AI Resume Analyzer

An AI-powered Resume Screening & Analysis API built using FastAPI, ChromaDB, Groq LLM, and RAG (Retrieval-Augmented Generation).

This program allows you to:

• Upload multiple resumes (PDF)
• Analyze resumes against a Job Description
• Rank candidates automatically
• Chat with a resume using AI
• Perform structured AI evaluation with strict recruiter logic

Live Demo

• Frontend: [link]

• Backend API Docs: [link]

Features

1️⃣ Resume Upload (Batch Support)

• Accepts multiple PDF resumes
• Extracts text using pypdf
• Splits text using RecursiveCharacterTextSplitter
• Stores embeddings in ChromaDB
• Session-based resume tracking

2️⃣ AI Resume Analysis

Analyzes resume against Job Description with:

• Matching keywords
• Missing keywords
• Experience validation
• Role mismatch detection

Structured recommendation:

• Strong Match
• Potential Match
• Reject

Results are automatically ranked by:

• Recommendation priority
• Number of matching keywords
• Years of experience

3️⃣ Resume AI Chat

Ask questions like:

• “Does this candidate have cloud experience?”
• “List backend skills”
• “Is this person fit for a data engineering role?”

The system:

• Retrieves top 3 relevant chunks
• Grounds answers strictly in resume content
• Returns concise bullet-point responses

Technical Stack

• Backend: FastAPI (Python)
• Frontend: Streamlit
• LLM Orchestration: Instructor + Groq (openai/gpt-oss-120b)
• Vector Database: ChromaDB
• Embeddings: FastEmbed (sentence-transformers/all-MiniLM-L6-v2)
• PDF Parsing: PyPDF

API Endpoints

Health Check

GET /

Response:

{
  "status": "OK",
  "message": "API is active"
}

Upload Resumes

POST /upload

Form Data:

• files → list of PDF files

Response:

{
  "message": "Success",
  "batch_id": "uuid-session-id",
  "count": 3
}

Analyze Resumes

POST /analyze

Request:

{
  "job_description": "Looking for a Backend Python developer with 3+ years experience in FastAPI and AWS",
  "session_id": "uuid-session-id"
}

Response:

{
  "results": [
    {
      "filename": "eg_resume.pdf",
      "analysis": {
          "matching_keywords": ["Python", "FastAPI", "AWS"],
        "missing_keywords": [],
        "profile_summary": "Strong backend alignment...",
        "recommendation": "Strong Match",
        "years_experience_actual": 4,
        "years_experience_required": 3
      }
    }
  ]
}

Chat with Resume

POST /chat

Request:

{
  "resume_id": "session||filename.pdf",
  "question": "Does this candidate have Docker experience?",
  "job_description": "Backend Python Developer role"
}

Response:

{
  "answer": "no"
}

Setup & Installation

Prerequisites

• Docker Desktop installed.
• A valid Groq API Key.

1. Clone the repository:

git clone https://github.com/Aman00240/RESUME_SCANNER
cd RESUME_SCANNER

2. Set up environment variables:

Create a .env file in the root directory:

GROQ_API_KEY=your_groq_key_here
model=model_name_here(eg.,llama-3.3-70b-versatile)

3. Run with Docker:

docker-compose up --build

4. Access the App:

• Frontend (UI): Open http://localhost:8501 in your browser.
• Backend (API Docs): Open http://localhost:8000/docs to test endpoints directly.