Table of Contents
This project combines a support chatbot powered by a Seq2Seq model with a recommendation system built on content-based filtering. The chatbot assists users by providing real-time responses to their inquiries, while the recommendation system offers personalized suggestions based on user preferences and content analysis. Together, they enhance user engagement and satisfaction by delivering tailored assistance and relevant content.
- Multilingual Chatbot that seamlessly switches between Arabic and English.
- Real-time support for venue reservations and inquiries.
- Personalized venue recommendations based on user activities.
- Similar venue suggestions to enhance user exploration.
- Python v3.10.11+
- MySQL with our database design
- Clone the github repo.
# Cloning our github repo.
git clone https://github.com/Zabatly/AI.git-
Create & activate python virtual environment
-
Install python packages
# Installing all required python packages from requirements.txt
pip install -r requirements.txt- Run your MySQL database and make sure your details match the ones in
app.py
host = "localhost",
user = "root",
password = "",
database = "zabatly"- Run the application and enjoy!
python -m flask run- Check our universal application to interact with the chatbot.
The models and frameworks used in our project:
- TensorFlow
- scikit-learn
- Pandas & NumPy
- Flask
- MySQL
- Increase the chatbot's dataset even further for both languages
- Enhance the chatbot's NLU capabilities to understand user intents and entities more accurately
- Expand multilingual support to include more languages to reach a wider user base.
Special thanks to the team for the help across the board