https://github.com/rishi-gupta-data/smartscreen
SmartScreen ATS is an intelligent, AI-powered Applicant Tracking System designed to revolutionize the recruitment process for both job seekers and recruiters. Leveraging advanced natural language processing and machine learning, it analyzes resumes and job descriptions to deliver highly accurate candidate-job matches.
https://github.com/rishi-gupta-data/smartscreen
ai-agent ai-resume-analyzer ai-tools ats jobs resume-builder resume-parser resume-template
Last synced: 3 months ago
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SmartScreen ATS is an intelligent, AI-powered Applicant Tracking System designed to revolutionize the recruitment process for both job seekers and recruiters. Leveraging advanced natural language processing and machine learning, it analyzes resumes and job descriptions to deliver highly accurate candidate-job matches.
- Host: GitHub
- URL: https://github.com/rishi-gupta-data/smartscreen
- Owner: Rishi-gupta-data
- License: mit
- Created: 2025-05-27T12:28:14.000Z (4 months ago)
- Default Branch: master
- Last Pushed: 2025-07-12T14:58:02.000Z (3 months ago)
- Last Synced: 2025-07-12T16:25:11.926Z (3 months ago)
- Topics: ai-agent, ai-resume-analyzer, ai-tools, ats, jobs, resume-builder, resume-parser, resume-template
- Language: Python
- Homepage:
- Size: 2.59 MB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# SmartScreen ATS
SmartScreen ATS is an intelligent, AI-powered Applicant Tracking System designed to revolutionize the recruitment process for both job seekers and recruiters. Leveraging advanced natural language processing and machine learning, it analyzes resumes and job descriptions to deliver highly accurate candidate-job matches. The platform supports both individual and bulk resume analysis, offering actionable feedback and keyword optimization suggestions to candidates. Recruiters benefit from automated candidate ranking, skill extraction, and streamlined filtering, making hiring faster and more effective. With a modern React frontend and a robust Flask backend, SmartScreen ATS ensures a seamless, user-friendly experience. Whether you’re a candidate aiming to optimize your CV or a recruiter seeking the perfect fit, SmartScreen ATS empowers you to make smarter hiring decisions. Unlock the future of recruitment with data-driven insights and automation.
## Features
- Analyze individual resumes against job descriptions
- Bulk analysis of multiple resumes (ZIP upload)
- Resume formatting suggestions
- Keyword optimization suggestions
- Modern, user-friendly web interface
## System Architecture
```mermaid
graph TD
A["User"]:::user --> B["React Frontend"]:::frontend
B --> C["Flask Backend API"]:::backend
C --> D["Database"]:::db
C --> E["NLP/ML Models"]:::ml
classDef user fill:#b3ffd9,stroke:#000,stroke-width:2px,color:#000;
classDef frontend fill:#b3e0ff,stroke:#000,stroke-width:2px,color:#000;
classDef backend fill:#e6ffe6,stroke:#000,stroke-width:2px,color:#000;
classDef db fill:#fff9b3,stroke:#000,stroke-width:2px,color:#000;
classDef ml fill:#e6ccff,stroke:#000,stroke-width:2px,color:#000;
```
## Feature Flow
```mermaid
flowchart TD
Start(["Start"]) --> Mode["Select Mode"]
Mode --> IndBox["Individual Mode"] & BulkBox["Bulk Mode"]
IndBox --> IndUpload["Upload Resume & JD"]
IndUpload --> IndAPI["POST /api/analyze"]
IndAPI --> IndResult["Show Analysis & Suggestions"]
BulkBox --> BulkUpload["Upload ZIP of Resumes & JD"]
BulkUpload --> BulkAPI["POST /api/bulk-analyze"]
BulkAPI --> BulkResult["Show Bulk Analysis Results"]
Start:::start
Mode:::mode
IndBox:::indmode
IndUpload:::upload
IndAPI:::api
IndResult:::result
BulkBox:::bulkmode
BulkUpload:::upload
BulkAPI:::api
BulkResult:::result
classDef start fill:#b3ffd9,stroke:#000,stroke-width:2px,color:#000
classDef mode fill:#b3e0ff,stroke:#000,stroke-width:2px,color:#000
classDef indmode fill:#d1e7dd,stroke:#000,stroke-width:2px,color:#000
classDef bulkmode fill:#f8d7da,stroke:#000,stroke-width:2px,color:#000
classDef upload fill:#e6ffe6,stroke:#000,stroke-width:2px,color:#000
classDef api fill:#e6ccff,stroke:#000,stroke-width:2px,color:#000
classDef result fill:#fff9b3,stroke:#000,stroke-width:2px,color:#000
style IndBox fill:#FFCDD2
```
## API Endpoints Overview
```mermaid
flowchart LR
API1["🔍 POST /api/analyze"] --> BE["🧠 Backend"]
API2["📁 POST /api/bulk-analyze"] --> BE
API3["📝 POST /api/formatting-suggestions"] --> BE
API4["💡 POST /api/keyword-optimization"] --> BE
FE["🖥️ Frontend"] --> API1 & API2 & API3 & API4
API1:::api
BE:::backend
API2:::api
API3:::api
API4:::api
FE:::frontend
classDef api fill:#e1f5fe,stroke:#0288d1,stroke-width:2px,color:#000
style BE fill:#ffe9ba
style FE fill:#fad7d7
```
## Folder Structure
```
ATS_ai/
backend/ # Flask backend API
frontend/ # React frontend app
README.md # Project documentation
.gitignore # Git ignore rules
```
## Setup Instructions
### Prerequisites
- Python 3.8+
- Node.js 14+
### Backend Setup
```bash
cd backend
python -m venv venv
source venv/bin/activate # On Windows: venv\Scripts\activate
pip install -r requirements.txt
python app.py
```
### Frontend Setup
```bash
cd frontend
npm install
npm start
```
The backend runs on `http://localhost:5000` and the frontend on `http://localhost:3000` by default.
## API Endpoints
- `POST /api/analyze`: Analyze a single resume (PDF) and job description
- `POST /api/bulk-analyze`: Analyze multiple resumes (ZIP of PDFs) and job description
- `POST /api/formatting-suggestions`: Get formatting suggestions for a resume
- `POST /api/keyword-optimization`: Get keyword optimization suggestions
### Sample Backend Route (Flask)
```python
@app.route('/api/analyze', methods=['POST'])
def analyze_resume():
if 'resume' not in request.files or 'job_description' not in request.form:
return jsonify({'error': 'Missing resume or job description'}), 400
resume_file = request.files['resume']
job_description = request.form['job_description']
resume_text = ats_backend.extract_text_from_pdf(resume_file)
ats_backend.set_job_description(job_description)
analysis = ats_backend.analyze_resume(resume_text, job_description)
return jsonify(analysis)
```
### Sample Frontend API Call (React)
```javascript
const formData = new FormData();
formData.append('resume', file);
formData.append('job_description', jobDescription);
fetch('http://localhost:5000/api/analyze', {
method: 'POST',
body: formData,
})
.then(res => res.json())
.then(data => setAnalysis(data));
```
### Sample API Request/Response
**Request:**
`POST /api/analyze`
Form Data:
- resume: (PDF file)
- job_description: "Looking for a Python developer with NLP experience."
**Response:**
```json
{
"score": 85,
"matched_keywords": ["Python", "NLP", "Flask"],
"suggestions": ["Add more details about your NLP projects."]
}
```
## Usage
1. Start both backend and frontend servers as above.
2. Open the frontend in your browser.
3. Upload resumes and job descriptions as prompted.
## Contributing
Pull requests are welcome! For major changes, please open an issue first to discuss what you would like to change.