Converse3.0

AI Chat Bot with Gemini AI using Python and Flask

This project demonstrates how to build an AI-powered chat bot using Gemini AI from Google, integrated with Python and Flask. The bot supports user registration, authentication with JWT tokens, and interaction through a RESTful API. It utilizes PostgreSQL for data storage and can be run locally or in a Docker container.

Technologies Used

• Python: Programming language used for backend development.
• Flask: Lightweight web framework for creating web applications in Python.
• PostgreSQL: Open-source relational database for data storage.
• Docker: Optional for containerization and easy deployment.
• Google Gemini API: Provides generative AI capabilities for natural language processing.

Table of Contents

• GIFs and Screenshots
• Project Structure
• Setup
  • Prerequisites
  • Update Configurations in .env
  • Run using Docker
  • Run using Docker Compose
  • Installation
• API Endpoints
• Credits
• Contribution

GIFs and Screenshots

Landing Page

Registration Page

Login Page

Homepage

Normal AI Chat Page

Project Structure

├── converse_backend
│   ├── client
│   │   └── ai_models
│   │   └── gemini
│   ├── controllers
│   ├── models
│   ├── service
│   └── utils
├── converse_frontend
│   ├── public
│   └── src
│   ├── assets
│   ├── components
│   │   ├── chatResponse
│   │   ├── landingpage
│   │   ├── layout
│   │   ├── shared
│   │   │   └── navBar
│   │   ├── ui
│   │   └── username
│   ├── pages
│   │   ├── auth
│   │   │   ├── register
│   │   │   │   └── components
│   │   │   └── signin
│   │   │   └── components
│   │   ├── converseChat
│   │   ├── converseChatDb
│   │   │   └── components
│   │   ├── converseChatDoc
│   │   │   └── components
│   │   ├── dashboard
│   │   │   └── components
│   │   └── landing-page
│   │   └── components
│   ├── providers
│   ├── routes
│   │   └── hooks
│   └── types
└── demo

Setup

Prerequisites

• Python 3.10+
• PostgreSQL
• Docker and Docker Compose (Optional)

Update Configurations in .env

APP_SECRET_KEY=app_secret_here
JWT_SECRET_KEY=jwt_secret_here

CORS_ORIGINS=http://localhost:5173,http://20.44.62.11:5173

DATABASE_URI=postgresql://postgres:password@localhost:5432/postgres

HOST=127.0.0.1
PORT=5000

GOOGLE_API_KEY=AIzaSyBqVVcd85PoRkGmojC4vTRBfwN4X-fnic9
GEMINI_MODEL_NAME=gemini-pro

To obtain API key from Google

• Visit Google AI Studio, log in, and create an API key.
• Update .env file with the API key.

Run using Docker

Backend

Go to converse_backend directory

docker build -t converse-backend-docker-image .
docker run -d -p 8000:8000 --name converse-backend converse-backend-docker-image

Frontend

Go to converse_frontend directory

docker build -t convers-frontend-docker-image -f Dockerfile.dev .
docker run -d -p 5173:5173 --name converse-frontend convers-frontend-docker-image

Run using Docker Compose

For Development

docker compose --profile dev up

For Production

docker compose --profile prod up

Note: Add the configurations in .prod.env for running the backend

Note: For production, use production-grade servers like nginx to serve the application

Installation

Clone the repository

git clone <repository_url>
cd <project_folder>

Setup PostgreSQL

• Install PostgreSQL and create a database.
• Update .env file with PostgreSQL database URI.

Set up Python Virtual Environment

python -m venv venv

In Linux

source venv/bin/activate

In Windows

venv\Scripts\activate

Install Python Dependencies

pip install -r requirements.txt

Run the application

python main.py

The development server will be hosted on http://localhost:8000 by default.

API Endpoints

Endpoint	Method	Description
/register	POST	Register a new user
/login	POST	Authenticate and obtain JWT token
/normal_chat_with_ai	POST	Chat with the AI using Gemini model
/normal_chat_history_list	GET	Retrieve list of chat history
/normal_chat_history?chat_id=<chat_id>	GET	Retrieve chat history by ID
/normal_chat_history_list?chat_id=<chat_id>	DELETE	Delete chat history by ID
/normal_chat_history_list	DELETE	Delete all the chats of that user

Detailed Endpoint Descriptions

POST /register

Description: Register a new user.

Request Body:

{
  "username": "string",
  "password": "string"
}

Response Body:

201 CREATED

{
  "message": "User has been successfully registered"
}

POST /login

Description: Authenticate and obtain JWT token.

Request Body:

{
  "username": "string",
  "password": "string"
}

Response Body:

200 OK

{
  "access_token": "jwt_token_here"
}

POST /normal_chat_with_ai

Description: Chat with the AI using Gemini model.

Request Body:

{
  "message": "string"
}

Auth Headers:

Authorization: Bearer your_jwt_token_here

Response Body:

200 OK

{
  "ai_response": "response_here",
  "chat_id": "chat_id_here"
}

GET /normal_chat_history_list

Description: Retrieve list of chat history.

Auth Headers:

Authorization: Bearer your_jwt_token_here

Response Body:

200 OK

{
  "data": [
    {
      "chat_id": "chat_id_here",
      "chat_name": "chat_name_here"
    }
  ],
  "message": "Chat history list has been successfully retrieved"
}

GET /normal_chat_history?chat_id=<chat_id>

Description: Retrieve chat history by ID.

Auth Headers:

Authorization: Bearer your_jwt_token_here

Response Body:

200 OK

{
  "data": {
    "chat_history": [
      {
        "role": "user",
        "text": "promp1_here"
      },
      {
        "role": "model",
        "text": "response1_here"
      },
      {
        "role": "user",
        "text": "prompt2_here"
      },
      {
        "role": "model",
        "text": "response2_here"
      }
    ],
    "chat_id": "34"
  },
  "message": "Chat history has been successfully retrieved"
}

DELETE /normal_chat_history_list?chat_id=<chat_id>

Description: Delete chat history by ID.

Auth Headers:

Authorization: Bearer your_jwt_token_here

Response Body:

204 NO CONTENT

DELETE /normal_chat_history_list

Description: Delete all the chats.

Auth Headers:

Authorization: Bearer your_jwt_token_here

Response Body:

204 NO CONTENT

Credits

• This project uses Google Gemini AI for generating responses in the chat bot.
• Built with Python, Flask, SQLAlchemy, and Docker.

Contribution

Feel free to open issues or pull requests if you find any bugs or have improvements.