---
title: GenAI Career Agent
emoji: 💬
colorFrom: yellow
colorTo: purple
sdk: gradio
sdk_version: 5.42.0
app_file: app.py
pinned: false
hf_oauth: true
hf_oauth_scopes:
- inference-api
short_description: A generative AI model that acts as a career coach
---

A Resume–Job Fit Analysis chatbot built using **Gradio**, **FAISS Vector Search**, and the **Hugging Face Inference API**.  
This Space hosts the **GenAI Career Agent**, a generative AI that analyzes user resumes, retrieves structured resume data through a vectorstore (FAISS), and evaluates how well a candidate fits any provided job description.

### 🚀 Features

- **AI Career Coach** – Helps users understand job fit, strengths, and areas for improvement.
- **RAG Pipeline** – Uses FAISS to retrieve relevant resume chunks.
- **LLM-Powered Analysis** – Uses a remote Hugging Face model via `InferenceClient`.
- **Structured JSON Output** including:
  - `job_fit_score`
  - `fit_summary`
  - `strengths`
  - `missing_skills`
  - `recommendations`
- **Secure Token Handling** with Hugging Face Space Secrets.

### 📌 Current Capability

✔ **Resume Parsing & Analysis**  
The system currently parses the user's resume (pre-embedded with MiniLM) and produces job-fit analytics using RAG + LLM inference.

### 🛠️ Upcoming Features

🔜 **GitHub Repo Intelligence**  
- Automatic retrieval of repositories  
- Summarization of project impact  
- Extraction of tech stack & coding patterns  
- Integration into the job-fit score  

🔜 **LinkedIn Profile Integration**  
- Work history extraction  
- Skill inference  
- Keyword alignment  
- Soft-skill assessment  

These features will be integrated into the same RAG pipeline so the model can reason across **Resume + GitHub + LinkedIn** for a unified career profile.

### 🧠 How It Works

1. Resume data is pre-embedded using  
   `sentence-transformers/all-MiniLM-L6-v2`.
2. Embeddings are stored inside  
   `data/vectorstores/`.
3. The FAISS retriever fetches the most relevant resume sections based on the job description.
4. A custom prompt formats the retrieved text and sends it to the LLM.
5. The LLM generates structured JSON insights.

### 🗂️ Tech Stack

- **Gradio 5** (ChatInterface front-end)
- **LangChain Runnables**
- **FAISS** Vector Search
- **HuggingFace Embeddings**
- **Hugging Face Inference API**

### 🔒 Token Handling

Set secrets in your Space


### 📜 Model Licensing & Notices

🧍 Personal / Educational Use

This is a personal project, intended solely for educational and career-analysis purposes.
Users are solely responsible for how they use the outputs.