AI-Powered Conversational Assistant

Project Overview

The AI-Powered Conversational Assistant is a project aimed at building a user-friendly chatbot application powered by AI. It combines cutting-edge technologies such as TensorFlow and Keras for model development, Angular for frontend design, and Firebase for backend services. This project is a perfect way to strengthen your skills in AI/ML, web development, and cloud integration.

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Features

• AI-based conversational assistant trained using TensorFlow and Keras.
• Real-time chat interface built with Angular.
• Firebase integration for user authentication and real-time database.
• Backend API using Flask to serve the trained AI model.
• Scalable deployment options with GCP or AWS.

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Tools and Technologies

• Programming Languages: Python, TypeScript
• Frameworks: TensorFlow, Keras, Angular, Flask
• Cloud Services: Firebase, GCP (Google Cloud Platform), AWS (Amazon Web Services)
• Libraries: pandas, numpy, scikit-learn, Flask-CORS
• Other Tools: Node.js, Firebase CLI, Angular CLI

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Learning Goals

This project is designed to help you:

1. Understand and apply AI/ML concepts using TensorFlow and Keras.
2. Build a modern web frontend with Angular.
3. Use Firebase for backend services like authentication and database management.
4. Deploy AI models and web apps to scalable cloud platforms like GCP or AWS.
5. Work end-to-end on a real-world AI/ML project.

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Prerequisites

Ensure you have the following installed:

• Python 3.8 or later
• Node.js and npm
• Angular CLI (npm install -g @angular/cli)
• Firebase CLI (npm install -g firebase-tools)
• TensorFlow and Keras libraries

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How to Run the Project

1. Clone the Repository

git clone https://github.com/rikulauttia/AI-Assistant.git
cd AI-Assistant

2. Install Required Libraries

pip install tensorflow keras flask flask-cors pandas numpy scikit-learn

3. Train the AI Model

Add your dataset to the data folder. Run the training script:

python train_model.py

The trained model (chatbot_model.h5) will be saved in the backend folder.

4. Start the Flask Backend

python app.py

Frontend: Angular Chat Interface

Set Up the Frontend

cd frontend
npm install
ng serve

Access the frontend at http://localhost:4200.

Firebase: Backend Services

Initialize Firebase

firebase login
firebase init

Deploy Firebase Functions

firebase deploy --only functions

Project Structure

AI-Assistant/
├── backend/ # Backend files
│ ├── venv/ # Virtual environment (ignored by .gitignore)
│ ├── train_model.py # Training script
│ ├── app.py # Flask API
│ ├── chatbot_model.h5 # Trained model
│ └── data/ # Dataset folder
├── frontend/ # Angular frontend
│ ├── src/ # Source code for frontend
│ ├── node_modules/ # Node dependencies
│ └── angular.json # Angular config
├── .gitignore # Ignored files
└── README.md # Project documentation

License

This project is licensed under the MIT License.