Mid Term Report On
Online Bot Ticketing System
Project-I
BACHELOR OF TECHNOLOGY
(Computer Science and Engineering)
SUBMITTED BY:
Yash Thakur (2230901)
Kareena (2230787)
Kushal Sharma (2230793)
Ayush Gupta (2230753)
Under the Guidance of
Dr. Ashima
Associative Professor
Department of Computer Science & Engineering
Chandigarh Engineering College
Jhanjeri Mohali - 140307
Table of Contents
Sr Contents Page No
No
1. Introduction 1
2. System Requirements 3
3. Software Requirement Analysis 6
4. Software Design 8
5. Implementation 11
6. References 16
Chapter - 1
Introduction
A chatbot (originally chatterbot) is a software application or web interface designed to facilitate
textual or spoken conversations. Modern chatbots leverage generative artificial intelligence to maintain
natural language conversations, simulating human-like interactions.
With the rapid advancement of technology, customer interactions with businesses have evolved
significantly. In the entertainment industry, particularly cinema ticket booking, customers often face
issues such as:
• Long queues
• Limited ticket availability
• Difficulty in finding suitable showtimes
To address these challenges, this project proposes integrating a chatbot into a cinema ticket booking
website. The chatbot will act as a virtual assistant, enabling users to:
• Book tickets effortlessly
• Check showtimes in real-time
• Retrieve information about movies
This system aims to enhance user experience, reduce manual effort, and streamline the ticket booking
process.
1.1 Background
Traditional ticketing systems depend on human agents, leading to delays, high operational costs,
and inefficiencies. AI-powered chatbots have emerged as an efficient solution for streamlining customer
support and ticketing operations.
However, existing chatbot solutions often struggle with:
• Accurate intent recognition
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• Proper ticket categorization
• Seamless human handoff mechanisms
This project aims to bridge these gaps by developing a smart, interactive, and automated ticketing system
that enhances user experience and optimizes support operations.
1.2 System Architecture
The system consists of four primary components:
1. User Interface – A web or mobile chatbot where users can interact and submit queries.
2. Chatbot Engine – An AI-based logic system that understands and processes user requests.
3. Ticketing System – A backend database that stores, manages, and assigns tickets.
4. Cloud Service – A hosting platform like AWS, Azure, or Google Cloud to manage website
content and scalability.
This architecture ensures seamless automation, efficient ticket management, and enhanced
performance.
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Chapter - 2
System Requirement
2.1 Programming Languages
The chatbot system is primarily built using Python and JavaScript. Python is chosen for backend
development due to its powerful libraries for AI/ML integration, making it suitable for chatbot logic and
automation. JavaScript, on the other hand, is used for frontend development to create an interactive and
seamless user experience.
Key programming languages used:
• Python – Backend development, AI/ML integration, chatbot logic.
• JavaScript – Frontend development for a dynamic UI.
2.2 Frameworks and Libraries
To ensure a smooth and efficient development process, various frameworks and libraries are used.
Flask or Django will be used for backend development, enabling easy API handling and business logic
implementation. For the frontend, React.js will provide an interactive and responsive interface. If needed,
Node.js will handle server-side scripting and asynchronous operations, while Bootstrap or Tailwind CSS
will be used for UI styling.
Some important frameworks and libraries include:
• Flask/Django – Backend framework for API handling.
• React.js – Frontend library for a dynamic user interface.
• Node.js – Server-side scripting and asynchronous operations.
• Bootstrap/Tailwind CSS – UI design and styling.
2.3 Natural Language Processing (NLP) Tools
To make the chatbot intelligent and responsive, NLP tools will be integrated. These tools help
process user queries, understand intent, and generate meaningful responses. Google’s Dialogflow will
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provide built-in NLP capabilities, while Rasa, an open-source framework, will allow advanced
customization. Additionally, OpenAI's GPT API can enhance chatbot conversations by providing human-
like responses.
Key NLP tools:
• Dialog flow – Google-powered NLP for user query processing.
• Rasa – Open-source framework for chatbot development.
• OpenAI GPT API – AI-powered response generation.
2.4 Database Management Systems
A reliable database is essential for managing user data, chatbot logs, and booking history. MySQL
will be used to store structured data such as user profiles and transaction records, ensuring efficient
retrieval and management. MongoDB will complement MySQL by handling unstructured data, such as
chatbot interactions and logs, providing flexibility in data storage.
Database systems used:
• MySQL – Manages structured data (user details, booking history).
• MongoDB – Stores unstructured data (chat logs, user interactions).
2.5 Cloud Services
Cloud computing plays a vital role in hosting the chatbot and managing server loads. The system
will utilize platforms like Google Cloud, AWS, or Azure for hosting, ensuring scalability and security.
Additionally, Firebase may be used for real-time data storage and authentication if needed.
Cloud services considered:
• Google Cloud, AWS, Azure – For hosting and server management.
• Firebase – Real-time storage and authentication (if required).
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2.6 Version Control System
To ensure smooth collaboration and maintain version control, Git and GitHub will be used. These
tools will help track changes in the codebase, allow multiple developers to work simultaneously, and ensure
that previous versions of the code are easily accessible if needed.
Version control system used:
Git/GitHub – Source code management and collaboration.
2.7 API Integration
To provide real-time data and seamless transaction processing, several APIs will be integrated. A
Cinema Database API will be used to fetch live movie details, ticket availability, and show timings.
Additionally, secure payment gateways like Razorpay, Stripe, or Paytm will be used to handle online
transactions smoothly.
Key API integrations:
• Cinema Database API – Provides real-time movie and ticket details.
• Payment Gateway API – Manages secure transactions.
2.8 Development Environment
For efficient development and testing, a robust computing environment is required. A high-
performance processor, adequate RAM, and SSD storage will ensure smooth development and execution of
the chatbot system.
Recommended development setup:
• Processor: Intel i5/i7 or AMD Ryzen 5/7 (3.0 GHz or higher).
• RAM: Minimum 8GB (16GB recommended).
• Storage: Minimum 256GB SSD (512GB recommended).
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Chapter - 3
Software Requirement Analysis
3.1 Components of the System
The system consists of three primary layers: the Presentation Layer, the Logical Layer, and the
Database Layer. Each of these plays a crucial role in ensuring smooth user interactions, chatbot
operations, and data management.
• Presentation Layer (Frontend)
The frontend is responsible for providing a seamless user experience. It is developed using
React.js, ensuring a responsive and user-friendly interface. Through this layer, users can interact with the
chatbot via text or voice, browse movie schedules, and make bookings conveniently.
Key functionalities of the Presentation Layer:
• Displays movie schedules and booking details.
• Enables chatbot interaction via text or voice input.
• Provides payment options for ticket purchases.
• Logical Layer (Backend)
The backend handles chatbot logic, user requests, and booking processes. It is developed using
Python (Flask/Django) or Node.js, ensuring efficient request handling and response generation. The
backend also integrates NLP models (Dialogflow/Rasa) for understanding user queries and interacts with
external APIs for real-time movie and payment data.
Core functionalities of the Logical Layer:
• Processes chatbot responses and user requests.
• Connects with NLP models for natural language understanding.
• Manages API interactions for movie listings and payment processing.
• Database Layer
Data storage is a crucial component of the system, ensuring that all user information, bookings, and
transaction records are efficiently managed. MySQL is used for structured data like user details and
booking records, while MongoDB stores chatbot logs and interaction data.
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Key responsibilities of the Database Layer:
• Stores user details, booking records, and payment history.
• Maintains movie schedules and seat availability.
• Logs chatbot interactions for future improvements.
3.2 Modules of the System
The system is divided into multiple modules, each responsible for a specific function. These
modules work together to ensure smooth and efficient chatbot operations.
Key Modules:
• User Management Module – Handles user authentication, role-based access, and profile
management.
• Chatbot Engine – Processes natural language queries and provides AI-driven responses.
• Booking and Ticketing Module – Manages movie selection, seat booking, and ticket generation.
• Payment Processing Module – Integrates with secure payment gateways for seamless transactions.
• Notification Module – Sends automated SMS and email confirmations for bookings.
• Admin Panel Module – Allows administrators to manage movies, bookings, and system analytics.
3.3 Data Flow and Pattern
The chatbot-driven movie booking system follows a structured data flow to ensure efficiency and accuracy.
• User Interaction Flow:
1. User Initiates Chat – The user interacts with the chatbot using text or voice input.
2. NLP Processing – The chatbot analyzes the query using Dialogflow, Rasa, or OpenAI GPT API.
3. Database Query – The backend fetches relevant movie showtimes and details.
4. Seat Selection & Payment – The user selects a seat, makes a payment, and receives confirmation.
5. Ticket Generation – A digital ticket is created and sent via SMS or Email.
This structured flow ensures a smooth, AI-powered movie booking experience for users while maintaining
efficient data management.
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Chapter - 4
Software Design and Architecture
4.1 Architecture Diagram:
4.2 Components of System:
• A presentation Layer (Frontend):
◦ Developed using React.js to create a responsive and user-friendly interface.
◦ Allows users to interact with the chatbot via text or voice.
◦ Provides booking details, movie schedules, and payment options.
• Logical Layer (Backend):
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◦ Developed using Python (Flask/Django) or Node.js for handling chatbot responses and booking
management.
◦ Integrates with NLP models (Dialog flow/Rasa) to process user queries.
◦ Manages API calls to movie databases and payment gateways.
• Database Layer:
◦ Stores user details, booking records, movie schedules, and transaction history.
◦ Uses MySQL for structured data and MongoDB for chatbot interaction logs.
4.3 Modules of the System:
• User Management Module – Handles user authentication, role-based access, and profile
management.
• Chatbot Engine – Processes natural language queries and responds using AI models.
• Booking and Ticketing Module – Manages movie selection, seat booking, and ticket generation.
• payment Processing Module – Integrates with payment gateways for transactions.
• Notification Module – Sends SMS/Email confirmations and reminders.
• Admin Panel Module – Allows admin users to manage movies, bookings, and analytics.
4.4 Data Flow and Pattern:
• User Interaction Flow:
◦ User Initiates Chat – The user interacts with the chatbot via text or voice.
◦ NLP Processing – The chatbot processes the query using Dialog flow/Rasa/GPT API.
◦ Database Query – The backend fetches relevant movie/showtime details from the database.
◦ Seat Selection & Payment – The user selects a seat, makes payment, and receives
confirmation.
◦ Ticket Generation – A digital ticket is generated and sent via SMS/Email.
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• Sequence Diagram:
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Chapter - 5
Implementation
5.1. Introduction
The implementation phase of the AI-Driven Chatbot Ticketing System is the key stage where
coding, integration, testing, and deployment come together. During this phase, various system
components—such as the backend chatbot engine, frontend user interface, payment system, and database—
are carefully developed and integrated. This modular approach ensures that each part of the system works
seamlessly with others. The objective is to provide a functional, user-friendly, and secure ticketing platform
powered by AI.
5.2. Implementation Phases
5.2.1 Setting Up the Development Environment
The initial step in the implementation process is setting up the development environment. This ensures that
all tools and technologies are in place to develop the system efficiently.
• Programming Languages: Python (Flask/Django) for backend development, JavaScript (React.js)
for the frontend.
• Databases: MySQL for structured data (user profiles, bookings), and MongoDB for unstructured
data (chatbot logs).
• AI/NLP Engine: Dialogflow or Rasa for natural language processing to handle chatbot responses.
• Cloud Services: AWS, GCP, or Azure for hosting the system and ensuring scalability.
• Version Control: GitHub to manage code and enable collaborative development.
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This phase ensures that the development team can begin coding with all the necessary tools, libraries, and
frameworks.
5.3 Backend Development
The backend is responsible for the core functionality of the system, including chatbot logic,
database integration, and API handling. It connects the frontend to the data, handles user requests, and
performs essential actions like movie booking and payment processing.
Steps for Backend Development:
1. Setting Up the Server:
Flask or Django is used to create the RESTful APIs that handle various requests, such as chatbot
interactions, bookings, payments, and notifications.
2. Chatbot Engine Development:
The chatbot is built using Dialogflow/Rasa or OpenAI GPT API to process user queries. Intent
recognition is implemented to help the chatbot understand user input and respond accurately.
3. Database Integration:
MySQL stores structured data, while MongoDB is used for logging chatbot interactions. This helps
in tracking user behavior and improving chatbot responses over time.
4. External API Integration:
Movie information is fetched from external APIs to display up-to-date schedules and availability.
Payment gateways like Razorpay or Stripe are integrated to handle secure online transactions,
while notification APIs (e.g., Twilio, Firebase) are used to send SMS/Email updates.
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5.4 Frontend Development
The frontend of the system is developed using React.js, ensuring a responsive and engaging user
experience. This is where users interact with the system, making it crucial for the interface to be intuitive
and user-friendly.
Steps for Frontend Development:
1. Chatbot UI:
A conversational interface is created using React.js, allowing users to interact with the chatbot
through text or voice.
2. Movie Selection & Booking Page:
The page displays available movies, showtimes, and seat selection options. Data is dynamically
fetched from the backend, keeping the movie listings up-to-date.
3. Payment Integration:
Secure online payment functionality is added, enabling users to make payments for their bookings.
Once the payment is successful, a ticket is generated.
4. User Dashboard:
A personalized dashboard is provided for each user, showing their booking history and upcoming
movie details.
5.5 Security and Authentication Implementation
Security is a top priority to protect user data and ensure secure transactions. Several measures are
taken to ensure both data integrity and privacy throughout the system.
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Security Measures Implemented:
• OAuth 2.0 and JWT Authentication are used to ensure secure user logins.
• Role-Based Access Control (RBAC) ensures that only authorized users have access to certain parts
of the system, such as the admin panel.
• SSL/TLS encryption secures user data during transactions, safeguarding personal and payment
information.
5.6 Testing & Debugging
Testing is an essential phase to ensure that the system works smoothly and efficiently. Different
levels of testing are conducted to verify that each part of the system works as expected and to ensure the
chatbot provides accurate responses.
Types of Testing Conducted:
• Unit Testing: Ensures each individual module, like the chatbot engine or payment system, functions
correctly.
• Integration Testing: Checks how well the backend, frontend, and APIs work together.
• User Testing: Verifies the system's usability and ensures that the chatbot's responses are correct and
relevant to users' queries.
5.7 Deployment & Maintenance
After testing, the system is deployed to a cloud platform like AWS, GCP, or Azure to ensure
scalability and availability. A CI/CD pipeline is implemented to enable continuous integration and delivery
of updates to the system.
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Post-Deployment Activities:
• Monitoring: Logs and performance metrics are monitored regularly to ensure the system runs
smoothly.
• Chatbot Improvements: The chatbot is continuously improved based on user feedback and
interaction data.
• System Maintenance: Regular updates and fixes are applied to ensure the system remains secure
and performs well.
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Chapter - 6
REFERENCES
1. Jura sky, D., & Martin, J. H. (2020). Speech and Language Processing. Pearson.
2. McTear, M., Callejas, Z., & Griol, D. (2016). The Conversational Interface: Talking to Smart Devices.
3. Dialog flow Documentation: https://cloud.google.com/dialogflow/docs
4. Rasa Documentation: https://rasa.com/docs/
5. OpenAI GPT-3: https://openai.com/research/gpt-3
6. Images: https://tiledesk.com/blog/chatbot-ticketing-system,
https://www.xenioo.com/bookingassistant-chatbot
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