🤖 AI Resume Analyzer Pro

An AI-powered resume analysis and career guidance platform built using Python, Streamlit, CrewAI, and Google Gemini AI.

The application helps users analyze their resumes, evaluate job-role compatibility, identify missing skills, improve their resumes, prepare for interviews, and receive personalized career guidance.

🚀 Live Demo

🔗 Live Application: https://ai-resume-analyzer-pro-dashboard.onrender.com

💻 GitHub Repository

🔗 GitHub: https://github.com/tamjeedraj/AI-Resume-Analyzer-Pro-dashboard

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📌 Problem Statement

Resume screening and evaluation is often a time-consuming manual process.

Candidates may not know:

• Whether their resume matches a particular job role
• Which skills are missing
• What areas of their resume need improvement
• How ATS-friendly their resume is
• How to prepare for interviews based on their profile

Recruiters also spend significant time reviewing multiple resumes manually.

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💡 Solution

AI Resume Analyzer Pro automates the resume analysis process using Generative AI and a multi-agent architecture.

Users can upload their resume in PDF format, and the system processes the resume and uses AI agents to generate useful insights such as:

• Resume analysis
• Skill identification
• Job-role matching
• Improvement suggestions
• ATS-related analysis
• Interview preparation
• Career guidance

The project also integrates Vision AI and Voice AI to provide a more interactive user experience.

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✨ Key Features

📄 Resume Upload

• Upload resumes in PDF format
• Validate uploaded files
• Extract text and information from the resume
• Process extracted information for AI analysis

🤖 AI Resume Analysis

• Analyze resume content using Google Gemini AI
• Identify skills and experience
• Analyze strengths and weaknesses
• Generate personalized improvement suggestions

🎯 Job Matching

• Compare resume skills with job requirements
• Identify matching skills
• Identify missing or required skills
• Provide job-role-specific recommendations

📊 ATS Analysis

• Analyze resume content from an ATS perspective
• Identify potential improvement areas
• Provide suggestions to improve resume quality

👥 Multi-Agent AI System

CrewAI is used to create specialized AI agents for different tasks.

The agents can perform tasks such as:

• Resume analysis
• Skill evaluation
• Job matching
• Interview preparation
• Career guidance

👁️ Vision AI

Vision AI capabilities are integrated to support image-based analysis and improve the application's AI interaction experience.

🎙️ Voice AI

Voice-based interaction is integrated to make the application more interactive and user-friendly.

💬 Interview Preparation

The system can generate personalized interview preparation based on the candidate's resume and career profile.

🖥️ Streamlit Dashboard

A simple and interactive dashboard is developed using Streamlit for:

• Resume upload
• AI analysis
• Results display
• User interaction
• Career guidance

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🛠️ Tech Stack

Programming Language

• Python

Generative AI

• Google Gemini AI
• Large Language Models (LLMs)
• Prompt Engineering
• Generative AI

AI Framework

• CrewAI
• Multi-Agent AI Architecture

Document Processing

• PDF Processing
• PDF Text Extraction
• Resume Information Extraction

AI Features

• Vision AI
• Voice AI
• Speech-to-Text
• Text-to-Speech

Frontend

• Streamlit
• Custom CSS
• Interactive Dashboard

Development & Deployment

• Git
• GitHub
• Render
• Environment Variables
• pip / Python Virtual Environment

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🏗️ System Workflow

                ┌─────────────────────┐
                │      User           │
                └──────────┬──────────┘
                           │
                           ▼
                ┌─────────────────────┐
                │  Upload Resume PDF  │
                └──────────┬──────────┘
                           │
                           ▼
                ┌─────────────────────┐
                │   PDF Processing    │
                │  & Text Extraction  │
                └──────────┬──────────┘
                           │
                           ▼
              ┌──────────────────────────┐
              │     CrewAI Agents        │
              ├──────────────────────────┤
              │ Resume Analysis Agent    │
              │ Skill Analysis Agent     │
              │ Job Matching Agent       │
              │ Interview Agent          │
              │ Career Guidance Agent    │
              └────────────┬─────────────┘
                           │
                           ▼
                ┌─────────────────────┐
                │   Google Gemini AI  │
                └──────────┬──────────┘
                           │
                           ▼
                ┌─────────────────────┐
                │  AI Generated Report│
                │  & Recommendations  │
                └─────────────────────┘

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📂 Project Structure

AI-Resume-Analyzer-Pro-dashboard/
│
├── app.py
├── requirements.txt
├── README.md
├── .env
├── .gitignore
│
├── agents/
│   └── ...
│
├── tools/
│   └── ...
│
├── utils/
│   └── ...
│
└── assets/
    └── ...

The exact folder structure may vary depending on the current implementation of the project.

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⚙️ Installation & Setup

1. Clone the Repository

git clone https://github.com/tamjeedraj/AI-Resume-Analyzer-Pro-dashboard.git

Move into the project directory:

cd AI-Resume-Analyzer-Pro-dashboard

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2. Create a Virtual Environment

Windows

python -m venv venv
venv\Scripts\activate

Linux / macOS

python3 -m venv venv
source venv/bin/activate

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3. Install Dependencies

Install all required Python packages:

pip install -r requirements.txt

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4. Configure Environment Variables

Create a .env file in the root directory:

GOOGLE_API_KEY=your_google_gemini_api_key

Replace:

your_google_gemini_api_key

with your own API key.

⚠️ Security

Never upload your .env file or API keys to GitHub.

Add .env to your .gitignore:

.env
venv/
__pycache__/

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▶️ Run the Application Locally

After installing dependencies and configuring the environment variables, run:

streamlit run app.py

Streamlit will start the application locally.

You can then open the local URL shown in your terminal, usually:

http://localhost:8501

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🌐 Deployment on Render

The application is deployed on Render and connected to the GitHub repository.

Deployment Process

1. Push the project to GitHub.
2. Create a new Web Service on Render.
3. Connect the GitHub repository.
4. Select the Python environment.
5. Configure the required build/install command.
6. Add required environment variables.
7. Configure the Streamlit start command.
8. Deploy the application.
9. Test the live application.

Example Start Command

streamlit run app.py --server.port $PORT --server.address 0.0.0.0

Environment Variables on Render

Add your API key from the Render dashboard:

GOOGLE_API_KEY = your_google_gemini_api_key

Do not hard-code API keys inside Python files.

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🔑 Required Configuration

Before running the application, make sure you have:

• Python installed
• Required packages installed
• Google Gemini API key
• .env configured for local development
• Required environment variables configured on Render

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📈 Future Improvements

Possible future improvements include:

• More advanced ATS scoring
• Support for additional resume formats
• LinkedIn profile analysis
• More job-board integrations
• Advanced resume recommendations
• Improved interview simulation
• Real-time voice interview practice
• Resume template generation
• Candidate profile comparison

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👨‍💻 Author

Tamjeed Raj

Project

AI Resume Analyzer Pro

Technologies

Python Streamlit CrewAI Google Gemini AI Generative AI Vision AI Voice AI GitHub Render

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🔗 Project Links

• GitHub: https://github.com/tamjeedraj/AI-Resume-Analyzer-Pro-dashboard
• Live Demo: https://ai-resume-analyzer-pro-dashboard.onrender.com

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⭐ Project Highlights

• AI-powered resume analysis
• CrewAI multi-agent architecture
• Google Gemini AI integration
• PDF resume processing
• Job-role matching
• ATS-oriented analysis
• Interview preparation
• Career guidance
• Vision AI integration
• Voice AI integration
• Streamlit dashboard
• GitHub version control
• Render cloud deployment