Customer Service Chatbot 🤖

A lightweight, production-ready FAQ chatbot built with Flask, FAISS, and Sentence Transformers, packaged in Docker with a modern web UI.

✨ Features

• 🔍 Semantic search over FAQs using sentence-transformers + FAISS
• 🎨 Modern chat UI with stickers, typing indicator, and timestamps
• 🖥 Flask backend with REST API (/chat, /health)
• 🐳 Dockerized for easy deployment (Gunicorn included)
• 📊 Logging for conversations and errors
• 📱 Responsive design for desktop & mobile
• 🛡️ Input sanitization (prevents HTML/JS injection)

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🛠 Tech Stack

• Backend: Flask, Gunicorn
• ML/NLP: SentenceTransformers (all-MiniLM-L6-v2), FAISS
• Frontend: HTML, CSS (Glassmorphism style), jQuery
• Containerization: Docker, multi-stage builds
• Data: CSV-based FAQ storage

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📂 Project Structure

.
├── app.py                 # Flask app entrypoint
├── utils/
│   └── chatbot_utils.py   # Embeddings + FAISS + response logic
├── templates/
│   └── index.html         # Frontend UI
├── static/
│   ├── style.css          # Chat UI styles
│   ├── chatbot_avatar.webp
│   ├── sticker.webp
│   └── background.jpg
├── data/
│   └── faq_data.csv       # FAQ dataset
├── requirements.txt
├── Dockerfile
├── .gitignore
└── README.md              # ← (this file)

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🚀 Getting Started

1️⃣ Clone the repo

git clone https://github.com/yourusername/customer-service-chatbot.git
cd customer-service-chatbot

2️⃣ Install dependencies

Create a virtual environment and install requirements:

python -m venv venv
source venv/bin/activate  # (Linux/Mac)
venv\Scripts\activate     # (Windows)

pip install -r requirements.txt

3️⃣ Run locally

python app.py

Open http://localhost:5000 in your browser.

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🐳 Docker Deployment

Build image

docker build -t chatbot .

Run container

docker run -p 5000:5000 chatbot

Now visit http://localhost:5000.

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📡 API Endpoints

POST /chat

Send a message to the chatbot:

{
  "message": "How can I reset my password?"
}

Response:

{
  "response": "You can reset your password by clicking on 'Forgot Password' on the login page."
}

GET /health

Health check (includes FAISS index info):

{
  "status": "ok",
  "faq_loaded": true,
  "faiss_index_size": 42
}

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📖 FAQ Data

All FAQs are stored in data/faq_data.csv. It must have two columns:

question,answer
"What are your business hours?","We are open from 9 AM to 5 PM, Monday to Friday."
"How can I reset my password?","Click 'Forgot Password' on the login page."

To update the bot, just edit the CSV and restart the server.

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🔮 Future Enhancements

• Conversation memory
• Admin panel for FAQ management
• Analytics dashboard (track top queries)
• Multilingual support
• Optional fallback to GPT models

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👩‍💻 Author

Made with ❤️ by Sagarika. GitHub: Sagarika311