README.md

metadata

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

{
  "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