semvec/pypi-packages huggingface dataset:
ai-resume-screener 0.1.0
AI-powered resume screening tool using LLMs and Streamlit
Requires Python: >=3.7

# 🧠 AI-Powered Resume Screener

A lightweight resume screening app that uses AI to extract key candidate details from PDFs and evaluates them based on skills. Built with **Streamlit**, **SQLite**, and **PyMuPDF**. Ideal for HR teams, devs, or anyone needing a smart CV filter!

---

## 📸 Screenshots

![Home Screen](assets/upload_page.jpg)
*Upload resumes and view extracted details*

![Parsed Resume Table](assets/ranked_shortlisting.jpg)
*See candidate info and download PDF*

---

## 🚀 Features

- 📄 PDF Resume Upload
- 🧠 LLM-Based Evaluation (GPT or any open-source LLM)
- 🧑‍💻 Name, Email, Skills Extraction
- 🗃 Resume Storage with SQLite
- 📊 Interactive Table View

---

## 🛠️ Tech Stack

- **Frontend**: Streamlit
- **Backend**: Python, PyMuPDF, SQLite
- **AI**: OpenAI / GPT-compatible LLMs / Together.ai (pluggable)

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🔄 Pluggable LLM Support
By default, this project uses GPT for resume evaluation.
You can use any open-source LLM model (like Mistral, LLaMA, Gemma, etc.) by simply updating the model name or API in the gpt_evaluator.py file.

---

## ⚙️ Getting Started

```bash
git clone https://github.com/your-username/ai-resume-screener.git
cd ai-resume-screener
pip install -r requirements.txt
streamlit run app.py
vaultrun 2.0.13
Rofi/Dmenu to interact with vault.
Requires Python: <4.0,>=3.9

# vault-cli
Vault cli helper

# Requirements
[rofi](https://github.com/davatorium/rofi) or [dmenu](https://tools.suckless.org/dmenu/) needs to be installed.
Optionally, `xsel`, if exists, selected secrets will be copied to clipboard.

config file should be located at `/home/{user}/.config/vaultrun/config.yaml` with:

```yaml
name:
  mount_point: <Secret mount point>
  secret_path: <Secret path to query>
  # If role_id and secret_id are not provided, use OIDC login
  role_id: <Role ID>
  secret_id: <Secret ID>
```

# Installation

from Pypi:

```bash
python -m pip3 install vaultrun```
```

Local installation:

```bash
poetry install
```

# Usage

From Pypi:

```bash
vaultrun
```

Local run:

```bash
poetry run vaultrun
```