AI-powered cold email writer — paste a job URL, upload your resume, get a tailored cold email in seconds.
LangChain · Llama 3.1 70B · Groq · Streamlit · PyPDF2
Cold emailing hiring managers is one of the most effective job search strategies — and one of the most time-consuming to do well. This app automates it entirely.
Paste any job posting URL, upload your resume PDF, and the app scrapes the job description, extracts structured role requirements, cross-references your experience, and generates a personalized cold email using Llama 3.1 70B via Groq's inference API.
User pastes job URL + uploads resume PDF
│
▼
┌─────────────────────────┐
│ WebBaseLoader │ Scrapes full job posting page
│ (LangChain) │
└────────┬────────────────┘
│
▼
┌─────────────────────────┐
│ LLM extraction chain │ Llama 3.1 70B via Groq
│ (PromptTemplate + │ Extracts: role · experience
│ JsonOutputParser) │ skills · description → JSON
└────────┬────────────────┘
│
▼
┌─────────────────────────┐
│ PyPDF2 │ Extracts full text from
│ resume_extract.py │ uploaded resume PDF
└────────┬────────────────┘
│
▼
┌─────────────────────────┐
│ Email generation chain │ Llama 3.1 70B via Groq
│ (PromptTemplate) │ Combines job JSON + resume text
│ │ → Personalized cold email draft
└────────┬────────────────┘
│
▼
Streamlit UI displays generated email
| Layer | Technology |
|---|---|
| LLM | Llama 3.1 70B Versatile |
| Inference | Groq API (langchain-groq) |
| Orchestration | LangChain (PromptTemplate, JsonOutputParser) |
| Web scraping | LangChain WebBaseLoader |
| PDF parsing | PyPDF2 |
| UI | Streamlit + streamlit-shadcn-ui |
| Secrets management | Streamlit Secrets |
- Job URL scraping — paste any job posting URL, the app fetches and parses the full page
- Structured job extraction — LLM extracts
role,experience,skills, anddescriptionas clean JSON - Resume PDF upload — drag and drop your resume, text is extracted and passed to the email chain
- Personalized email generation — combines your experience with the specific job requirements
- One-click generation — single button press runs the full pipeline end to end
- Python 3.11+
- Groq API key — free tier available
# Clone the repo
git clone https://github.com/DebugJedi/coldEmail.git
cd coldEmail
# Create virtual environment
python -m venv venv
source venv/bin/activate
# Install dependencies
pip install -r requirements.txtCreate .streamlit/secrets.toml:
groq_api_key = "your_groq_api_key"post_url = "https://example.com/jobs/your-default-job"myurl = "https://your-portfolio-or-linkedin-url"resume = "path/to/your/resume.pdf"ignore = "words to exclude from email"comp_ignore = "company names to exclude"streamlit run streamlit_email_app.py
# → http://localhost:8501- Open the app at
http://localhost:8501 - Upload your resume PDF using the file uploader
- Paste the job posting URL into the text input
- Click Submit
- Wait ~10 seconds for the full pipeline to run
- Copy the generated cold email
coldEmail/
├── streamlit_email_app.py ← Streamlit UI · file upload · job URL input
├── emailGenerator.py ← E_generator class · orchestrates full pipeline
├── jobPosting.py ← Scrapes job URL · extracts structured JSON via LLM
├── resume_extract.py ← PyPDF2 resume text extraction
├── myData.py ← Loads personal portfolio/profile data
├── assets/
│ └── style.css ← Custom Streamlit styling
├── app/
│ └── resources/photos/ ← App header image
├── .streamlit/
│ └── secrets.toml ← API keys and config (gitignored)
└── requirements.txt
jobPosting.py uses WebBaseLoader to scrape the full job page, then passes it through a LangChain prompt chain instructing Llama 3.1 to return only valid JSON with four keys: role, experience, skills, description. JsonOutputParser parses the response into a Python dict.
resume_extract.py uses PyPDF2 to iterate all pages and concatenate extracted text — handles multi-page resumes cleanly.
emailGenerator.py combines the structured job JSON and resume text into a single prompt. The LLM is instructed to write a professional cold email targeting the hiring manager for that specific role, grounded in the candidate's actual experience.
Configurable ignore and comp_ignore fields in secrets allow you to exclude specific names or company references from the generated email — useful when applying from a current employer context.
- Job URL scraping and structured extraction
- Resume PDF upload and text extraction
- LLM-powered personalized email generation
- Privacy controls (name/company exclusions)
- ChromaDB integration for portfolio/experience vector store
- Multiple email tone options (formal / casual / aggressive)
- Export to clipboard / download as
.txt - Support for LinkedIn job URLs
- Streamlit Cloud deployment
Built and maintained by Priyank Rao — Data Scientist / ML Engineer
Portfolio · GitHub
MIT