AI Chatbot

Flask • Gemini API • Scalable AI System

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Overview

A modular AI chatbot built using Flask (Python) and Google Gemini API, designed with scalability, clean backend structure, and real-time interaction in mind.

This project focuses on building systems, not just features.

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Live Demo

Add your deployed link here (Render / Railway / Vercel)

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Tech Stack

Layer	Technology
Backend	Python, Flask
AI Model	Google Gemini API
Frontend	HTML, CSS, JavaScript
Config	python-dotenv
Version Control	Git, GitHub

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Core Features

• Real-time conversational AI
• Gemini API integration
• Chat session management (history, rename, delete)
• Modular service-based backend
• Smooth UI with typing interaction
• Lightweight and extensible architecture

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Project Structure

ai-chatbot-nlp/
├── app.py
├── config.py
├── utils.py
├── intents.json
├── services/
│   └── gemini_service.py
├── static/
│   ├── style.css
│   └── script.js
├── templates/
│   └── index.html
└── README.md

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Setup & Installation

1. Clone Repository

git clone https://github.com/arpitsingh97096-debug/ai-chatbot-nlp.git
cd ai-chatbot-nlp

2. Install Dependencies

pip install flask python-dotenv google-generativeai

3. Environment Variables

Create a .env file in the root directory:

GEMINI_API_KEY=your_api_key_here

4. Run Locally

python app.py

Open: http://127.0.0.1:5000

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Security

• Environment-based API key management
• .env excluded via .gitignore
• No sensitive data exposed in source code

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Deployment

You can deploy this project using:

Platform	Type
Render	Full-stack hosting
Railway	Backend + DB hosting
Vercel	Frontend + API proxy

Basic Deployment Flow

1. Push project to GitHub
2. Connect repo to deployment platform
3. Add environment variables
4. Deploy

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Preview

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GitHub Stats

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Future Improvements

• Authentication system (JWT / OAuth)
• Database integration (MongoDB / PostgreSQL)
• Streaming responses (real-time token output)
• Multi-model AI support
• Docker + CI/CD pipeline

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Author

Arpit Singh
AI • Systems • Backend Development

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