🤖 AI Chatbot — Flask + OpenAI + SQLite

A full-stack conversational AI chatbot with persistent chat history, built using Flask, the OpenAI GPT API, and SQLite. Features a dark-themed chat UI with a session history sidebar and one-click Railway deployment.

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🚀 Features

• Persistent chat history — all conversations saved to SQLite, survive server restarts
• Session sidebar — browse, load, and delete past conversations
• Multi-turn memory — full context passed to GPT on every message
• OOPs database layer — clean Database class with encapsulated SQLite logic
• REST API backend — Flask endpoints for chat, history, sessions
• Production-ready — Gunicorn + Railway deployment config included

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🗂 Project Structure

flask_chatbot/
├── app.py              # Flask routes + OpenAI integration
├── database.py         # Database class — all SQLite logic (OOPs)
├── requirements.txt    # Dependencies
├── Procfile            # Gunicorn start command for deployment
├── railway.json        # Railway platform config
├── .env.example        # Environment variable template
├── .gitignore
└── templates/
    └── index.html      # Chat UI with sidebar, history, suggestions

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⚙️ Local Setup

# 1. Clone
git clone https://github.com/yourusername/flask-chatbot.git
cd flask-chatbot

# 2. Create virtual environment
python -m venv venv
source venv/bin/activate        # Mac/Linux
venv\Scripts\activate           # Windows

# 3. Install dependencies
pip install -r requirements.txt

# 4. Add your OpenAI API key
cp .env.example .env
# Edit .env → paste your key from platform.openai.com

# 5. Run
python app.py
# Visit http://localhost:5000

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🔌 API Endpoints

Method	Endpoint	Description
GET	/	Serve the chat UI
POST	/chat	Send a message, get AI reply
GET	/sessions	List all saved sessions
GET	/history/<session_id>	Load messages for a session
DELETE	/session/<session_id>	Delete a session

POST /chat — Example

Request:

{ "message": "Explain Python OOPs", "session_id": "sess_abc123" }

Response:

{ "reply": "OOPs stands for Object-Oriented Programming...", "session_id": "sess_abc123" }

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🗄 Database Schema

CREATE TABLE sessions (
    id         TEXT PRIMARY KEY,
    created_at TEXT NOT NULL,
    title      TEXT               -- auto-set to first user message
);

CREATE TABLE messages (
    id         INTEGER PRIMARY KEY AUTOINCREMENT,
    session_id TEXT NOT NULL,
    role       TEXT NOT NULL,     -- 'user' or 'assistant'
    content    TEXT NOT NULL,
    created_at TEXT NOT NULL,
    FOREIGN KEY (session_id) REFERENCES sessions(id) ON DELETE CASCADE
);

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☁️ Deploy to Railway (Free)

1. Push your code to GitHub
2. Go to railway.app → New Project → Deploy from GitHub
3. Select your repo
4. Go to Variables tab → add:

   OPENAI_API_KEY = sk-your-key-here
   SECRET_KEY     = any-random-string
   

5. Railway auto-detects Procfile and deploys. Your live URL appears in ~2 minutes.

Note: Railway's free tier resets the filesystem, so SQLite data clears on redeploy. For persistent storage on Railway, swap SQLite for their free PostgreSQL add-on.

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🛠 Tech Stack

Layer	Technology
Backend	Python 3.11, Flask 3.0
AI	OpenAI GPT-3.5-turbo
Database	SQLite (via Python sqlite3)
Frontend	HTML, CSS, Vanilla JS
Server	Gunicorn
Deployment	Railway

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💡 Concepts Demonstrated

• OOPs — Database class with encapsulation, private methods (_init_db, _get_connection)
• REST API — 5 endpoints with proper HTTP methods and JSON responses
• SQL — CREATE TABLE, INSERT, SELECT, DELETE, FOREIGN KEY, cascade delete
• Flask — routing, sessions, jsonify, request.get_json()
• Git — .gitignore protects .env and chatbot.db
• Deployment — Procfile, environment variables, production server

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📄 License

MIT — free to use and modify.