- Developed a name generation AI utilizing PyTorch and NumPy to construct a Deep Learning LSTM model, ensuring accurate and culturally relevant name suggestions.
- Integrated BeautifulSoup, AsyncIO, and Request to efficiently web-scrap diverse datasets containing names from various ethnicities and cultures, enhancing the training process.
- Initiated the project with a GAN-based model approach, swiftly pivoting to the LSTM model to achieve desired outcomes and improve name generation accuracy.
- Engineered a user-friendly web interface using Flask Python framework, facilitating seamless interaction and accessibility for users.
- Implemented cloud deployment on an AWS EC2 instance of Ubuntu, leveraging scalability and accessibility benefits for the hosted website.
- PyTorch
- TQDM
- NumPy
- SciKitLearn
- BeautifulSoup
- AsynclO
- Requests
cd website
pip install -r requirements.txt
python app.py
cd webScrapper-Py/MDb_scraper
python3 start.py
- You will get .csv files in the
webScrapper-Py
folder with nameactorsData.csv
.
pytest -vv
Know more about the project here