[FEATURE]: ChatBot implementation #165
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Pull Request: Gemini API Integration (Frontend + Backend)
Description
This PR integrates Gemini AI API into both the backend and frontend of the project.
It allows chatbot-like interactions through Gemini’s service with proper request handling, secure API key usage, and frontend components for a smooth user experience.
Issue no. #118
Changes Implemented
Backend
Created services/geminiAi.js for handling API communication with Gemini.
Added a new route:
POST /api/chatai → Accepts user messages, sends them to Gemini API, and returns AI response.
Implemented error handling to gracefully manage missing/invalid inputs.
Frontend
Added chatbot UI with input + output rendering.
Connected frontend to backend route /api/chatai.
Implemented loading state + error messages for better UX.
Styled using Tailwind + popup hint for chatbot icon.
Setup Instructions
Add a .env file in the root directory with the following variable:
GEMINI_API_KEY=YOUR_GEMINI_KEY...make sure to add the key in GeminiService.ts file in backend
Ensure the API key is correctly referenced in geminiAi.js (backend service).
Without this, the service will crash.
Run backend + frontend together:
Backend
npm run dev
Frontend
npm start
##Video
https://github.com/user-attachments/assets/205875df-f242-428f-ad51-bbeaf2b5b7c9
##backend .env
GEMINI_API_KEY=YOUR_API_KEY
CORS_ORIGIN=http://localhost:5173
PORT=4000
##frontend .env
VITE_API_BASE_URL=http://localhost:3000
##Important Notes regarding this PR
Example .env file structure is provided above—developers need to fill in their own key.
If the GEMINI_API_KEY is missing, backend requests will fail with error.
Riya Chandra || GSSOC Contributor 2025
Thank you !
@Kushika-Agarwal