FAQs Chatbot

A smart chatbot that can understand travel-related questions and give quick and helpful answers. It uses machine learning and language processing to talk like a human and respond correctly.

🖥️ User Interface Overview

The FAQs Chatbot's user interface is designed to be simple, responsive, and interactive, making it easy for users to ask questions and receive answers in real-time.

Key Elements:

• Chat Window: A central area where users can type their queries and view responses from the bot.
• Input Box: Allows users to enter their travel-related questions.
• Send Button: Triggers the chatbot to process the query and display an answer.
• Bot Response Area: Dynamically updates to show the chatbot's replies in a conversational format.
• Responsive Design: Fully optimized for desktop, tablet, and mobile devices using HTML/CSS.

🎬 Project Demonstration

Interaction Flow:

• User types a question in the input field.
• The query is sent to the Flask backend.
• The model predicts the intent using TensorFlow and NLP (SpaCy).
• A relevant answer is selected and displayed in the chat window.

🧩 Architectural Design

The chatbot is built using Flask as the backend, HTML/CSS/JS for the user interface, and tools like TensorFlow and SpaCy for intent recognition and prediction. A custom intents.json file stores predefined categories and responses.

🧰 Tech Stack

Layer	Technologies Used
Frontend	HTML, CSS, JavaScript
Backend	Flask (Python Web Framework)
NLP	SpaCy (for text preprocessing and tokenization)
Machine Learning	TensorFlow (for training and intent classification)
Data Handling	Custom intents.json file (predefined Q&A dataset)
Model Format	.keras file (Keras-trained TensorFlow model)

🔄 How It Works

1. User Input
   The user sends a query via the chatbot interface in the browser.

2. Frontend to Backend
   The input is sent to the Flask backend using JavaScript (typically via a POST request).

3. Preprocessing & Prediction
   The backend uses SpaCy to preprocess the text and passes it to the trained TensorFlow model.

4. Intent Classification
   The model predicts the intent of the user’s query (e.g., flight info, visa, currency).

5. Response Retrieval
   Based on the predicted intent, a matching response is fetched from the intents.json file.

6. Reply to User
   The response is sent back to the frontend and displayed in the chat window.

⚙️ Behind the scenes, the system uses a simple rule-based + ML hybrid architecture to deliver fast and accurate answers to travel-related FAQs.

✨ Features

• 🤖 AI-Powered Chatbot
  Understands and responds to travel-related questions using a trained machine learning model.

• 🧠 Intent Recognition
  Classifies user queries into predefined categories using NLP and TensorFlow.

• 📚 Custom Intent Dataset
  Uses a handcrafted intents.json file containing common travel questions and answers.

• 💬 Real-Time Interaction
  Instant responses via a seamless chat interface built with HTML, CSS, and JavaScript.

• 🛠️ Lightweight Flask Backend
  Fast and easy-to-deploy Python server to handle logic and model inference.

• 🗣️ Natural Language Processing
  Text preprocessing powered by SpaCy for more accurate intent classification.

• 📱 Responsive UI
  Chat interface works smoothly across desktop, tablet, and mobile devices.

• 🧩 Modular Architecture
  Cleanly separated UI, backend, and model logic for easy customization and scaling.

⚙️ Setup Instructions

🛠️ Prerequisites

• Python 3.10 or higher
• pip (Python package manager)
• Git (to clone the repository)

📦 Installation Steps

1. Clone the Repository

   git clone https://github.com/your-username/travel-faqs-chatbot.git
   cd travel-faqs-chatbot

2. Create a Virtual Environment (Optional but Recommended)

   python -m venv venv
   source venv/bin/activate       # On Windows: venv\Scripts\activate

3. Install Required Packages

   pip install -r requirements.txt

🚀 Running the Project

1. Start the Flask Server

   python app.py

2. Access the Chatbot Open your browser and go to: localhost:5000

🗂️ Project Structure

currency-converter-chatbot/
│
├── .gitignore                   # Git ignore rules (should include .venv, .env, __pycache__)
├── app.py                       # Main Flask application handling webhook logic
├── chat.py                      # Chatbot logic and ML integration
├── chatbot_model.keras          # Trained TensorFlow model 
├── data.pickle                  # Contains the preprocessed data (words, labels)
├── intents.json                 # This file has the intents for the Chatbot
├── model.py                     # Model training file
├── requirements.txt             # List of Python dependencies
└── README.md                    # Project documentation

🙏 Acknowledgements

I would like to thank the following tools, libraries, and communities for making this project possible:

• TensorFlow – for building and training the intent classification model
• SpaCy – for natural language preprocessing
• Flask – for creating a lightweight and scalable backend
• NLTK – for supporting text cleaning and processing
• [JavaScript, HTML, CSS] – for designing a responsive and interactive user interface

The open-source community – for continuous support, resources, and inspiration

🙌 Thank You

Thank you for taking the time to explore this project! I hope this spam detection system is helpful for learning, experimentation, and real-world understanding of text classification.

If you have any feedback, suggestions, or improvements, feel free to reach out or raise an issue. Happy coding! 🚀