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ChatGem is a AI chatbot mobile application powered by Google's Gemini LLM models

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AmarMe/ChatGem_app

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ChatGem - AI chatBot Mobile App

This repository contains the source code and necessary resources for a chat bot mobile application developed using Flutter for the frontend, Node.js with Express for the backend server, MongoDB as the database, and integration with Google Gemini models API to generate response for our queries, tasks.

Backend Server (Node.js with Express): The backend server for the chat bot functionality resides in a separate repository. Find the server code at Backend Server Repository.

Overview

The chat bot mobile app is designed to provide a seamless and interactive messaging experience. It uses two models of Google's Gemini API. One is Gemini-pro which accepts text as input and text as output and used for multi-turn chatting feature and another one is Gemini-vision-pro used to get details Or insights of the image you provided. It accepts Image with text as input and text as output.

Deployment

To deploy this project run

  1. Get API Key at here

  2. Clone backend repo

 git clone https://github.com/AmarMe/ChatGem-Node_server.git
  1. create a .env file and save your apikey and create DB in mongodb and save mongoDB URI
  Geminiapikey= "paste your API key here" 
  Mongo_URI   = "paste your mongoDB uri here"
  1. install npm packages
  npm install 
  1. Run the server
  node index.js  
  1. Clone frontend repo and run the project in a code editor
 git clone https://github.com/AmarMe/ChatGem_app.git

Demo

ChatGem.Demo.Vid.mp4

Screenshots

splash screen Homepage multiturn chat Imageand text model_1 Imageand text model_2

Tech Stack

Client: Flutter

Utilizes Flutter framework to create a visually appealing and responsive mobile app interface.

Server: NodeJS, ExpressJS

Employs Node.js along with the Express framework to manage server-side functionalities and handle incoming requests from the mobile app.

Database: MongoDB

To store the images which sent from the Image with text feature in the mobile app and stored as a Buffer(base64) datatype

API Integration: Google's Gemini API

This project is powered by two Gemini LLM models. Gemini-pro and Gemini-vision-pro

Acknowledgements