails for job applications. It combines the power of Large Language Models (LLMs) with your resume and the content of job listings to generate a tailored, professional outreach email in seconds.

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Features

-Extracts job details directly from any public job posting URL -Parses resumes in PDF and DOCX formats -Uses LLaMA 3.1 via Groq to generate highly personalized cold emails -Clean UI built using Flask + Gunicorn -End-to-end AI pipeline for resume parsing, job scraping, and email generation

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🛠Tech Stack

Layer	Tools Used
Frontend (Web UI)	Flask
Backend (LLM & Processing)	Python, LangChain, Groq API
Parsing & Scraping	PyPDF2, python-docx, BeautifulSoup, LangChain WebBaseLoader
Deployment	Gunicorn
Environment Management	Python-dotenv

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Demo Screenshot

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⚙Installation & Setup

1. Clone the repository

git clone https://github.com/yourusername/cold-email-generator.git
cd cold-email-generator

2. Create a virtual environment

python3 -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install dependencies

pip install -r requirements.txt

4. Configure your .env

Create a .env file with the following:

GROQ_API_KEY=your_groq_api_key_here

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Run the App

Using Flask (for local testing)

python app.py

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Project Structure

cold-email-generator/
│
├── app.py                  # Flask entry point
├── chains.py               # Handles LLM prompts and logic
├── utils.py                # Text cleaning, resume parsing
├── requirements.txt        # Required libraries
├── .env                    # API key 
├── README.md               # Project documentation

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🔐 Security Note

Your .env file contains sensitive credentials (API keys) and must not be committed to version control. Add .env to your .gitignore.