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Candidate Evaluation Tool

AI-Powered Resume and Interview Analysis Platform

This tool simplifies candidate evaluation by integrating generative AI with traditional development techniques. It automates resume analysis and video interview evaluation, offering insights into a candidate's suitability for a role. Built using Python, LangChain, Streamlit, and other key technologies, the project provides a seamless and efficient workflow for managing every stage of the recruitment process.

Table of Contents

  1. Objective and Goal
  2. Setup Instructions
  3. Usage Guidelines
  4. System Architecture
  5. Dependencies

Objective and Goal

Objective: Build a tool that helps evaluate candidates more efficiently, focusing on two main areas: Resume Analysis and Interview Analysis. The tool should use advanced AI to make the evaluation process faster and more accurate, giving insights into how well a candidate fits a particular job.

Goal:

  • Automate the extraction and summarization of important details from resumes to provide a clear recommendation on whether the candidate is suitable for the job.
  • Analyze video interviews using Retrieval-Augmented Generation (RAG) to assess the candidate’s answers for accuracy and communication skills, offering detailed feedback.

Setup Instructions

Prerequisites

  • Python: Make sure you have Python 3.11.3 installed on your machine.
  • API Keys: You need the following API keys:
    • Gemini LLM: Get this from Gemini API.
    • Pinecone Vector Database: Create an account on Pinecone and obtain your API key.

Environment Setup

  1. Extract the Code:

  • Download the zip file of the project and extract it to your desired location.

  • Navigate to the project directory:

    cd <extracted-directory>

  1. Create a Virtual Environment:

    python3.11 -m venv myenv
source myenv/bin/activate    # On Windows: myenv\Scripts\activate

  2. Install dependencies:

    pip install -r requirements.txt
You may need to add the following line to download the spaCy model:
python -m spacy download en_core_web_sm

  3. Set up the environment variables: Create a .env file in the root directory with the following contents:

    GOOGLE_API_KEY=<your_gemini_api_key>
PINECONE_API_KEY=<your_pinecone_api_key>

  4. Run the Application:

    streamlit run FitFinder.py

Usage Guidelines

Step 1: Resume Analysis

  • Upload documents such as PDFs, Docs of Resume.
  • Write the Job Description.

Step 2: Interview Analysis

  • Upload the interview Video.
  • Without any clicks, wait for a few minutes to receive a scores,Transcript,Recommendation for that particular candidate

System Architecture

This tool integrates several AI-powered and traditional components to manage the full lifecycle of candidate analysis. Here's a brief overview:

  1. Resume Analysis:

    • Uploaded Resume are converted into text, cleaned, and stored.
    • Then using the googel gemini that provide a candidate resume summary.

  2. Interview Analysis:

    • Convert video into text and then text are divided into chunks, converted to embeddings, and stored in Pinecone for efficient retrieval.
    • The system finds the top 3 related chunks for generate a recommendation for the candidate.

Dependencies

Ensure you have all the required dependencies by installing them through requirements.txt. Here's an outline of key packages:

  • Python 3.11.3
  • Langchain
  • Whisper
  • Streamlit
  • Hugging Face (for embeddings)
  • Pinecone for vector database
  • Gemini LLM via API

Install dependencies:

pip install -r requirements.txt

License

This project is licensed under the MIT License. See the LICENSE file for more details.