Spaces:
Runtime error
Runtime error
| 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. |