Chatbot

The aim of this project was to develop a basic AI chatbot prototype that could be integrated into a SaaS model. The chatbot is designed to handle common user queries related to a specific domain using natural language processing (NLP) techniques. This report outlines the approach taken, technologies used, challenges faced, and setup instructions for testing the prototype.

1. Setup Instructions

1.1. Requirements

• Python 3.12 or higher
• Flask 2.0
• NLTK library (pip install nltk)

1.2. Steps to Run the Project

Backend Setup:

• Clone the repository containing the project file or extract the zip file provided.
• Install required Python dependencies using pip install -r requirements.txt.
• Set up NLTK by running Python and executing nltk.download('punkt') and nltk.download('stopwords') (provided in code) to download necessary resources.

Running the Flask App:

• Navigate to the project directory in the terminal.
• Run python app.py to start the Flask development server.

Accessing the Chatbot:

• Open a web browser and go to http://localhost:5000 to access the chatbot interface.
• Interact with the chatbot by typing queries into the input box and clicking "Send".

2. Technologies Used

• Python 3.12: Backend logic and NLP processing.
• Flask 2.0: Web framework for routing and handling HTTP requests.
• NLTK (Natural Language Toolkit): Python library for NLP tasks.
• JSON: Data format for storing FAQs and responses.
• HTML/CSS/JavaScript: Frontend interface for user interaction and displaying chatbot responses.

3. Approach

3.1. Functional Requirements

• Greetings and Farewells: The chatbot should handle basic greetings and farewells using predefined responses. (around 30 greetings options)
• FAQ Handling: It should answer frequently asked questions (FAQs) related to the domain using keyword matching and NLP techniques. (30 questions and answers)
• Fallback Response: Provide a fallback response for queries it cannot answer based on the FAQ database.

3.2. Technical Implementation

• Python: Utilized Python as the primary programming language for backend development.
• Flask: Chose Flask as the web framework for its simplicity and ease of integration with Python.
• NLTK (Natural Language Toolkit): Implemented basic NLP functionalities such as tokenization and stopwords removal using NLTK library.
• JSON: Stored FAQs and responses in a JSON format for easy retrieval and parsing.

3.3. Chatbot Workflow

• User Interaction: Users input queries through a web interface.
• Processing: The chatbot receives user queries, processes them using NLP techniques, and retrieves appropriate responses from the FAQ database.
• Response: Sends the response back to the user interface for display.

4. Challenges Faced

• NLP Accuracy: Ensuring accurate matching of user queries to relevant FAQs posed a challenge, especially with variations in user input.
• Deployment: Configuring the Flask application for deployment on different platforms and ensuring smooth integration with frontend components.
• User Experience: Designing a user-friendly interface that mimics natural conversation flow while maintaining simplicity.

5. Demo

6. Conclusion

The chatbot prototype successfully demonstrates the integration of basic AI concepts into a SaaS model, providing a responsive interface for handling user queries. By leveraging Python, Flask, and NLTK, the project achieves efficient NLP-based query handling and delivers predefined responses based on stored FAQs. Future enhancements could focus on improving NLP accuracy, expanding the FAQ database, and enhancing the user interface for a more intuitive user experience.