https://github.com/rijoslal/quizzy
https://github.com/rijoslal/quizzy
beautifulsoup chromadb ci-cd django django-rest-framework edge-tts gemini-api groq html-css-javascript huggingface-transformers langchain mlflow ollama tensorflow whisper
Last synced: 3 months ago
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- Host: GitHub
- URL: https://github.com/rijoslal/quizzy
- Owner: RijoSLal
- Created: 2025-02-23T19:12:21.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2025-04-01T06:10:05.000Z (over 1 year ago)
- Last Synced: 2025-04-01T06:28:48.814Z (over 1 year ago)
- Topics: beautifulsoup, chromadb, ci-cd, django, django-rest-framework, edge-tts, gemini-api, groq, html-css-javascript, huggingface-transformers, langchain, mlflow, ollama, tensorflow, whisper
- Language: Python
- Homepage:
- Size: 0 Bytes
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Quizzy
Quizzy is an AI-powered virtual interviewer built to simulate personalized interviews through a robust integration of Retrieval-Augmented Generation (RAG), multi-modal analysis (vision, audio, text), and intelligent evaluation systems. It processes resumes and job descriptions, evaluates candidates in real-time, and generates detailed feedback, scoring, and job suggestions.Quizzy delivers an end-to-end mock interview experience that is hyper-personalized and interactive.
Built on Django, Quizzy orchestrates a modular architecture where core machine learning components are managed in a dedicated ML repository. These components are dynamically pulled and tracked using MLflow, DagsHub, and MLOps pipelines, ensuring reproducibility, scalability, and version control for both data and models.
---
## Demo
**Video Overview**
Watch the full walkthrough of how Quizzy functions and performs an AI-driven interview:
[YouTube Demo Link](https://youtu.be/JGKYGQfVkoU)
---
## Architecture and Features
### Workflow
1. **Input Collection**: Users provide their resume, job description, and preferred interview time.
2. **Resume Analysis**:
- Name and domain extraction
- Resume-job description similarity using MXBAI embeddings (cosine similarity)
3. **Interview Phase**:
- Dynamic question generation via Groq's LLaMA 70B model
- Document retrieval from ChromaDB with Gemini embeddings
- Real-time TTS (Edge TTS) and STT (Whisper)
- Vision-based posture and emotion detection using MediaPipe and MobileNet
- Profile summarization using HuggingFace model
4. **Post Interview**:
- Score computation and evaluation report
- Suggestions for improvement
- Curated job recommendations from LinkedIn scraping based on candidate profile
### ML and MLOps Stack
- **Model Management**: MLflow
- **Experiment Tracking & Versioning**: DagsHub
- **Vision Models**: MobileNet (transfer learned), MediaPipe
- **Text Models**: Groq (LLaMA 70B), Gemini Embeddings
- **TTS/STT**: Edge TTS and Whisper
- **Resume Matching**: MXBAI embeddings
- **Document Store**: ChromaDB
> ML code and training pipelines are managed in a separate repository:
> [https://github.com/RijoSLal/Quizzy_MLOPS](https://github.com/RijoSLal/Quizzy_MLOPS)
> Models and data are versioned and stored at:
> [https://dagshub.com/slalrijo2005/Quizzy_MLOPS](https://dagshub.com/slalrijo2005/Quizzy_MLOPS)
---
### ⚠️ Minimum System Requirements
- **CPU:** 12 threads (e.g., 4P + 4E cores, 2.0 GHz+)
- **RAM:** 6 GB
- **GPU:** Integrated, 48 EUs+, OpenGL 4.5+
> ⚠️ Systems below these specs may face performance issues or instability.
---
## Deployment
To configure a **production-ready self-hosted environment using NGINX and Cloudflare Tunnel**, refer to my detailed blog post:
[Self-hosting Django with NGINX & Cloudflare Tunnel](https://rijo.hashnode.dev/self-hosting-django-with-nginx-cloudflare-tunnel-configure-a-production-ready-server)
---
## Local Development
### Requirements
- Python 3.8+
- pip
- virtualenv
### Setup
```bash
git clone https://github.com/RijoSLal/quizzy.git
cd quizzy
python -m venv venv
source venv/bin/activate # or venv\Scripts\activate on Windows
pip install -r requirements.txt
```
### Set Environment Variables
Create a `.env` file:
```env
APIKEY=gemini_api_key
GROQ=groq_api_key
GROQQ=groq_api_key
GROQ_API_KEY=groq_api_key
DJANGO=django_secret_key
```
### Run Tests
```bash
python manage.py test
```
---
### Run the Server
```bash
python manage.py runserver
```
---
## Docker Deployment
### Build Docker Image
```bash
docker build -t quizzy .
```
### Run with Environment File
```bash
docker run --env-file .env quizzy
```
### Run Tests
```bash
docker run --env-file .env quizzy python manage.py test
```
---
## Directory Structure
```
quizzy/
├── chroma_langchain_db/
├── interview/
| ├── urls.py
| ├── no_stream_camera_capture.py
│ ├── camera_capture.py
│ ├── resume_management.py
│ ├── retriever.py
│ ├── scrape.py
│ ├── speech.py
│ ├── vectordb.py
│ └── views.py
├── manage.py
├── quizzy/
│ ├── settings.py
│ └── urls.py
├── static/
| ├── images/
│ └── styling/
├── templates/
│ ├── home.html
│ ├── interview.html
│ ├── resume.html
│ └── score.html
├── Dockerfile
├── .dockerignore
├── .env
└── requirements.txt
```
---
## Contribution
Contributions are welcome. Please open an issue or submit a pull request if you would like to suggest improvements or contribute features. Ensure your contributions follow the project’s code standards and include relevant documentation.
---
## License
This project is licensed under the MIT License.
See the [LICENSE](./LICENSE) file for details.
---