🎓 CampusSafe Match

CampusSafe Match is a dual-mode, AI-powered Resume-to-Job analysis platform. Built for hackathons, it serves two distinct audiences natively from a single, powerful execution engine:

1. 👔 HR Recruiters: Instantly screens applicant resumes against job descriptions to assign Match Scores, Eligibility checks, and vital Scam Risk warnings.
2. 🎓 Students: Acts as an educational coach by dropping rigid corporate grading and replacing it with detailed "Learning Roadmaps," identifying exact skill deficiencies, and estimating growth timelines.

Everything is wrapped in a highly-polished, responsive dark-themed Streamlit dashboard with intelligent Plotly visualizations!

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✨ Features

• Dual UI Architecture: Radically alters the UX and data presentation conditional on whether the user selects HR Mode or Student Mode.
• Hybrid NLP Engine: Utilizes local sentence-transformers models (Hugging Face) for deep semantic understanding, while autonomously falling back to a scikit-learn TF-IDF keyword vectorizer if local memory constraints are hit.
• Scam Protection: Ingests company email domains and Job Description texts to warn users if the posting exhibits common job board phishing behaviors.
• Automated Coaching: "Student Mode" auto-generates comprehensive Learning Roadmaps comprising actionable "First Steps" and "Project Suggestions" strictly calibrated around their missing requirements.
• Persistent History: Every analysis is permanently logged locally into an SQLite database (campussafe.db) which surfaces to an interactive Data Table in the History page for historical lookup.

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🛠 Tech Stack

• Frontend: Streamlit, Custom CSS, Plotly
• AI/ML Core: Hugging Face SentenceTransformers (all-MiniLM-L6-v2), Scikit-Learn
• Data Processing: Pandas, NLTK-like custom RegEx matching, PyPDF, Python-docx
• Persistence: SQLite (Local Database)

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🚀 Quickstart Installation

1. Clone the Repository / Enter the Directory

   cd campussafe_match

2. Set up a Virtual Environment (Recommended)

   python3 -m venv venv
   source venv/bin/activate

3. Install Dependencies

   pip install -r requirements.txt

4. Launch the Application! Upon first launch, the app will briefly download the local HuggingFace AI model into cache.

   streamlit run app.py

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📁 File Structure Configuration

File	Purpose
app.py	The main execution entrypoint housing the core presentation layout and mode-shifting UI forms.
analysis.py	Contains the raw mathematical matching algorithms, semantic execution, and roadmap dictionary builders.
database.py	Initializes and connects the local .db SQLite logger.
ui.py	Houses the external Plotly metric dashboard generators and specialized layout CSS blocks.
utils.py	Safely parses PDF/DOCX file binary drops and runs the academic format vs resume validation scoring logic.
pages/1_History.py	The secondary Streamlit page that builds out a searchable history log.