README.md

---
title: Nsbecf
emoji: 👀
colorFrom: yellow
colorTo: purple
sdk: gradio
sdk_version: 6.9.0
app_file: app.py
pinned: false
---

# AI Career Fair Matcher

AI Career Fair Matcher helps students prioritize career fair companies by analyzing resume fit against live job postings.

## What It Does
- Accepts a resume PDF.
- Uses a built-in company CSV and optionally accepts a user-uploaded CSV.
- Extracts resume text and builds a structured profile JSON.
- Uses AI resume parsing through Hugging Face InferenceClient with fallback parsing.
- Detects ATS providers from company careers URLs.
- Fetches jobs using requests first and Playwright fallback.
- Scores jobs with explainable rules.
- Ranks companies by fit.
- Generates recruiter talking points.

## Project Structure
- `app.py`
- `src/resume/`
- `src/jobs/`
- `src/scoring/`
- `src/output/`
- `NSBE 2026 Baltimore Company_ Schools  - Companies.csv` (built-in default)
- `data/NSBE 2026 Baltimore Company_ Schools  - Companies (1).csv` (alternate built-in)

## Resume Profile Schema
```json
{
  "skills": [],
  "languages": [],
  "frameworks": [],
  "tools": [],
  "target_titles": [],
  "locations": [],
  "experience_level": ""
}
```

## Matching Rules
- Rewards skill overlap.
- Rewards role match.
- Rewards entry-level signals.
- Penalizes senior role signals.

## Local Run
1. Install dependencies:
	`pip install -r ../requirements.txt`
2. Optional but recommended for Playwright fallback:
	`playwright install chromium`
3. For AI resume parsing, set in `.env`:
  `HF_TOKEN=...`
  `HF_MODEL=meta-llama/Llama-3.1-8B-Instruct`

## Output
- Ranked companies
- Matching jobs
- Resume profile JSON
- Recruiter talking points