🌟 Internify – AI-Powered Resume → Job Matching System

Internify is an AI/ML-powered system that analyzes resumes, extracts skills, and recommends the most relevant job roles using machine learning techniques such as TF-IDF, Logistic Regression, and KMeans clustering.

This repository contains:

• 🧠 Flask Backend API
• 🎨 Streamlit Frontend
• 🛢 SQLite Database Support
• 📄 PDF Parsing + NLP
• 📊 Job Matching Analytics

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🚀 Features

🔍 AI Resume Understanding

• PDF resume parsing
• Text extraction using NLP
• Skill & keyword detection
• TF-IDF vectorization

🤖 Machine Learning Pipeline

• Resume vectorization
• Logistic Regression scoring
• KMeans clustering
• Weighted final score
• Top job recommendations

🧩 Backend API (Flask)

• GET / – API health check
• POST /signup
• POST /login
• POST /upload_resume
• GET /matches?user_id=

🖥️ Frontend (Streamlit)

• Resume upload UI
• Job recommendations
• Score visualization (Pie chart + Histogram)
• User login + history

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📂 Project Structure

Internify/ │ ├── backend/ │ ├── app.py │ ├── recommender_pipeline.py │ ├── db_handler.py │ ├── models/ │ │ ├── content_filter.py │ │ ├── kmeans_model.py │ │ ├── logistic_regression.py │ │ └── nlp_parser.py │ ├── utils/ │ │ ├── pdf_to_text.py │ │ └── resume_parser.py │ ├── uploads/ # (empty → contains .gitkeep) │ └── database/ # (empty → contains .gitkeep) │ ├── frontend/ │ ├── streamlit_app.py │ └── requirements.txt │ ├── requirements.txt └── README.md

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🔧 Installation & Setup

Clone the repo:

git clone https://github.com/<your-username>/internify.git
cd internify

▶️ Run Backend (Flask)

Install backend dependencies:

pip install -r requirements.txt


Run server:

cd backend
python app.py


API available at:

http://127.0.0.1:5000

🖥️ Run Frontend (Streamlit)

Open new terminal:

cd frontend
pip install -r requirements.txt
streamlit run streamlit_app.py


Open in browser:

http://localhost:8501

🧪 API Testing
Health Check
GET /

Signup
POST /signup
{
  "name": "test",
  "email": "test@example.com",
  "password": "pass123"
}

Upload Resume (example)
curl -X POST \
  -F "user_id=1" \
  -F "file=@resume.pdf" \
  http://127.0.0.1:5000/upload_resume

🛠 Tech Stack
Backend

Python

Flask

SQLite

scikit-learn

PyMuPDF

Frontend

Streamlit

Pandas

Matplotlib

ML/NLP

TF-IDF

Logistic Regression

KMeans

👨‍💻 Author

Kriti Dogra
AI/ML Developer & Designer
GitHub: https://github.com/Kd1880

📜 License

This project is licensed under the MIT License.