NLP Chatbot Application

Project Overview

This NLP Chatbot Application is a web-based conversational agent built with Python, Flask, and natural language processing techniques. The chatbot provides an intuitive user interface for interactive conversations, responding to user queries using pattern matching and context-based response generation.

Live Demo: https://chatbot-app-d9i6.onrender.com/
Repository: https://github.com/Ismat-Samadov/chatbot_app

Features

Real-time Conversation: Instant messaging interface with dynamic responses
Pattern Recognition: Identifies user intents through regex pattern matching
Context-based Responses: Generates appropriate replies based on identified topics
Dark/Light Theme Toggle: User-customizable interface appearance
Responsive Design: Fully functional across desktop and mobile devices
Typing Indicators: Visual feedback when the chatbot is "thinking"
Timestamp Display: Shows the time each message was sent
Simple Deployment: Easy to deploy on cloud platforms like Render

Architecture

The application follows a client-server architecture with the following components:

flowchart TD
    A[User Interface] <--> B[Flask Web Server]
    B <--> C[NLP Processing Engine]
    
    subgraph "Frontend"
    A
    end
    
    subgraph "Backend"
    B
    C
    end
    
    C --> D[(Pattern Database)]
    C --> E[(Response Templates)]

Data Flow

sequenceDiagram
    participant User
    participant Frontend
    participant Backend
    participant NLP Engine
    
    User->>Frontend: Enter message
    Frontend->>Backend: Send HTTP POST request
    Backend->>NLP Engine: Process message
    NLP Engine->>Backend: Return appropriate response
    Backend->>Frontend: Send JSON response
    Frontend->>User: Display bot response

Technology Stack

Frontend

HTML5/CSS3: Page structure and styling
JavaScript (ES6+): Client-side interactivity
Font Awesome: Icons for user interface elements
Google Fonts (Poppins): Typography

Backend

Python 3.x: Server-side programming
Flask: Web framework
Regular Expressions: Pattern matching for intent recognition
Random module: Response variation for natural conversation
JSON: Data exchange format

Deployment

Render: Cloud platform hosting
Git/GitHub: Version control and source code management
Gunicorn: WSGI HTTP Server for production

Implementation Details

NLP Processing Approach

The chatbot employs a rule-based approach to natural language processing:

Intent Recognition: Uses regular expressions to match user inputs with predefined patterns
Response Selection: Chooses appropriate responses from categorized templates
Contextual Analysis: Identifies specific keywords to provide topic-relevant replies
Default Handling: Provides general responses when no specific pattern is matched

This approach provides a foundation for conversational interaction while remaining lightweight and efficient for web deployment.

Installation and Setup

Prerequisites

Python 3.7 or higher
pip (Python package manager)
Git (optional, for cloning the repository)

Local Installation

Clone the repository:

git clone https://github.com/Ismat-Samadov/chatbot_app.git
cd chatbot_app

Create and activate a virtual environment:

python -m venv venv

# On Windows
venv\Scripts\activate

# On macOS/Linux
source venv/bin/activate

Install dependencies:
```
pip install -r requirements.txt
```
Run the application:
```
python app.py
```
Open your browser and navigate to:
```
http://localhost:8080
```

Usage Guide

Starting a Conversation

Open the chatbot application in your web browser
The chatbot will greet you with a welcome message
Type your message in the input field at the bottom
Press Enter or click the send button to submit

Example Interactions

Greetings: "Hi", "Hello", "Hey there"
Questions about the bot: "Who are you?", "What can you do?"
Topic-specific queries: "Tell me about Python", "What's this course about?"
Closing the conversation: "Goodbye", "Thanks", "Bye"

Theme Toggle

Click the moon/sun icon in the top-right corner to switch between light and dark themes.

Code Structure

The project follows a clear organization pattern:

chatbot_app/
│
├── app.py                  # Main Flask application
├── requirements.txt        # Python dependencies
├── .gitignore              # Git ignore rules
├── LICENSE                 # MIT License
│
├── static/                 # Static assets
│   ├── style.css           # CSS styling
│   └── script.js           # Client-side JavaScript
│
└── templates/              # HTML templates
    └── index.html          # Main page template

Pattern Matching System

The chatbot uses regular expressions to identify user intents:

patterns = {
    "greeting": r"(?i)^(hi|hello|hey|greetings|good (morning|afternoon|evening)).*",
    "goodbye": r"(?i)^(bye|goodbye|see you|farewell|exit|quit).*",
    "thanks": r"(?i)^(thanks|thank you|appreciate it|grateful).*",
    "how_are_you": r"(?i)^(how are you|how('s| is) it going|what('s| is) up|how do you do).*",
    "who_are_you": r"(?i)^(who are you|what are you|tell me about yourself|introduce yourself).*",
    "capabilities": r"(?i)^(what can you do|what are your capabilities|help|functions|features).*"
}

Key features of this system:

Case insensitivity: Using (?i) flags
Pattern alternatives: Pipe operator | for multiple matching options
Grouping: Parentheses to organize complex patterns
Beginning of string: ^ anchor to match from the start
Wildcards: .* to allow any text after the pattern

Response Generation

Responses are organized by intent categories:

responses = {
    "greeting": ["Hello!", "Hi there!", "Greetings!", "Hey, how can I help?"],
    "goodbye": ["Goodbye!", "See you later!", "Bye!", "Take care!"],
    "thanks": ["You're welcome!", "Glad I could help!", "No problem!", "Anytime!"],
    "how_are_you": ["I'm doing well, thanks for asking!", "I'm good! How about you?", "I'm fine, how can I help?"],
    "who_are_you": ["I'm a simple chatbot built with Python and Flask.", "I'm your AI assistant, how can I help?"],
    "capabilities": ["I can answer simple questions and have basic conversations.", "I'm a basic chatbot that can respond to various prompts."],
    "default": ["I'm not sure I understand. Could you rephrase that?", "Interesting. Tell me more.", "I'm still learning. Could you elaborate?"]
}

The process flow for generating responses:

flowchart TD
    A[Receive User Message] --> B{Pattern Match?}
    B -->|Yes| C[Select Response from Matched Intent]
    B -->|No| D{Keyword Present?}
    D -->|Yes| E[Select Relevant Contextual Response]
    D -->|No| F[Select Default Response]
    C --> G[Return Response to User]
    E --> G
    F --> G

UI/UX Design

Design Philosophy

The chatbot interface was designed with the following principles:

Simplicity: Clean layout focusing on the conversation
Familiarity: Chat interface patterns users recognize
Accessibility: Clear typography and contrasting colors
Responsiveness: Adaptable to different screen sizes
Visual Feedback: Typing indicators and message animations

Theme Options

flowchart TD
    A[User Interface] --> B{Theme Selection}
    B -->|Light Theme| C[Light Background / Dark Text]
    B -->|Dark Theme| D[Dark Background / Light Text]
    C --> E[Blue Accent Colors]
    D --> F[Purple Accent Colors]

Responsive Breakpoints

Desktop: 800px max-width container
Tablet: 768px responsive adjustments
Mobile: 480px further optimizations

Deployment

The application is deployed on Render, a cloud platform that simplifies web service hosting.

Deployment Process

Repository Setup: Code pushed to GitHub repository
Render Configuration:
- Connected to GitHub repository
- Set Python environment
- Specified build command: pip install -r requirements.txt
- Set start command: gunicorn app:app
Environment Configuration:
- Set PORT environment variable
Deployment Trigger: Automatic deployment on main branch updates

Deployment Architecture

flowchart LR
    A[Local Development] -->|Git Push| B[GitHub Repository]
    B -->|Webhook| C[Render Build Process]
    C -->|Deploy| D[Render Web Service]
    D -->|Serve| E[End Users]

Future Enhancements

The current implementation provides a foundation that can be extended in several ways:

Short-term Improvements

Extended vocabulary: Add more patterns and responses
Session management: Remember conversation context
User feedback system: Collect ratings on response quality

Medium-term Features

Integration with transformer models: Implement Hugging Face transformers
Multi-language support: Add capability for different languages
Voice input/output: Add speech recognition and synthesis

Long-term Vision

Machine learning pipeline: Train on conversation data
API integrations: Connect to weather, news, or other services
Personalization: Learn user preferences over time

Lessons Learned

Technical Insights

Pattern matching limitations: Simple regex patterns work for basic intents but struggle with complex queries
Frontend-backend communication: Handling asynchronous requests properly
Deployment considerations: Environment configuration for cloud platforms

Project Management

Scope definition: Starting with a minimal viable product
Feature prioritization: Building core functionality first
Documentation importance: Maintaining clear explanations of code functionality

Contributors

Ismat Samadov - Initial work and maintainer

License

This project is licensed under the MIT License - see the LICENSE file for details.

MIT License

Copyright (c) 2025 Ismat-Samadov

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.

Name		Name	Last commit message	Last commit date
Latest commit History 6 Commits
static		static
templates		templates
.gitignore		.gitignore
LICENSE		LICENSE
README.md		README.md
app.py		app.py
requirements.txt		requirements.txt

License

basic-tutorials/chatbot_app

Folders and files

Latest commit

History

Repository files navigation

NLP Chatbot Application

Project Overview

Table of Contents

Features

Architecture

Data Flow

Technology Stack

Frontend

Backend

Deployment

Implementation Details

NLP Processing Approach

Installation and Setup

Prerequisites

Local Installation

Usage Guide

Starting a Conversation

Example Interactions

Theme Toggle

Code Structure

Pattern Matching System

Response Generation

UI/UX Design

Design Philosophy

Theme Options

Responsive Breakpoints

Deployment

Deployment Process

Deployment Architecture

Future Enhancements

Short-term Improvements

Medium-term Features

Long-term Vision

Lessons Learned

Technical Insights

Project Management

Contributors

License

About

Topics

Resources

License

Uh oh!

Stars

Watchers

Forks

Releases

Packages 0

Uh oh!

Languages

Packages